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 .
DETAILED ACTION
The following NON-FINAL Office Action is in response to application 18/289,897
filed on 11/08/2023. This communication is the first action on the merits.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/08/2023 has been
considered by the examiner.
Drawings
The drawings were received on 11/08/2023. These drawings are acceptable.
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, and 6-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Specifically, representative Claim 1 recites:
A storage battery diagnosis device comprising:
a data point sequence generator which generates, on the basis of a current of a storage battery detected by a current detection device and voltage of the storage battery detected by a voltage detection device, a data point sequence including a Zth-order derivative/integral curve expressed with a Zth-order derivative/integral voltage or a Zth-order derivative/integral capacity where Z represents a real number;
a reference data provider which provides, as a reference, a reference data point sequence of the storage battery or an electrode;
a point set registrator which performs point set registration between the reference data point sequence and the data point sequence generated by the data point sequence generation unit; and
a diagnosis unit circuitry which estimates a parameter indicating a state of degradation of the storage battery or the electrode on the basis of a result from the point set registrator;
a feature point set extractor which extracts a feature point set from the data point sequence and extracts a reference feature point set from the reference data point sequence, wherein
the point set registrator performs point set registration between the feature point set and the reference feature point set and wherein each of the feature point set and the reference feature point set extracted by the feature point set extractor includes at least two feature points among a concave inflection point, a convex inflection point, a local maximum point, a local minimum point, a positive zero crossing point, and a negative zero crossing point.
Under Step 1 of the analysis, claim 1 belongs to a statutory category, namely it is a storage battery diagnosis device claim.
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 a Mathematical Concept. This can be seen in the claim limitations of “, a data point sequence including a Zth-order derivative/integral curve expressed with a Zth-order derivative/integral voltage or a Zth-order derivative/integral capacity where Z represents a real number”, a point set registrator which performs point set registration between the reference data point sequence and the data point sequence generated by the data point sequence generation unit”, “estimates a parameter indicating a state of degradation of the storage battery or the electrode on the basis of a result from the point set registrator”, “a feature point set extractor which extracts a feature point set from the data point sequence and extracts a reference feature point set from the reference data point sequence”, and “the point set registrator performs point set registration between the feature point set and the reference feature point set and wherein each of the feature point set and the reference feature point set extracted by the feature point set extractor includes at least two feature points among a concave inflection point, a convex inflection point, a local maximum point, a local minimum point, a positive zero crossing point, and a negative zero crossing point” which is the judicial exception of a mental process because these limitations are merely data observations, evaluations, and/or judgements in order to identify feature points (e.g. inflection points, maximum, minimum, and zero crossings), compare data sets, and determine a degradation parameter 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. see Spec. [0030]-[0044] describing the use of a mathematical operations including integration of current over time, differentiation of voltage with respect to capacity, and numerical approximation techniques, as well as known signal processing methods such as filtering and smoothing, in order to generate and analyze data point sequences for estimating a degradation parameter of the storage battery.
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 storage battery diagnosis device recites additional elements including “A storage battery diagnosis device comprising: a data point sequence generator which generates, on the basis of a current of a storage battery detected by a current detection device and voltage of the storage battery detected by a voltage detection device”, “a reference data provider which provides, as a reference, a reference data point sequence of the storage battery or an electrode”, and “a diagnosis unit circuitry” however these elements are found to be data gathering and output steps, which are recited at a high level of generality, and thus merely amount to “insignificant extra-solution” activity(ies). See MPEP 2106.05(g) “Insignificant Extra-Solution Activity,”. Furthermore, the claim recites the performance of mathematical calculations and data analysis operations (e.g., generating derivative/integral curves, extracting feature points, performing point set registration, and estimating a degradation parameter) by the storage battery diagnosis device however this is found to be equivalent to adding the words “apply it” and mere instructions to apply a judicial exception on a general purpose computer does not integrate the abstract idea into a practical application. See MPEP 2106.05(f).
The generic data gathering, processing, and output steps, are recited at such a high level of generality (e.g. using “detection device” and “diagnosis unit circuitry”) 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, the claim merely determines a degradation parameter based on mathematical analysis of data, and does not recite any action taken based on the result (e.g., controlling the storage battery, adjusting a charging or discharging operations, or modifying a battery management parameter such as a charging rate or cutoff threshold).
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 1). Such insignificant extra-solution activity, e.g. data gathering and output, 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, amount to significantly more than the abstract idea.
With regards to the dependent claims, claims 6-18, merely further expand upon the algorithm/abstract idea and do not set forth further additional elements that integrate the recited abstract idea into a practical application or amount to significantly more. Therefore, these claims are found ineligible for the reasons described for claim 1. Specifically:
With respect to dependent claims 6-8 and 12 specifically, the claims further recite limitations directed to mathematical variations of the data analysis, including generating derivative and/or integral curves of different orders, aligning feature points across derivative curves of multiple orders, and generating derivative and integral representations of battery data. These limitations merely expand upon the mathematical concepts underlying the abstract idea by further refining how the data is analyzed and processed. The recited steps constitute mathematical calculations and data manipulations and do not reflect an improvement to computer functionality or another technological field. Accordingly, these limitations fail to integrate the abstract idea into a practical application. See MPEP 2106.05 (g)(h). With respect to dependent claims 9-10 specifically, the claims further recite limitations directed to additional data processing techniques, including smoothing the reference data point sequence and adjusting feature extraction parameters. These limitations merely relate to refining how the data is prepared and processed for analysis and constitute insignificant extra solution activity and/or further mathematical manipulation of data. Such limitations do not impose meaningful limits on the abstract idea and do not reflect an improvement to a technological field. Accordingly, these limitations fail to integrate the abstract idea into a practical application. See MPEP 2106.05(g).
With respect to dependent claims 11-13 specifically, the claims further recite limitations directed to evaluating and interpreting the results of the data analysis, including calculating transformation parameters, estimating capacity retention or deviation, and modeling electrode characteristics. These limitations merely represent further data evaluation and analysis of the result of the abstract idea. The recited steps constitute mathematical calculations and post solution activity an do not provide a specific technological improvement. Accordingly, these limitations fail to integrate the abstract idea into a practical application or amount to significantly more. See MPEP 2106.05(f)(h).
With respect to dependent claims 14-18 specifically, the claims further recite limitations directed to implementing specific mathematical registration techniques, including iterative closest point (ICP) and coherent point drift (CPD), as well as generic system components such as a display device and controller. The use of known mathematical algorithms merely amounts to further refinement of the abstract idea, and the recitations of generic components constitutes implementation of the abstract idea on a general purpose computer. For example, displaying results constitute output of information, and reciting a controller without specifying how control is performed amounts to instructions to apply the abstract idea. Accordingly, these limitations do not integrate the abstract idea into a practical application or amount to significantly more. See MPEP 2106.05(f)(g).
Accordingly, for the reasons above and those discussed in relation to independent claim 1, the dependent claims are insufficient to integrate the claimed abstract ideas into a practical application or significant more.
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, 6, 9-11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over US 20210293890 A1, Gorrachategui et al. (hereinafter Gorrachategui) in view of US 20160061908 A1, Torai et al. (hereinafter Torai).
Regarding Claim 1, Gorrachategui discloses a storage battery diagnosis (Gorrachategui, [0001] This invention generally relates to a battery diagnostic system, and more specifically to a battery diagnostic system for estimating the remaining useful life (RUL) of a battery) device comprising:
a data point sequence generator which generates, on the basis of
a current of a storage battery detected by a current detection device and a voltage of the storage battery detected by a voltage detection device (Gorrachategui, [0045] The battery 114 is connected to the battery diagnostic system 102 via an input interface (not shown). The memory 104 is configured to store a neural network 106 trained to estimate an RUL of the battery 114. Further, the memory 104 may be configured to store a predetermined set of features indicative of a battery cycle of the battery 114 and a capacity of the battery 114. The predetermined set of features may include a current, a voltage, a capacity or the like. The battery cycle herein is formed by values of one or combination of voltages and currents measured for the battery 114 during one or combination of a charging cycle and a discharging cycle), a data point sequence including a Zth-order derivative/integral curve expressed with a Zth-order derivative/integral voltage or a Zth-order derivative/integral capacity where Z represents a real number (Gorrachategui, [0066] FIG. 6 shows a graphical representation 600 depicting charge derivative with respect to voltage and variations of the voltage as the battery 114 ages, according to some embodiments of the present disclosure. In some embodiments, determination of a charge derivative with respect to voltage and the variations of the voltage is performed during test cycles of the battery 114 by the charging system 110. The charging system 110 may determine the variations of the voltage based on an incremental capacity (IC) analysis. In the IC analysis, properties of the battery 114 (e.g., properties related to chemistry of the battery 114, such as oxidation potential, reduction potential, or the like) are tracked. During the IC analysis, a disturbance appears as a small peak, such as peak 602 in a low-voltage region, as shown in FIG. 6. The value of the peak is the capacitance peak feature 326 for that cycle, and the voltage at which the peak is located is the voltage at capacitance peak feature 328 for that cycle);
a diagnosis circuitry which estimates a parameter indicating a state of degradation of the storage battery or the electrode on the basis of a result from the point set registrator (Gorrachategui, [0053] At operation 208, the processor 108 performs a feature extraction operation to extract a predetermined set of features, such as features of capacity, internal resistance, TIEDVD, the capacity fade, the capacitance peak and a voltage at capacitance peak of the battery 114. The processor 108 submits the extracted set of features to the neural network 106);
a feature point set extractor which extracts a feature point set from the data point sequence (Gorrachategui, [0012] Accordingly, one embodiment discloses a battery diagnostic system that includes a memory configured to store a neural network trained to estimate a remaining useful life (RUL) of a test battery from a predetermined set of features indicative of a battery cycle of the test battery and a capacity of the test battery; a charging system configured to charge and discharge the test battery to provide a set of measurements of a battery cycle and the capacity of the test battery) and extracts a reference feature point set from the reference data point sequence (Gorrachategui, [0053] At operation 208, the processor 108 performs a feature extraction operation to extract a predetermined set of features, such as features of capacity, internal resistance, TIEDVD, the capacity fade, the capacitance peak and a voltage at capacitance peak of the battery 114. The processor 108 submits the extracted set of features to the neural network 106), wherein,
the feature point set extractor includes at least two feature points among a
concave inflection point, a convex inflection point, a local maximum point, a local minimum point, a positive zero crossing point, and a negative zero crossing point (Gorrachategui, [0051] At operation 206, the charging system 110 acquires data comprising a set of measurements. For the δ test cycles, a set of measurements (e.g., δ measurements) are obtained. For instance, when test cycles δ=10, features from the voltage and charge waveforms of these 10 test cycles are extracted. The features include a capacity, an internal resistance (IR), a time interval of equal discharging voltage differences (TIEDVD), a capacity fade (ΔC), a capacitance peak (C.sub.pk) and a voltage at capacitance peak (V.sub.pk) of the battery 114. Thus, the set of measurements 204 include 10 measurements from each of the features except for the ΔC. A measurement for the capacity fade feature is obtained by comparing capacity between the 10.sup.th test cycle and Pt test cycle. Further, difference in the capacities is determined based on the comparison).
each of the feature point set and the reference feature point set extracted
by the feature point set extractor (Gorrachategui, [0055] FIG. 3A shows a graphical representation 300 of the variability of each feature along a lifespan of the battery 114, as a measurement of amount of information each feature provides, according to some embodiments of the present disclosure);
Gorrachategui does not disclose a reference data provider which provides, as a reference, a reference data point sequence of the storage battery or an electrode;
a point set registrator which performs point set registration between the reference data point sequence and the data point sequence generated by the data point sequence generation unit;
the point set registrator performs point set registration between the feature point set and the reference feature point set;
However, Torai teaches a reference data provider which provides, as a reference, a reference data point sequence of the storage battery or an electrode (Torai, [0043] There is computed a difference between the partial derivative characteristic curve and a reference derivative curve indicating a reference characteristic of the capacity-to-voltage derivative. The partial derivative characteristic curve is fitted to the reference derivative curve by reducing the difference, to estimate an SOC. There is estimated a maximum value of capacity, from the partial derivative characteristic curve and the reference derivative curve. The reference derivative curve is given by a complex of first and second characteristic derivative curves);
a point set registrator which performs point set registration between the reference data point sequence and the data point sequence generated by the data point sequence generation unit (Torai, [0077] The adjusted separated waveform derivative curves derived from the positive electrode and the negative electrode are then synthesized, and processing to generate a new characteristic derivative curve and to compare that characteristic derivative curve with the measured characteristic derivative curve is repeatedly performed, until the absolute value of the difference (error) between the characteristic derivative curve and the measured characteristic derivative curve is minimized, thereby optimizing the shape of the characteristic derivative curve so that it approximates the measured characteristic derivative curve);
the point set registrator performs point set registration between the feature point set and the reference feature point set (Torai, [0231] Then, the maximum capacity error computer 162 computes the evaluation value A between the first reference derivative curve and the first partial derivative characteristic curve.
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Gorrachategui and Torai teachings because Torai teaches iterative point set registration through alignment and error minimization, which is directly applicable to aligning measured battery data with reference data. Applying Torai’s technique to Gorrachetegui is nothing more than using a known data alignment method to improve the accuracy of an existing system. One of ordinary skill in the art would have been motivated to make this combination to reduce error, improve curve fitting, obtain more reliable parameter estimation, which are standard and expected goals in data-driven battery diagnostics.
Regarding Claim 6, Gorrachategui in view of Torai discloses the storage battery diagnosis device according to claim 1, wherein
in a case of generating a data point sequence including a Z_Dth-order derivative curve, derivative curves of two or more mutually different order are included (Torai, [0038] the secondary battery capacity measurement system may further include an averaging processor that performs piecewise or moving averaging processing in a prescribed time range with respect to measured values obtained as a voltage V and current I of a charged and discharged battery in a time sequence, so as to take the determined average value as time sequential data of the measured value).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Gorrachategui and Torai teachings because Torai teaches performing point set registration between reference data and generated data using feature point sets, which corresponds to aligning data point sequences and feature point sets, and Gorrachategui discloses generating data point sequences including derivative curves but does not disclose such alignment. A person of ordinary skill in the art would have been motivated to make this combination in order to improve the accuracy and reliability of aligning derivative based data point sequences and corresponding feature point sets for subsequent analysis, since improved alignment leads to more accurate interpretation of battery characteristics.
Regarding Claim 9, Gorrachategui in view of Torai teaches the storage battery diagnosis device according to claim 1, wherein
The reference data provision unit smooths the reference data point sequence (Torai, [0038] the secondary battery capacity measurement system may further include an averaging processor that performs piecewise or moving averaging processing in a prescribed time range with respect to measured values obtained as a voltage V and current I of a charged and discharged battery in a time sequence, so as to take the determined average value as time sequential data of the measured value.).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Gorrachategui and Torai teachings because Torai teaches smoothing a data point sequence (e.g., via averaging/filtering), which directly corresponds to smoothing the reference data point sequence as recited. Gorrachetegui already relies on data point sequences for battery analysis but fails to address noise or irregularities in those sequences. It would have been an obvious and routine improvement to apply Torai’s smoothing to Gorrachetegui to reduce noise, stabilize the data, and improve accuracy of subsequent analysis. A person of ordinary skill in the art would have been motivated to make this combination to enhance data quality and reliability, since cleaner input data directly leads to more accurate battery characterization and estimation results.
Regarding Claim 10, Gorrachategui in view of Torai teaches the storage battery diagnosis device according to claim 1, wherein
the reference data provider smooths the reference data point sequence and adjusts a strength of smoothing such that the number of feature points in a reference feature point set extracted from the smoothed reference data point sequence by the feature point set extractor is smaller than the number of feature points in the reference feature point set extracted from the reference data point sequence by the feature point set extractor (Torai, [0039] the SOC computer has an SOC error computer that computes the error between the reference derivative curve and the partial derivative characteristic curve, the SOC error computer, using a reference derivative curve of the second relationship of correspondence, which has been corrected by the partial derivative characteristic curve of the second relationship of correspondence in the reference derivative curve reconstructor, computes the difference between the reference derivative curve and the partial derivative characteristic curve to optimize as the variable the capacity that has been integrated at the starting point of the partial derivative characteristic curve, and the SOC computer re-estimates the SOC by the optimized capacity [0041] if there is a plurality of peaks in the partial derivative characteristic curve of the first relationship of correspondence, when the difference is computed between the reference derivative curve and the partial derivative characteristic curve of the first relationship of correspondence, the maximum capacity error computer uses the distances between the peak spacing as one of the parameters in computing errors, and the maximum capacity computer, by integrating the reference derivative curve of the first relationship of correspondence that has been corrected by the peak values of the partial derivative characteristic curve of the first relationship of correspondence within the range of the prescribed voltage value V, computes the maximum capacity of the second battery [0087] The averaging processor 12 performs averaging processing on each of the current values I and voltage values V actually measured and converted to digital data and outputs the result. For example, in order to reduce the number of data points, averaging processing is performed on digital data for 10 sampling periods, so that the amount of data is 1/10 with respect to the sampling period. This averaging processing uses averaging such as piecewise averaging or a moving average. The averaging processor 12 integrates the current value I over a prescribed time period and computes the capacity Q as the variation amount in the capacity over that prescribed period of time).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Gorrachategui and Torai teachings because Torai teaches applying denoising/filtering to data points, which directly corresponds to smoothing and controlling the strength of smoothing to reduce the number of extracted points. Gorrachetegui already relies on data points sequences and feature extraction but fails to address controlling noise or optimizing the number of extracted feature points. A person of ordinary skill in the art would have been motivated to make this combination to improve efficiency, stability, and reliability of downstream analysis, since cleaner and reduced data directly leads to better performance.
Regarding Claim 11, Gorrachategui discloses the storage battery diagnosis device according to claim 1, wherein
the diagnosis circuitry calculates, on the basis of the transformation parameters, electrode parameters including a full capacity retention rate of the electrode and including a capacity deviation of the electrode or a capacity error of the storage battery (Gorrachategui, [0012] Accordingly, one embodiment discloses a battery diagnostic system that includes a memory configured to store a neural network trained to estimate a remaining useful life (RUL) of a test battery from a predetermined set of features indicative of a battery cycle of the test battery and a capacity of the test battery; a charging system configured to charge and discharge the test battery to provide a set of measurements of a battery cycle and the capacity of the test battery; a processor configured to [0013] extract the predetermined set of features from the set of measurements to submit the extracted set of features to the neural network, wherein the extracted set of features include a capacity, an internal resistance, a time interval of equal discharging voltage difference (TIEDVD), a capacity fade, a capacitance peak, and a voltage at capacitance peak of the test battery; and an output interface configured to output the estimated RUL of the test battery);
Gorrachategui does not disclose the point set registrator calculates transformation parameters including an enlargement/reduction parameter and a translation parameter;
However, Torai teaches the point set registrator calculates transformation parameters including an enlargement/reduction parameter and a translation parameter (Torai, [0201] The maximum capacity optimization processor 163 subjects the second partial derivative characteristic curve to parallel translation by a pre-established change value of ΔQ of the capacity Q with respect to the horizontal axis, in the direction that makes the above-described evaluation value B smaller. As a result, the value of the capacity Q corresponding to each derivative values of the regions of the second partial derivative characteristic curve are changed [0173] Next, the SOC optimization processor 153 subjects the second partial derivative characteristic curve to parallel translation with respect to the horizontal axis as shown at FIG. 9A, so as to minimize the absolute value of the difference of the derivative value dV/dQ determined by the SOC error computer 152);
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Gorrachategui and Torai teachings because Torai clearly teaches that a point set registration process calculations transformation parameters, including translation, which directly corresponds to the claimed transformation parameters. Gorrachetegui relies on data point sequences and feature extraction but does not address calculating or applying such transformation parameters through point set registration. A person of ordinary skill in the art would have been motivated to make this combination to improve alignment accuracy, consistency, and overall reliability of the resulting analysis, since properly transformed data leads to more precise downstream evaluation.
Regarding Claim 18, Gorrachategui in view of Torai discloses a storage battery system comprising:
the storage battery diagnosis device according to claim 1;
a display device which displays an output from the storage battery diagnosis device (Gorrachategui, [0044] FIG. 1 shows a principle block diagram 100 of a battery diagnostic system 102, according to some embodiments of the present disclosure. The battery diagnostic system 102 includes a memory 104, a processor 108, a charging system 110, and an output interface 112); and
a controller which controls the storage battery on the basis of the output from the storage battery diagnosis device (Gorrachategui, [0012] Accordingly, one embodiment discloses a battery diagnostic system that includes a memory configured to store a neural network trained to estimate a remaining useful life (RUL) of a test battery from a predetermined set of features indicative of a battery cycle of the test battery and a capacity of the test battery; a charging system configured to charge and discharge the test battery to provide a set of measurements of a battery cycle and the capacity of the test battery; a processor configured to [0013] extract the predetermined set of features from the set of measurements to submit the extracted set of features to the neural network, wherein the extracted set of features include a capacity, an internal resistance, a time interval of equal discharging voltage difference (TIEDVD), a capacity fade, a capacitance peak, and a voltage at capacitance peak of the test battery; and an output interface configured to output the estimated RUL of the test battery).
Claims 7-8, and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over US 20210293890 A1, Gorrachategui et al. (hereinafter Gorrachategui) in view of US 20160061908 A1, Torai et al. (hereinafter Torai), in further view of US 20210359347 A1, Stefanopoulou et al. (hereinafter Stefanopoulou).
Regarding Claim 7, Gorrachategui in view of Torai discloses the storage battery diagnosis device according to claim 1, wherein
in a case of generating a data point sequence including a Z_Dth-order derivative curve, the feature point sets or the reference feature point sets extracted by the feature point set extractor are such that positions of the feature point sets or the
reference feature point sets are caused to coincide with each other between derivative curves of two or more mutually different orders (Gorrachategui, [0014] Another embodiment discloses a method for estimating a remaining useful life (RUL) of a test battery, the method comprising providing a set of measurements of battery cycle and a capacity of the test battery; extracting a predetermined set of features from the set of measurements to submit the extracted set of features to a neural network, wherein the extracted set of features include a capacity, an internal resistance, a time interval of equal discharging voltage difference (TIEDVD), a capacity fade, a capacitance peak, and a voltage at capacitance peak of the test battery and outputting the estimated RUL of the test battery)
Gorrachategui in view of Torai does not disclose feature point sets are caused to coincide with each other between derivative curves of two or more mutually different orders.
However, Stefanopoulou teaches feature point sets are caused to coincide with each other between derivative curves of two or more mutually different orders (Stefanopoulou, [0063] the validity of the parameter estimation is checked by the alignment of the peaks in the dV/dQ curve, since the alignment of the peak locations implies correct estimation of the utilization of individual electrodes in the cell).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Gorrachategui in view of Torai and Stefanopoulou teachings because Stefanopoulou teaches aligning features points across derivative curves of different orders, which directly corresponds to causing feature point sets to coincide across multiple derivative curves. Gorrachetegui, even when combined with Torai, relies on extracted features and data point sequences but fails to disclose or suggest enforcing alignment across different derivative orders. It would have been an obvious and routine modification to apply Stefanoupoulou’s alignment approach to ensure correspondence between feature point sets derived from different curves. A person of ordinary skill in the art would have been motivated to make this combination to improve consistency and accuracy of feature alignment, which directly improves parameter estimation and overall reliability of battery diagnostic analysis.
Regarding Claim 8, Gorrachategui in view of Torai discloses the storage battery diagnosis device according to claim 1, wherein
in a case of generating a data point sequence including a Z_Dth-order derivative curve, the feature point sets or the reference feature point sets extracted by the feature point set extractor are such that positions of the feature point sets or the reference feature point sets (Gorrachategui, [0013] extract the predetermined set of features from the set of measurements to submit the extracted set of features to the neural network, wherein the extracted set of features include a capacity, an internal resistance, a time interval of equal discharging voltage difference (TIEDVD), a capacity fade, a capacitance peak, and a voltage at capacitance peak of the test battery; and an output interface configured to output the estimated RUL of the test battery).
Gorrachategui in view of Torai does not disclose feature point sets are caused to coincide with each other between two or more different Z_Dth-order derivative curves on the basis of a sigmoidal potential change, due to a phase change, in an electrode potential curve of the storage battery.
However, Stefanopoulou teaches feature point sets are caused to coincide with each other between two or more different Z_Dth-order derivative curves on the basis of a sigmoidal potential change, due to a phase change, in an electrode potential curve of the storage battery (Stefanopoulou, [0060] Electrode materials undergo several phase transitions during lithium intercalation, and their potential shows a staircase curve where the plateaus correspond to the coexistence of two phases, and the step between the plateaus represents the single-phase stage when the phase transition is completed (Ref. 1.15). This rapid voltage changes at the steps appear as peaks in the differential voltage curve (dV/dQ vs. Q). Hence, a dV/dQ curve allows the features in the OCV curve to be seen more clearly).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Gorrachategui in view of Torai and Stefanopoulou teachings because Stefanopoulou clearly teaches that feature point sets (e.g., peaks) coincide across derivative curves due to sigmoidal potential changes associated with phase transition in electrode materials, while Gorrachetegui in view of Torai fails to provide any basis for aligning feature points across different derivative orders. A person of ordinary skill in the art would have been motivated to make this combination to improve alignment accuracy and ensure the results reflect actual underlying battery behavior, thereby increasing the reliability and robustness of the diagnostic output.
Regarding Claim 12, Gorrachategui discloses the storage battery diagnosis device according to claim 1, wherein
electrode parameters including a full charge capacity retention rate of the electrode and including a capacity deviation of the electrode or a capacity error of the storage battery (Gorrachategui, [0012] Accordingly, one embodiment discloses a battery diagnostic system that includes a memory configured to store a neural network trained to estimate a remaining useful life (RUL) of a test battery from a predetermined set of features indicative of a battery cycle of the test battery and a capacity of the test battery; a charging system configured to charge and discharge the test battery to provide a set of measurements of a battery cycle and the capacity of the test battery, [0013] extract the predetermined set of features from the set of measurements to submit the extracted set of features to the neural network, wherein the extracted set of features include a capacity, an internal resistance, a time interval of equal discharging voltage difference (TIEDVD), a capacity fade, a capacitance peak, and a voltage at capacitance peak of the test battery; and an output interface configured to output the estimated RUL of the test battery).
Gorrachategui does not disclose
point set registrator
performs point set registration between data point sequences which are the Z--_Dth-order derivative curve and the reference Z_Dth-order derivative curve, and
performs point set registration between data point sequences which are the Z_Ith-order integral curve and the reference Z_Ith-order integral curve,
where Z_D and Z_I each represent a real number larger than O: and
the point set registrator calculates transformation parameters including an enlargement/reduction parameter and a translation parameter,
the diagnosis circuitry calculates, on the basis of the transformation parameters.
However, Torai teaches point set registrator
performs point set registration between data point sequences which are the Z--_Dth-order derivative curve and the reference Z_Dth-order derivative curve (Torai, [0231] Then, the maximum capacity error computer 162 computes the evaluation value A between the first reference derivative curve and the first partial derivative characteristic curve [0232] The maximum capacity error computer 162 computes the evaluation value B of the derivative value dV/dQ between the second reference derivative curve and the second partial derivative characteristic curve from each of the capacity values over the range of capacity Q (from the capacity Q.sub.s to the capacity Q.sub.e) of the second partial derivative characteristic curve.), and
performs point set registration between data point sequences which are the Z_Ith-order integral curve and the reference Z_Ith-order integral curve, where Z_D and Z_I each represent a real number larger than O (Torai, [0233] The maximum capacity optimization processor 163 causes the reference derivative curve reconstructor 164 to repeatedly change the parameters of separated waveform curves derived from the positive electrode and the negative electrode of the first reference derivative curve, and to generate a new first reference derivative curve until the evaluation value B, in which the absolute values of the differences of the derivative value dV/dQ in the second reference derivative curve of the capacity Q range of the second partial derivative characteristic curve is minimized [0234] In this case, each of the maximum capacity error computer 162 and the maximum capacity optimization processor 163 repeats optimization processing to compute the derivative value dV/dQ difference evaluation value and peak position difference evaluation value B, and to cause the reference derivative curve reconstructor 164 to generate a new first reference derivative curve until the evaluation value B is minimized [0235] That is, in the optimization processing, the reference derivative curve reconstructor 164 repeatedly performs processing to change the parameters of the functions constituting the separated waveform curves derived from the positive electrode and the negative electrode in the first reference derivative curve and to generate a new first reference derivative curve, so that the evaluation value of the derivative value dV/dQ in the second partial derivative characteristic curve and the second reference derivative curve in the capacity Q range of the second partial derivative characteristic curve is minimized): and
the point set registrator calculates transformation parameters including an enlargement/reduction parameter and a translation parameter (Torai, [0201] The maximum capacity optimization processor 163 subjects the second partial derivative characteristic curve to parallel translation by a pre-established change value of ΔQ of the capacity Q with respect to the horizontal axis, in the direction that makes the above-described evaluation value B smaller. As a result, the value of the capacity Q corresponding to each derivative values of the regions of the second partial derivative characteristic curve are changed [0218] As shown in FIG. 11A, the shape of the second partial derivative characteristic curve is optimized so as to minimize the evaluation value B, that is, so that it resembles the shape of the second reference derivative curve. The optimization performs fitting processing repeatedly to adjust the parameters of the functions constituting the separated waveform curves derived from the positive electrode and the negative electrode, to change the peak height and peak positions of the separated waveform curves, to synthesize the separated waveform curves derived from the positive electrode and the negative electrode, to generate the first reference derivative curve, to change to the second reference derivative curve, and to minimize the evaluation value B with respect to the second partial derivative characteristic curve),
the diagnosis circuitry calculates, on the basis of the transformation parameters (Torai, [0236] The maximum capacity optimization processor 163 takes the first reference derivative curve, for which the evaluation value B by the difference between the derivative value dV/dQ between the second partial derivative characteristic curve and the second reference derivative curve is minimized, to be the reconstructed first reference derivative curve).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Gorrachategui and Torai teachings because Torai teaches performing point set registration using transformation parametersing including scaling and translation, which complements Gorrachategui’s feature extraction and battery diagnostic framework. A person of ordinary skill in the art would have been motivated to make this combination in order to improve the alignment of data point sequences and corresponding feature point sets, thereby improving the accuracy, consistency, and reliability of the resulting battery parameter determination.
Gorrachategui in view of Torai does not disclose the Zth-order derivative/integral curve generated by the data point sequence generator includes a Z_Dth-order derivative curve and a Z_Ith-order integral curve,
the reference data point sequence from the reference data provider includes at least parts of a reference Z_Dth-order derivative curve and a reference Z_Ith-order
integral curve, and
in a case of generating a data point sequence including the Z_Dth-order
derivative curve, the Z_Dth-order derivative curve is a second-order derivative curve, and,
out of the electrodes, a negative electrode is made from graphite
However, Stefanopoulou teaches the Zth-order derivative/integral curve generated by the data point sequence generator includes a Z_Dth-order derivative curve and a Z_Ith-order integral curve (Stefanopoulou, [0010] On the other hand, differential analysis has been widely used in electrochemical society focusing on more physical information from the electrode materials. Two most common differential analyses are differential voltage analysis (DVA) (Ref. 2.6, 2.12-2.14) and incremental capacity analysis (ICA) (Ref. 2.15), where those two have an inverse relationship),
the reference data point sequence from the reference data provider includes at least parts of a reference Z_Dth-order derivative curve and a reference Z_Ith-order
integral curve (Stefanopoulou, [0010] The basic idea of the differential analysis lies in that the valuable electrochemical information is hidden in raw voltage data; Thus, the voltage data is taken for differentiation by the capacity of the battery (i.e., dV/dQ for DVA; inverse of DVA is ICA dQ/dV) to reveal the hidden information of each electrode material), and
the diagnosis circuitry calculates, on the basis of the transformation parameters (Stefanopoulou, [0009] Voltage fitting approach adopts optimization algorithms to find a parameter set that provides the best fit for the battery voltage curve between the measured data and the model)
in a case of generating a data point sequence including the Z_Dth-order
derivative curve, the Z_Dth-order derivative curve is a second-order derivative curve (Stefanopoulou, [0010] On the other hand, differential analysis has been widely used in electrochemical society focusing on more physical information from the electrode materials. Two most common differential analyses are differential voltage analysis (DVA) (Ref. 2.6, 2.12-2.14) and incremental capacity analysis (ICA) (Ref. 2.15), where those two have an inverse relationship. The basic idea of the differential analysis lies in that the valuable electrochemical information is hidden in raw voltage data, Thus, the voltage data is taken for differentiation by the capacity of the battery (i.e., dV/dQ for DVA; inverse of DVA is ICA dQ/dV) to reveal the hidden information of each electrode material), and,
out of the electrodes, a negative electrode is made from graphite (Stefanopoulou, [0017] The reference battery cell may include an anode comprised of an active material selected from the group consisting graphite, lithium titanate, hard carbon, tin/cobalt alloy, and silicon carbon. The characteristic curve can be a differential voltage curve of the reference battery electrode);
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Gorrachategui in view of Torai and Stefanopoulou teachings because Stefanopoulou teaches the use of both derivative and integral representations of battery data and incremental capacity analysis to extract complementary electrochemical information. A person of ordinary skill in the art would have been motivated to include both representation in the combined systems in order to improve the completeness and reliability of feature extraction by capturing different characteristics and battery behavior that is not observable from a single representation alone.
Regarding Claim 13, Gorrachategui in view of Torai in further view of Stefanopoulou teaches the storage battery diagnosis device according to claim 11, wherein
the reference data provider provides a reference data point sequence of the storage battery and a reference data point sequence of at least one electrode out of a positive electrode and a negative electrode of the storage battery (Stefanopoulou, [0020] The controller can be configured to execute the program stored in the controller to calculate a negative electrode parameter from the first differential voltage point and the second differential voltage point. Alternatively or additionally, the controller can be configured to execute the program stored in the controller to calculate a utilized positive electrode potential from the negative electrode parameter. Alternatively or additionally, the controller can be configured to execute the program stored in the controller to calculate a positive electrode parameter from the first differential voltage point and the second differential voltage point. Alternatively or additionally, the controller can be configured to execute the program stored in the controller to calculate a utilized negative electrode potential from the positive electrode parameter. Alternatively or additionally, the controller can be configured to execute the program stored in the controller to select the characteristic curve from a plurality of characteristic curves stored in the controller),
the diagnosis circuitry
generates an electrode potential curve of the one electrode on the basis of
an electrode parameter and the reference data point sequence of the one electrode (Stefanopoulou, [0027] Step (e) of the method in may further include finding a match for the first differential voltage point and the second differential voltage point in a cell level and an individual electrode level with a half-cell potential. The method may further include estimating a set of negative electrode parameters using the first differential voltage point and the second differential voltage point),
subtracts the electrode potential curve from the reference data point
sequence of the storage batter. B y, to generate an electrode potential curve of another one of the electrodes (Stefanopoulou, [0079] When a cell is at equilibrium state without current flowing, the terminal voltage of the cell is equal to the OCV which is the electrical potential difference between the half-cell potential of positive U.sub.p(y) and negative U.sub.n(x) electrode, V.sub.oc(z)=U.sub.p(y)−U.sub.n(x)),
compares the electrode potential curve of the other electrode and a
reference data point sequence of the other electrode with each other (Stefanopoulou, [0009] Voltage fitting approach adopts optimization algorithms to find a parameter set that provides the best fit for the battery voltage curve between the measured data and the model), and
obtains an electrode parameter of the other electrode (Stefanopoulou, [0010] the voltage data is taken for differentiation by the capacity of the battery (i.e., dV/dQ for DVA; inverse of DVA is ICA dQ/dV) to reveal the hidden information of each electrode material. Since electrode materials have their own electrochemical features such as the phase transition of the material during lithium intercalation (Ref. 2.16, 2.17), these distinct features can be used for identifying the contribution of each electrode to the cell), and,
in a case where no reference data point sequence of the other electrode is
provided from the reference data provider, the diagnosis circuitry calculates a reference data point sequence of the other electrode on the basis of a difference between the reference data point sequence of the storage battery and the reference data point sequence of the one electrode (Stefanopoulou, [0079] The stoichiometric states x and y represent lithium mole fraction of each electrode materials. For example, a fully lithiated graphite is x=1 for Li.sub.xC.sub.6, i.e., one lithium atom per six carbon atoms. When a cell is at equilibrium state without current flowing, the terminal voltage of the cell is equal to the OCV which is the electrical potential difference between the half-cell potential of positive U.sub.p(y) and negative U.sub.n(x) electrode).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Gorrachategui in view of Torai and Stefanopoulou teachings because Gorrachategui teaches a battery diagnostic system utilizing extracted features and reference data point sequences for evaluating battery characteristics, Torai teaches processing and aligning such data points sequences through registration techniques to improve accuracy and consistency and Stefanopoulou teaches providing reference data point sequences at both the battery level and the individual electrode level and generating am electrode parameter and the reference data, and it would have been obvious to integrate these teachings to enable electrode level analysis and improve diagnostic accuracy. A person of ordinary skill in the art would have been motivated to integrate these teachings into the combined system in order to enable electrode analysis and improve the accuracy of the battery diagnostics.
Claims 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over US 20210293890 A1, Gorrachategui et al. (hereinafter Gorrachategui) in view of US 20160061908 A1, Torai et al. (hereinafter Torai), in further view of US 20160110913 A1, Kosey et al. (hereinafter Kosey).
Regarding Claim 14, Gorrachategui in view of Torai in further view of Kosey teaches the storage battery diagnosis device according to claim 1, wherein
the point set registrator performs point set registration through ICP (Kosey, [0041] The term “3D registration algorithm” is recognized by those skilled in the art and refers to any process, algorithm, method, procedure, and/or technique, for solving and/or approximating one or more solutions to the 3D registration problem. Some examples of 3D registration algorithms include the Iterative Closest Point algorithm).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Gorrachategui in view of Torai and Kosey teachings because Kosey teaches performing point set registration using an iterative closest point algorithm, and Gorrachategui does not explicitly disclose using ICP for point set registration. A person of ordinary skill in the art would have been motivated to make this combination in order to improve the accuracy and convergence of point set registration between data sets.
Regarding Claim 15, Gorrachategui in view of Torai in further view of Kosey teaches the storage battery diagnosis device according to claim 1, wherein
the point set registrator performs point set registration through ICP (Kosey, [0041] The term “3D registration algorithm” is recognized by those skilled in the art and refers to any process, algorithm, method, procedure, and/or technique, for solving and/or approximating one or more solutions to the 3D registration problem. Some examples of 3D registration algorithms include the Iterative Closest Point algorithm) and separately performs point set matching through the ICP on each type of feature point in the feature point set (Kosey, [0084] As is known by those versed in the art, 3D registration involves an attempt to align two or more 3D models, by finding or applying spatial transformations over the 3D models. 3D registration is useful in many imaging, graphical, image processing, computer vision, medical imaging, robotics, and pattern matching applications [0093] for a subgroup of 3D models that includes more than two 3D models, the Multiple Graphs or Hypergraphs extension of the Probabilistic Graph and Hypergraph Matching algorithm can be used. Further by way of example, as another alternative, another implementation of the parallel 3d registration scheme for 3D registration of a plurality of 3D models presented here can be applied in order to solve the 3D registration sub-process in a recursive fashion. It would be noted that many other alternatives also exist. Note that different algorithms may be applied to solve different atomic sub-problems).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Gorrachategui in view of Torai and Kosey teachings because Kosey teaches performing point set registration using an iterative closest point algorithm and further teaches performing matching on feature points, while Gorrachategui does not explicity disclose such separate matching for different feature point types. A person of ordinary skill in the art would have been motivated to make this combination in order to improve the accuracy and convergence of point set registration between data sets.
Regarding Claim 16, Gorrachategui in view of Torai in further view of Kosey teaches the storage battery diagnosis device according to claim 14, wherein
the ICP is one-dimensional ICP for a feature point set (Kosey, [Col 46. Line 21 - Col. 47 Line 8] Finding a best match position of the two one dimensional arrays can be done in a number of ways: least squares or ICP).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Gorrachategui in view of Torai and Kosey teachings because Kosey teaches performing point set registration using a ICP algorithm for feature point sets, while Gorrachetegui does not disclose any specific implementation of ICP. Although Kosey does not explicitly limit ICP to one-dimensional data, ICP is a general registration techniques applicable to data of varying dimensionality, including one-dimensional feature point sets. A person of ordinary skill in the art would have been motivated to apply ICP in a one-dimensional context when the feature point sets are one dimensional, as a straightforward adaptation of a known technique, in order to reduce computational complexity and improve efficiency while maintaining alignment accuracy.
Regarding Claim 17, Gorrachategui in view of Torai in further view of Kosey teaches the storage battery diagnosis device according to claim 1, wherein
the point set registrator performs point set registration through CPD (Kosey, [0041] The term “3D registration algorithm” is recognized by those skilled in the art and refers to any process, algorithm, method, procedure, and/or technique, for solving and/or approximating one or more solutions to the 3D registration problem. Some examples of 3D registration algorithms include the Iterative Closest Point algorithm, the Robust Point Matching algorithm, the Kernel Correlation algorithm, the Coherent Point Drift algorithm, RANSAC based algorithms, any graph and/or hypergraph matching algorithm, any one of the many variant of these algorithms, and so forth).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Gorrachategui in view of Torai and Kosey teachings because Torai teaches point set registration techniques for aligning data and Kosey teaches performing point set registration using CPD, and a person of ordinary skill in the art would have been motivated to combine these systems to improve alignment accuracy and overall performance.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclose:
-US 20180260965 A1, describing techniques for performing point set registration between datasets, including determining parameters such as rotation, translation, and scaling to align data points.
-US 20210349157 A1, describing systems and methods for determining battery state and parameters using battery models and processing measurement data, including estimating battery characteristics based on voltage and current data.
Conclusion
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/IBRAHIM NAGI SHOHATEE/
Examiner, Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857
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