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 .
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gogotsi et al. (US 9,171,679 B2 hereinafter Gogotsi) in view of Wang et al. (US 9,989,595 B1 hereinafter Wang).
As to claim 1, Gogotsi discloses a method for monitoring a capacitance of a capacitor (col. 1, lines 15-35; col. 7, lines 50-67; col. 8, lines 1-20; methods for monitoring and determining capacitance in electrochemical flow capacitors/supercapacitors through charge-discharge testing, current measurements, and voltage profiles), the method comprising: measuring an electrical current supplied from or to the capacitor over time (col. 9, lines 30-55; FIG. 8; current density measured during galvanostatic charge/discharge cycles over time); integrating the electrical current to determine a charge amount supplied from or to the capacitor over time (col. 10, lines 5-35; Equations 1-2; charge Q calculated via integration of current over time, ∫ I dt, for coulombic efficiency and capacity determination); determining a state of charge (SoC) of the capacitor over time (col. 10, lines 40-60; col. 11, lines 1-25; state of charge implicitly determined via voltage windows (e.g., 0-2.7V) and charge/discharge to equilibrium, with state of charge proportional to voltage in linear supercapacitors); determining a total charge capacity of the capacitor based on a linear relationship between changes in the charge amount and changes in the SoC (col. 9, lines 1-30; FIG. 6-7; total charge capacity derived from linear Q-V relationships in cyclic voltammetry and galvanostatic profiles, where ΔQ / ΔV reflects capacity, and state of charge implicitly ∝ V/ΔV); determining the capacitance based on the calculated total charge capacity (col. 8, lines 50-67; col. 10, lines 20-50; capacitance C = Q / ΔV directly calculated from integrated charge and voltage change, e.g., 0.7 F device capacitance); and outputting the determined capacitance (col. 12, lines 1-20; capacitance values output for system performance and control).
Gogotsi does not explicitly disclose determining a state of charge (SoC) of the capacitor over time using the specific term "SoC" and determining a total charge capacity of the capacitor based on a linear relationship between changes in the charge amount and changes in the SoC (though voltage is used as an implicit proxy for state of charge).
Wang teaches determining a state of charge (SoC) of the capacitor over time (col. 1, lines 5-25; col. 5, lines 20-60; col. 6, lines 1-50; Equations 5A-5E; state of charge explicitly determined as part of state vector x(t) from current integration, voltage measurements, and model-based estimation in electrochemical devices); determining a total charge capacity of the capacitor based on a linear relationship between changes in the charge amount and changes in the SoC (col. 5, lines 20-60; col. 6, lines 1-50; total capacity derived from state of charge-V_oc relationships and charge integration, with linear approximations in kinetic models for capacity estimation).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Gogotsi to include the explicit state of charge determination and linear charge-state of charge relationship techniques of Wang, in order to enable precise real-time capacity estimation and error reduction in dynamic supercapacitor applications, such as energy storage systems where noise and variations affect measurements (Wang, col. 1, lines 30-50; col. 2, lines 10-30; improving accuracy without offline testing, directly complementary to Gogotsi's charge-discharge monitoring).
As to claim 2, Gogotsi in view of Wang discloses the method of claim 1, wherein the determining the SoC comprises: determining a predicted SoC over time based on the charge amount and a nominal charge capacity of the capacitor (Wang, col. 6, lines 10-30; predicted SoC from integrated charge Q and nominal capacity C_nom via x(t+1) = A x(t) + B I(t)); determining an estimated terminal voltage of the capacitor based on the predicted SoC, the electrical current, and an equivalent circuit model of the capacitor (Wang, col. 5, lines 30-50; V_est = C x(t) from model including current and resistances); measuring a terminal voltage of the capacitor (Gogotsi, col. 9, lines 10-30; voltage measured during cycles); and determining the SoC by correcting the predicted SoC based on the estimated terminal voltage and the measured terminal voltage (Wang, col. 6, lines 30-50; correction term L (y(t) - C x(t)), where y(t) is measured voltage). The motivation to combine is the same as that of claim 1.
As to claim 3, Gogotsi in view of Wang discloses the method of claim 2, wherein the equivalent circuit model comprises an internal cell resistance of the capacitor (Wang, col. 5, lines 10-40; model includes high-frequency resistance R_hf, charge-transfer resistance R_ct; Gogotsi, col. 8, lines 20-40; equivalent series resistance in supercapacitor models). The motivation to combine is the same as that of claim 1.
As to claim 4, Gogotsi in view of Wang discloses the method of claim 2, wherein the nominal charge capacity is determined in advance based on one of: an amount of charge required for changing a terminal voltage of the capacitor between a minimum voltage value and a maximum voltage value; or the total charge capacity determined in a previous iteration of the method (Gogotsi, col. 10, lines 10-40; nominal capacity from charge to max voltage 2.7V from min 0V; Wang, col. 6, lines 40-50; iterative updates in real-time estimation). The motivation to combine is the same as that of claim 1.
As to claim 5, Gogotsi in view of Wang discloses the method of claim 2, wherein the determining the estimated terminal voltage comprises determining an open-circuit voltage of the capacitor over time using a lookup table (Wang, col. 7, lines 1-20; OCV from correlation data/lookup table based on SoC; Gogotsi, col. 11, lines 10-30; voltage profiles used for estimation). The motivation to combine is the same as that of claim 1.
As to claim 6, Gogotsi in view of Wang discloses the method of claim 2, wherein the determining the estimated terminal voltage comprises measuring a temperature of the capacitor to determine one or more internal cell parameters (Wang, col. 4, lines 50-67; col. 5, lines 1-20; temperature incorporated in kinetic models affecting resistances and capacitance). The motivation to combine is the same as that of claim 1.
As to claim 7, Gogotsi in view of Wang discloses the method of claim 2, wherein the correcting of the predicted SoC uses an internal state estimation algorithm (Wang, col. 6, lines 1-50; state estimation via observer equations). The motivation to combine is the same as that of claim 1.
As to claim 8, Gogotsi in view of Wang discloses the method of claim 7, wherein the internal state estimation algorithm is a Lueneberger observer algorithm or a Kalman filter algorithm (Wang, col. 7, lines 20-40; explicitly uses extended Kalman filter, with Luenberger observer variants obvious for deterministic cases per MPEP 2143). The motivation to combine is the same as that of claim 1.
As to claim 9, Gogotsi in view of Wang discloses the method of claim 1, wherein the determining of the total charge capacity comprises performing a least-squares regression based on measurement points associating changes in the charge amount with changes in the SoC (Wang, col. 6, lines 20-40; least-squares in parameter fitting for state updates; Gogotsi, col. 9, lines 40-60; regression in capacitance vs. current density plots). The motivation to combine is the same as that of claim 1.
As to claim 10, Gogotsi in view of Wang discloses a non-transitory computer readable medium including software code configured to perform the method of claim 1 when the software code is run on a computer processor (Wang, col. 8, lines 1-15; processor-executable instructions for the estimation method). The motivation to combine is the same as that of claim 1.
As to claim 11, Gogotsi discloses a capacitor monitoring device for monitoring the capacitance of a capacitor (col. 12, lines 1-30; system with integrated sensors and controllers), the capacitor monitoring device comprising: a current sensor configured to measure an electrical current supplied from or to the capacitor over time (col. 9, lines 30-50); a voltage sensor configured to measure a terminal voltage of the capacitor over time (col. 9, lines 10-30); a capacitance determination unit configured to measure an electrical current supplied from or to the capacitor over time (col. 9, lines 30-55; FIG. 8), integrate the electrical current to determine a charge amount supplied from or to the capacitor over time (col. 10, lines 5-35; Equations 1-2), determine a state of charge (SoC) of the capacitor over time (col. 10, lines 40-60; col. 11, lines 1-25), determine a total charge capacity of the capacitor based on a linear relationship between changes in the charge amount and changes in the SoC (col. 9, lines 1-30; FIG. 6-7), and determine the capacitance based on the calculated total charge capacity (col. 8, lines 50-67; col. 10, lines 20-50; col. 12, lines 10-20; control unit for calculations); and an output unit configured to output the determined capacitance (col. 12, lines 20-30).
Gogotsi does not explicitly disclose determining a state of charge (SoC) of the capacitor over time using the specific term "SoC" and determining a total charge capacity of the capacitor based on a linear relationship between changes in the charge amount and changes in the SoC (though voltage is used as an implicit proxy for state of charge) in the capacitance determination unit.
Wang teaches a capacitance determination unit configured to determine a state of charge (SoC) of the capacitor over time (col. 1, lines 5-25; col. 5, lines 20-60; col. 6, lines 1-50; state of charge explicitly determined as part of state vector x(t) from current integration, voltage measurements, and model-based estimation in electrochemical devices); determine a total charge capacity of the capacitor based on a linear relationship between changes in the charge amount and changes in the SoC (col. 5, lines 20-60; col. 6, lines 1-50; total capacity derived from state of charge-V_oc relationships and charge integration, with linear approximations in kinetic models for capacity estimation).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the device of Gogotsi to include the explicit state of charge determination and linear charge-state of charge relationship techniques of Wang in the capacitance determination unit, in order to enable precise real-time capacity estimation and error reduction in dynamic supercapacitor applications (Wang, col. 1, lines 30-50; col. 2, lines 10-30).
As to claim 12, Gogotsi in view of Wang discloses the device of claim 11, wherein the determining the SoC comprises: determining a predicted SoC over time based on the charge amount and a nominal charge capacity of the capacitor (Wang, col. 6, lines 10-30); determining an estimated terminal voltage of the capacitor based on the predicted SoC, the electrical current, and an equivalent circuit model of the capacitor (Wang, col. 5, lines 30-50); measuring a terminal voltage of the capacitor (Gogotsi, col. 9, lines 10-30); and determining the SoC by correcting the predicted SoC based on the estimated terminal voltage and the measured terminal voltage (Wang, col. 6, lines 30-50). The motivation to combine is the same as that of claim 11.
As to claim 13, Gogotsi in view of Wang discloses the device of claim 12, wherein the equivalent circuit model comprises an internal cell resistance of the capacitor (Wang, col. 5, lines 10-40; Gogotsi, col. 8, lines 20-40). The motivation to combine is the same as that of claim 11.
As to claim 14, Gogotsi in view of Wang discloses the device of claim 12, wherein the nominal charge capacity is determined in advance based on one of: an amount of charge required for changing a terminal voltage of the capacitor between a minimum voltage value and a maximum voltage value; or the total charge capacity determined in a previous iteration of the method (Gogotsi, col. 10, lines 10-40; Wang, col. 6, lines 40-50). The motivation to combine is the same as that of claim 11.
As to claim 15, Gogotsi in view of Wang discloses the device of claim 12, wherein the determining the estimated terminal voltage comprises determining an open-circuit voltage of the capacitor over time using a lookup table (Wang, col. 7, lines 1-20; Gogotsi, col. 11, lines 10-30). The motivation to combine is the same as that of claim 11.
As to claim 16, Gogotsi in view of Wang discloses the device of claim 12, wherein the determining the estimated terminal voltage comprises measuring a temperature of the capacitor to determine one or more internal cell parameters (Wang, col. 4, lines 50-67; col. 5, lines 1-20). The motivation to combine is the same as that of claim 11.
As to claim 17, Gogotsi in view of Wang discloses the device of claim 12, wherein the correcting of the predicted SoC uses an internal state estimation algorithm (Wang, col. 6, lines 1-50). The motivation to combine is the same as that of claim 11.
As to claim 18, Gogotsi in view of Wang discloses the device of claim 17, wherein the internal state estimation algorithm is a Lueneberger observer algorithm or a Kalman filter algorithm (Wang, col. 7, lines 20-40). The motivation to combine is the same as that of claim 11.
As to claim 19, Gogotsi in view of Wang discloses the device of claim 11, wherein the determining of the total charge capacity comprises performing a least-squares regression based on measurement points associating changes in the charge amount with changes in the SoC (Wang, col. 6, lines 20-40; Gogotsi, col. 9, lines 40-60). The motivation to combine is the same as that of claim 11.
As to claim 20, Gogotsi in view of Wang discloses a system comprising: a capacitor; and a capacitor monitoring device according to claim 11 (Gogotsi, col. 1, lines 20-35; supercapacitor assembly with monitoring; Wang, col. 8, lines 1-15; integrated system). The motivation to combine is the same as that of claim 11.
Conclusion
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/TUNG X NGUYEN/Primary Examiner, Art Unit 2858 2/7/2026