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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/02/2025 was filed after the mailing date of the Non-Final Rejection on 08/20/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
Applicant’s amendment filed 11/19/2025 has been entered. Claims 1-10 remain pending.
Response to Arguments
Applicant's arguments, see Pages 4-6, filed 11/19/2025, with respect to the 35 U.S.C. 102 rejection of Claims 1 and 10 have been fully considered but they are not persuasive.
Applicant argues on Page 5 that “Plett describes a conventional Kalman filter for estimating the state of charge (SOC) and state of health (SOH) of a battery system. (See Plett, pg. 1 lines 4-15). Equations (8) and (9) of Plett correspond to the standard process function and measurement function, respectively, which include stochastic variables wk (process noise) and vk (measurement noise). In contrast, present Claim 1 recites: “wherein an error term in the process model or the measurement model of the Kalman filter method, which describes a process noise or measurement noise, is updated in each case in successive iterations of the Kalman filter method.” The stochastic variables wk and vk relied upon by the Office Action in PLETT are described in Plett as "random variables” representing process noise (wk) and measurement noise (vk), and therefore as typical in conventional Kalman filter implementations. Their influence on the state estimation is statistical based on constant covariances. (See Plett, pg. 8 lines 10-26 and pg. 11 lines 5-15). Nothing in Plett teaches dynamically updating these error terms in each iteration of the filter (e.g. using current or voltage uncertainties or from parameter-variance). The only “updating” in Plett concerns the normal reduction of the estimation uncertainty P after each measurement. Accordingly, Plett implements a standard Kalman filter, as acknowledged in Plett. (See Plett pg. 1 line 24 to pg. 2 line 4). Additionally, Plett fails to recognize the problem addressed by Claim 1, in that constant process- and measurement-noise parameters cannot ensure accurate SOC estimation across different operating conditions.”
Examiner respectfully disagrees. Previously disclosed prior art Plett (WO2006057468) teaches on Page 6, Lines 2-15 "In particular, the Kalman filter takes into account both measurement uncertainty and estimation uncertainty when it updates its estimation in successive steps. The Kalman filter corrects both uncertainties based on new measurements received from sensors. This is very important for two reasons. First, sensors often have a noise factor, or uncertainty, associated with its measurement. Over time, if uncorrected, the measurement uncertainty can accumulate. Second, in any modeling system the estimation itself has inherent uncertainty because the internal dynamic of the system may change over time. The estimation of one time step may be less accurate than the next because the system may have changed internally to behave less similarly to the model. The correction mechanism in the Kalman filter minimizes these uncertainties at each time step and prevents them from degrading accuracy over time". That is, Plett details that the uncertainties (i.e. error) are minimized (i.e. updated) at each time step (i.e. each iteration).
Applicant's arguments, see Pages 6-11, filed 11/19/2025, with respect to the 35 U.S.C. 101 rejection have been fully considered but they are not persuasive.
Applicant argues on Pages 6-7 that “A method for determining a state of an energy storage device, wherein the state of the energy storage device is determined iteratively in each case in successive iterations of a Kalman filter method based on a plurality of measured values of current and voltage of a charging or discharging process of the energy storage device, wherein an error term in the process model or the measurement model of the Kalman filter method, which describes a process noise or measurement noise, is updated in each case in successive iterations of the Kalman filter method. Step 2A, Prong One The Office Action states, at page 4 that the limitations of Claim 1 "constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitation the fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics.” However, Applicant respectfully submits that “wherein the state of the energy storage device is determined iteratively in each case in successive iterations of a Kalman filter method based on a plurality of measured values of current and voltage of a charging or discharging process of the energy storage device, wherein an error term in the process model or the measurement model of the Kalman filter method, which describes a process noise or measurement noise, is updated in each case in successive iterations of the Kalman filter method” merely involves mathematical concepts, but no portion of Claim 1 recites mathematical concepts.
As established in MPEP § 2106.04(ID(A), Prong One expressly distinguishes between “claims that recite an exception (which require further eligibility analysis) and claims that merely involve an exception (which are eligible and do not require further eligibility analysis).” (Emphasis in original). See, also, MPEP § 2106.04(a)(1) (“When evaluating a claim to determine whether it recites an abstract idea, examiners should keep in mind that while ‘all inventions at some level embody, use, reflect, rest upon, or apply laws of nature, natural phenomenon, or abstract ideas’, not all claims recite an abstract idea.”) (citing Alice Corp. Pty. Ltd. v. CLS Bank, Int’1, 573 U.S. 208, 217, 110 USPQ2d 1976, 1980-81 (2014)).
While some of the limitations of Claim 1 involve mathematical concepts, the mathematical concepts are not recited in the claims. For example, Claim 1 does not recite any mathematical relationships, formulas, or calculations. Accordingly, Applicant respectfully submits that under Step 2A Prong One, Claims 1 is patent eligible under 35 U.S.C. 101 because Claim 1 does not recite an abstract idea. As such, withdrawal of the rejections of Claims 1 and 10 is respectfully requested.”
Examiner respectfully disagrees. The MPEP recites in 2106.04(a)(2) that “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of ‘‘managing a stable value protected life insurance policy by performing calculations and manipulating the results’’ as an abstract idea)”. Furthermore, the claims are interpreted under broadest reasonable interpretation in light of the specifications. The specifications detail starting in [0042] “A brief summary of a general nonlinear extended Kalman filter is provided hereinafter, as described in lecture notes for course ECE5720: Battery Management and Control by Gregory Plett, 2015, University of Colorado”. [0043]-[0055] then details the equations associated with the Kalman filter operation. The specification further details the mathematical relationship starting in [0061] and ending in [0131]. Claim 1 details the limitation “the state of the energy storage device is determined iteratively in each case in successive iterations of a Kalman filter method based on a plurality of measured values of current and voltage of a charging or discharging process of the energy storage device”. This limitation is not merely reciting a mathematical calculation, but explicitly in words is reciting a plurality of mathematical calculations occurring (that is, iteratively) that determines the “state of the energy storage device” based on the input of “a plurality of measured values of current and voltage”. That is, the claims are detailing in words the operation on the data (plurality of measured values) to determine an output. The mathematical function detailed in the claim is the Kalman filter, which under broadest reasonable interpretation in light of the specifications is a mathematical function. Thus the claim does not merely involve the exception, the claim recites the exception. Similarly with the second limitation of the claim of “wherein an error term in the process model or the measurement model of the Kalman filter method, which describes a process noise or measurement noise, is updated in each case in successive iterations of the Kalman filter method” details a mathematical operation occurring as the process model or measurement model of the Kalman filter is interpreted under broadest reasonable interpretation in light of the specifications as being mathematical as detailed above, and the error term is a further mathematical calculation occurring. The specification discloses in [0044], [0056], and [0086] the error terms (specifically in terms of a covariance matrix).
Applicant argues on Pages 8-9 that “However, the claim as a whole recites a specific application of adaptive filtering in the field of battery management systems. Several claim features integrate the mathematical concept into a concrete technical process:
1. Obtaining real-world sensor data — current and voltage measurements of a physical energy storage device during charging or discharging. These measurements cannot be carried out abstractly and necessarily involve tangible hardware (sensors, wiring, converter interface).
2. Model-based estimation — the Kalman-filter process and measurement models explicitly describe the electrochemical behavior of the battery (open-circuit voltage, internal resistance, RC time constants, capacity, etc.).
3. Dynamic adaptation of noise covariances (X_q(k), x_r(k)) — the claimed update step uses the current measurement uncertainties and model-parameter variances to re-determine the process- and measurement-noise levels for that specific physical battery in its present operating condition (temperature, aging, load).
4. Technical result — the dynamically adapted filter yields a more accurate and robust determination of the state of charge (SOC), which is an operational variable directly governing charging and discharging control of the battery pack.
Thus, the claimed process is tied to a specific machine (an energy-storage device with current and voltage sensors) and produces data that directly influence the physical operation of that machine. The claimed process improves the accuracy of a battery-state estimator — a physical control component — and thus represents an improvement in the operation of a technological system. Accordingly, Applicant respectfully submits that under Step 2A Prong Two, Claims 1 and 10 are patent eligible under 35 U.S.C. 101 because claim recitations are integrated into a practical application as the claimed process provides an improvement to technology and improvements to the functioning of a computer itself. As such, withdrawal of the rejections of Claims 1 and 10 are respectfully requested. Step 2B Applicant does not concede that Claims 1 and 10 recite limitations of performing mathematics that are not integrated into a practical application. However, Applicant respectfully submits that Claims 1 and 10 as a whole recites a plurality of additional features that amount to “significantly more” than an abstract idea implemented on a generic computer:
1. Using current and voltage sensor measurements, applying a physical battery model, and redetermining noise covariances based on measured and modeled uncertainties go far beyond generic data processing. These measurements link the claimed method to a concrete piece of hardware and cannot be carried out in the abstract.
2. The method improves the accuracy and reliability of SOC estimation without requiring additional sensors or full charge/discharge cycles, thus enhancing energy-storage control, lifetime, and safety.
3. This represents a technological improvement in the field of electrochemical energy-storage management. Reference is made to Thales Visionix, Inc. v. United States, 850 F.3d 1343 (Fed. Cir. 2017), where it was held (emphasis added): “Just as the claims in Diehr reduced the likelihood that the rubber molding process would result in ‘overcuring’ or ‘undercuring,’ the claims here result in a system that reduces errors in an inertial system that tracks an object on a moving platform.”
The claims in Thales Visionix related to determining the orientation of an object relative to a moving reference frame based on sensor measurements. Thus, the claimed subject matter enabled accurate real-time tracking of the object’s motion — a concrete technical application of a mathematical relationship to improve a physical system.
Examiner respectfully disagrees. The MPEP states in 2106.04(d) that "The Supreme Court has long distinguished between principles themselves (which are not patent eligible) and the integration of those principles into practical applications (which are patent eligible). See, e.g., Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 80, 84, 101 USPQ2d 1961, 1968-69, 1970 (2012) (noting that the Court in Diamond v. Diehr found ‘‘the overall process patent eligible because of the way the additional steps of the process integrated the equation into the process as a whole,’’ but the Court in Gottschalk v. Benson ‘‘held that simply implementing a mathematical principle on a physical machine, namely a computer, was not a patentable application of that principle’’)." Furthermore, the MPEP details in 2106.04(d)(I) that "The Supreme Court and Federal Circuit have identified a number of considerations as relevant to the evaluation of whether the claimed additional elements demonstrate that a claim is directed to patent-eligible subject matter. The list of considerations here is not intended to be exclusive or limiting. Additional elements can often be analyzed based on more than one type of consideration and the type of consideration is of no import to the eligibility analysis." Claim 1 does not detail any additional elements, thus Claim 1 does not have any additional elements to consider for the integration of the judicial exception into a practical application. Claim 10 details “computing unit, a memory unit, an interface unit, wherein the memory unit stores commands executable by the computing unit”, which are interpreted under broadest reasonable interpretation to be generic computer elements. Generic computer elements are not considered significantly more than the abstract idea and do not integrate the abstract idea into a practical application. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
With regards to applicant's argument that the claim features integrate the mathematical concept into a concrete technical process, Applicant argues that obtaining real-world sensor data, that is the current and voltage measurements of a physical energy storage device during charging and discharging. There are no claim limitations that positively recite the obtaining of real world sensor data. Claim 1 details that "the state of the energy storage device is determined iteratively in each case in successive iterations of a Kalman filter method based on a plurality of measured values of current and voltage of a discharging or discharging process of the energy storage device". That is, the claim limitation is detailing the input of data that has already been gathered into the mathematical operation, and does not detail any limitation towards the gathering of said data.
With regards to the model based estimation and "the Kalman-filter process and measurement models explicitly describe the electrochemical behavior of the battery (open-circuit voltage, internal resistance, RC time constants, capacity, etc.)", the Kalman filter processing and measurement models detailing the mathematical relationship between the open-circuit voltage, internal resistance, RC time constants, capacity, etc. are all details towards mathematical relationships and functions that the Kalman filter process and measurement models are performing the calculations to determine the state of the energy storage device.
With regards to the dynamic adaption of noise covariances, the claim limitation details "an error term in the process model or the measurement model of the Kalman filter method, which describes a process noise or measurement noise, is updated in each case in successive iterations of the Kalman filter method". The claim limitation details further mathematical calculations occurring. Furthermore, the claim does not detail the operating conditions, nor do any dependent claims detail the operating conditions (temperature, aging, load). The claim limitation details the mathematical calculation occurring to determine the error term.
With regards to the technical result, the claim limitations do not detail any limitation towards the charging and discharging control of the battery pack. The claim limitation only references that the data input into the Kalman filter is measured values and that the measured values came from the charging or discharging process of the energy storage device. That is, the claim limitations do not detail limitations towards the measuring of the energy storage device, nor does the claim limitations detail limitations towards the action of charging and discharging the battery. Furthermore, the claim limitations do not detail the control of the charging or discharging of the battery.
With regards to the claimed process being tied to a specific machine of "an energy storage device with current and voltage data", the claim limitations do not include limitations towards the inclusion of the energy storage device, and only details that there are measured values of current and voltage. That is, the claim limitations are silent to the incorporation of sensors themselves. Furthermore, Applicant argues that the process is an improvement to the accuracy and represents an improvement in the operation of the physical control component, but does not detail in the limitations any incorporation of this detail. That is, the claim limitations only detail a) determination of the state of the energy storage device, and b) an error term. Thus, the claims do not include any details towards this improved control.
With regards to Applicant's argument with respect to Thales Visionix, Inc. v. United States, 850 F.3d 1343 (Fed. Cir. 2017), the fact patterns between Thales Visionix and the instant application are different. As applicant's argument details, Thales Visionix is related to determining the orientation of an object relative to a moving reference frame based on sensor measurements. The instant application details determining a state of a battery through a Kalman filter and determining an error term.
Applicant argues on Page 10 that “Similarly, present Claim 1 is directed to determining the state of charge (SOC) of an energy-storage device based on measured current and voltage signals and dynamically updated noise covariances within a Kalman-filter framework. The claimed method therefore enables tracking the charge state of a physical battery in real time and reduces estimation errors arising from changing operating conditions and aging.
Moreover, notwithstanding the above, the claim also constitutes an improvement in the field of battery technology itself. Conventional state-of-charge (SOC) estimation techniques in battery-management systems typically rely on Kalman filters with fixed process- and measurement-noise parameters. Such fixed parameters represent a compromise calibration valid only for specific conditions (e.g., limited temperature or current ranges) and often lead to significant SOC-estimation errors when the battery operates under different or dynamic load situations.
By contrast, the claimed method continuously and dynamically updates the process- and measurement-noise error terms within each iteration of the Kalman filter, based e.g. on current and voltage measurements and the uncertainties of the battery-model parameters. This enables the estimator to adapt in real time to changing operating conditions and aging, resulting in faster- converging, more accurate, and more-robust SOC estimates without requiring recalibration or additional sensors. The improved SOC accuracy directly enhances the efficiency, safety, and lifetime of the energy-storage system, thereby improving the overall operation of a technological system rather than merely performing a mathematical calculation on a generic computer.
Courts have recognized that such advances in the operation of a technological system satisfy Step 2B of the Alice/Mayo framework. For example, in Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016), and McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016), claims improving the functioning of a computer or another technical field were found patent-eligible. Here, the technology being improved is battery monitoring and control, specifically, the Kalman-filter-based estimation of SOC of an energy storage device, not merely generic computation. Accordingly, when the additional limitations are considered as an ordered combination, they transform the underlying mathematical operation into a specific, technical solution to a long-standing problem in adaptive battery-state estimation.”
Examiner respectfully disagrees. As stated above, Claims 1 and 10 detail the input data to the Kalman filter method includes measured data, but does not include limitations towards that of measuring the voltage or current, nor does the claim limitations details operations of charging or discharging the energy storage device. Thus, the claim limitations do not include details towards determining the tracking of the charge state of a physical battery in real time and reduction of estimation errors arising from changing operation conditions and aging. The MPEP details in 2106.04(D)(III) that "The Prong Two analysis considers the claim as a whole. That is, the limitations containing the judicial exception as well as the additional elements in the claim besides the judicial exception need to be evaluated together to determine whether the claim integrates the judicial exception into a practical application. Because a judicial exception alone is not eligible subject matter, if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. However, the way in which the additional elements use or interact with the exception may integrate it into a practical application. Accordingly, the additional limitations should not be evaluated in a vacuum, completely separate from the recited judicial exception. Instead, the analysis should take into consideration all the claim limitations and how those limitations interact and impact each other when evaluating whether the exception is integrated into a practical application." As detailed above, Claim 1 contains only the judicial exception and no additional elements. Thus Claim 1 contains no additional elements in part or as a whole to integrate the judicial exception into a practical application. Claim 10 contains the additional elements of “computing unit, a memory unit, an interface unit, wherein the memory unit stores commands executable by the computing unit”. The claim limitations are not detailing an improvement to a computer itself as the computer elements are merely performing the mathematical operations to determine the state of the energy storage device and the error term from the Kalman filter. The claim limitations do not detail limitations relating to the monitoring or control step. Thus the claim limitations are directed towards the mathematical operations and the additional elements of Claim 10 do not amount to significantly more than the mathematical operations.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 are rejected under 35 U.S.C. 101. The claimed invention is directed to the abstract concept of performing mathematical abstract steps without significantly more. The claim(s) recite(s) the following abstract concepts in BOLD of
1. (Original) A method for determining a state of an energy storage device,
wherein the state of the energy storage device is determined iteratively in each case in successive iterations of a Kalman filter method based on a plurality of measured values of current and voltage of a charging or discharging process of the energy storage device,
wherein an error term in the process model or the measurement model of the Kalman filter method, which describes a process noise or measurement noise, is updated in each case in successive iterations of the Kalman filter method.
10. (Currently Amended) A device for determining a state of an energy storage device, comprising a computing unit, a memory unit, an interface unit, wherein the memory unit stores commands executable by the computing unit, wherein the device is designed, when the commands are executed in the computing unit, to determine a state of the energy storage device in each of successive iterations of a Kalman filter method based on a plurality of measured values of current and voltage of a charging or discharging process of the energy storage device,
wherein an error term in the process model or the measurement model of the Kalman filter method, which describes a process noise or measurement noise, is updated in each case in successive iterations of the Kalman filter method.
Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitation the fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that since the claimed methods and system are not tied to a particular machine or apparatus, they do not represent an improvement to another technology or technical field. Similarly there are no other meaningful limitations linking the use to a particular technological environment. Finally, there is nothing in the claims that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea. Claims 1 contain no additional elements. Claim 10 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitation of “computing unit, a memory unit, an interface unit, wherein the memory unit stores commands executable by the computing unit” is interpreted under broadest reasonable interpretation to be a generic computer elements. Generic computer elements are not considered significantly more than the abstract idea and do not integrate the abstract idea into a practical application. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
Claims 2-9 further limit the abstract ideas without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5 and 7-10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Plett (WO2006057468).
In regards to Claim 1, Plett teaches “wherein the state of the energy storage device is determined iteratively in each case in successive iterations of a Kalman filter method based on a plurality of measured values of current and voltage of a charging or discharging process of the energy storage device (method/apparatus for the estimation of battery pack system states and parameters using digital filtering techniques that include Kalman filtering – Page 1; Lines 4-15; the Kalman filter is governed by equations (8) and (9) – Page 8 Lines 10-15; Figure 6 details with step 606 an increment time index k, i.e. iterations; Figure 3a shows the components of the estimator with the battery connected to a load circuit, i.e. charging or discharging, and measurements of the battery terminal voltage are made with a voltmeter and measurements of the battery current are made with an ammeter, with the measurements processed with the arithmetic circuit 304, and the arithmetic circuit uses a mathematical model of the battery with equations (14) and (15) – Page 20 Lines 9-29 and Page 21 Lines 1-14),
wherein an error term in the process model or the measurement model of the Kalman filter method, which describes a process noise or measurement noise, is updated in each case in successive iterations of the Kalman filter method (Kalman filter takes into account both measurement uncertainty and estimation uncertainty when it updates it estimation in successive steps and the Kalman filter corrects both uncertainties based on new measurements received from sensors. This is important as sensors have a noise factor/uncertainty associated with the measurement and over time the measurement uncertainty can accumulate and the estimation itself has inherent uncertainty and the correction mechanism in the Kalman filter minimizes these uncertainties at each time step and prevents them from degrading accuracy over time – Page 6, Lines 2-17; Equation (9) governs the estimation of the measurement uncertainty with the added random variables wk and vk in equations (8) and (9) represent the process noise and the measurement noise and their contribution to the estimation is represented by their covariance matrices in Figures 1a and 1B – Page 8, Lines 14-26).”
In regards to Claim 2, Plett discloses the claimed invention as detailed above. Plett further teaches “the error term of the Kalman filter method is determined using values of at least one parameter of a model of the Kalman filter method (Equation (9) governs the estimation of the measurement uncertainty with the added random variables wk and vk, i.e. parameters, in equations (8) and (9) represent the process noise and the measurement noise and their contribution to the estimation is represented by their covariance matrices in Figures 1A and 1B – Page 8, Lines 14-26).”
In regards to Claim 3, Plett discloses the claimed invention as detailed above. Plett further teaches “wherein the error term of the Kalman filter method is determined using an uncertainty or uncertainties of the current and/or voltage of the charging or discharging process of the energy storage device (Kalman filter corrects uncertainties based on new measurements received from sensors – Page 6, Lines 4-10; Equation (9) governs the estimation of the measurement uncertainty with the added random variables wk and vk, i.e. parameters, in equations (8) and (9) represent the process noise and the measurement noise and their contribution to the estimation is represented by their covariance matrices in Figures 1A and 1B – Page 8, Lines 14-26; measurements of the battery terminal voltage are made with a voltmeter and measurements of the battery current are made with an ammeter, with the measurements processed with the arithmetic circuit 304, and the arithmetic circuit uses a mathematical model of the battery with equations (14) and (15) – Page 20 Lines 9-29 and Page 21 Lines 1-14).”
In regards to Claim 4, Plett discloses the claimed invention as detailed above. Plett further teaches “wherein the error term of the Kalman filter method is determined using uncertainties of values of at least one parameter of a model of the Kalman filter method (Equation (9) governs the estimation of the measurement uncertainty with the added random variables wk and vk, i.e. parameters, in equations (8) and (9) represent the process noise and the measurement noise and their contribution to the estimation is represented by their covariance matrices in Figures 1A and 1B – Page 8, Lines 14-26).”
In regards to Claim 5, Plett discloses the claimed invention as detailed above. Plett further teaches “wherein at least one uncertainty of a parameter of the model is determined using an absolute value of at least one parameter of the model and/or at least one measured quantity of current and voltage (Equation (9) governs the estimation of the measurement uncertainty with the added random variables wk and vk, i.e. parameters, in equations (8) and (9) represent the process noise and the measurement noise and their contribution to the estimation is represented by their covariance matrices in Figures 1A and 1B – Page 8, Lines 14-26; equation 17 yk also comprises measured current value ik, i.e. measured quantity of current – Page 22 Lines 5-10; the measurement from terminal voltage mk, i.e. measured quantity of voltage, are used to calculated the corrected state vector – Page 26, Lines 1-10).”
In regards to Claim 7, Plett discloses the claimed invention as detailed above. Plett further teaches “wherein the at least one parameter is selected from a set comprising: a capacity of the energy storage device (mathematical model with the states and parameters of interest given with Equation (27), with Ck as the cell capacity – Page 40, Lines 15-25).”
In regards to Claim 8, Plett discloses the claimed invention as detailed above. Plett further teaches “wherein the error term of the process model is a covariance matrix that describes a cross-dependency of the uncertainties of process parameters of a plurality of parameters of a model of the Kalman filter method (Equation 151 the result of the correct component in the current time step is used to predict the result for the next time step with equation 152 predicts the uncertainty which is also referred to as the error covariance and as such, the matrix in equation 152 is the process noise covariance matrix – Page 9, Lines 10-20).”
In regards to Claim 9, Plett discloses the claimed invention as detailed above. Plett further teaches “wherein the uncertainties of the process parameters of the plurality of process parameters are modeled by probability distributions which are selected from: Gaussian normal distribution, uniform distribution, Weibull distribution (the variables wk, vk, rk, and ek are independent, zero-mean, Gaussian noise processes, i.e. Gaussian normal distribution, of covariance Matrices – Page 37, Table 1).”
In regards to Claim 10, Plett teaches “a computing unit, a memory unit, an interface unit, wherein the memory unit stores commands executable by the computing unit (the arithmetic circuit includes processor, gate arrays, custom logic, computer, memory, storage, communication interfaces – Page 33, Lines 20-30), wherein the device is designed, when the commands are executed in the computing unit, to determine a state of the energy storage device in each of successive iterations of a Kalman filter method based on a plurality of measured values of current and voltage of a charging or discharging process of the energy storage device (method/apparatus for the estimation of battery pack system states and parameters using digital filtering techniques that include Kalman filtering – Page 1; Lines 4-15; the Kalman filter is governed by equations (8) and (9) – Page 8 Lines 10-15; Figure 6 details with step 606 an increment time index k, i.e. iterations; Figure 3a shows the components of the estimator with the battery connected to a load circuit, i.e. charging/discharging, and measurements of the battery terminal voltage are made with a voltmeter and measurements of the battery current are made with an ammeter, with the measurements processed with the arithmetic circuit 304, and the arithmetic circuit uses a mathematical model of the battery with equations (14) and (15) – Page 20 Lines 9-29 and Page 21 Lines 1-14),
wherein an error term in the process model or the measurement model of the Kalman filter method, which describes a process noise or measurement noise, is updated in each case in successive iterations of the Kalman filter method (Kalman filter takes into account both measurement uncertainty and estimation uncertainty when it updates it estimation in successive steps and the Kalman filter corrects both uncertainties based on new measurements received from sensors. This is important as sensors have a noise factor/uncertainty associated with the measurement and over time the measurement uncertainty can accumulate and the estimation itself has inherent uncertainty and the correction mechanism in the Kalman filter minimizes these uncertainties at each time step and prevents them from degrading accuracy over time – Page 6, Lines 2-17; Equation (9) governs the estimation of the measurement uncertainty with the added random variables wk and vk in equations (8) and (9) represent the process noise and the measurement noise and their contribution to the estimation is represented by their covariance matrices in Figures 1a and 1B – Page 8, Lines 14-26).”
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.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Plett in view of Balasingam (US20140244225).
In regards to Claim 6, Plett discloses the claimed invention as detailed above in Claim 2. Plett is silent with regards to the language of “wherein the model is a process model that determines a change of current and voltage of the energy storage as a function of the iterations based on an equivalent circuit model”
Balasingam teaches “wherein the model is a process model that determines a change of current and voltage of the energy storage as a function of the iterations based on an equivalent circuit model (change in the initial estimated voltage drop model parameter vector and initial estimated capacity dependence on the number of iterations – [0084]; operational mode of the battery is based on the load, and for the equivalent model is based on the operational mode and the operational model is determined based on the current and voltage associated with the battery - [0103]; equivalent circuit model for the determine mode is selected based on the determined mode – [0104]; calculation of the state of charge using the equivalent circuit model – [0105]; iteration over the battery with voltage/current measurements and state of charge – [106], Figure 12; different iterations use different voltage measurements, where in previous in time voltage measurement used in the current iteration or v[k+1] could be used in iteration k+2 – [0107]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Plett to incorporate the teaching of Balasingam to incorporate the use of the equivalent circuit model. By using an equivalent circuit model this is an improvement that yields predictable results in evaluation of the state of charge of the battery.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YOSSEF KORANG-BEHESHTI whose telephone number is (571)272-3291. The examiner can normally be reached Monday - Friday 10:00 am - 6:30 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached at (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/YOSSEF KORANG-BEHESHTI/Examiner, Art Unit 2863