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 .
Response to Amendment
The Amendments to the Claims filed 12/10/2025 have been entered. Claims 1-15 are pending in the application. Claims 11-15 are new. Applicant’s amendment to the Claims have overcome each and every 35 U.S.C. 112(b) previously set forth in the non-final rejection dated 10/02/2025. Due to amendments to the claims new 35 U.S.C. 112(b) rejections and 35 U.S.C. 103 rejections are presented below.
Claim Rejections - 35 USC § 112
As noted above the previous 35 U.S.C. 112(b) rejections previously set forth have been overcome by amendment to the claims. However, new 112(b) issues have arisen due to amendment to the claims.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 15 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 15 recites the limitations “
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” in lines 3-4. It is unclear from the language of the claim what the variable “T” is defined as. The claims are indefinite, because it is unclear what is required by the claims. When possible it is preferred that variables recited in an equation are defined in the claims.
Claims that depend on the above rejected claims are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), Second paragraph.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract without significantly more.
With respect to claim 1 the limitation(s):
obtaining battery data of a device, wherein the battery data comprises a voltage and a current of a battery of the device;
wherein the device comprises a power station that repeatedly switches between charging and discharging, wherein the current of the battery is irregular, wherein the device has a cluster-box-cell structure comprising cells connected in series;
estimating an open circuit voltage of the battery based on the battery data;
establishing a function curve between a state of charge and the open circuit voltage of the battery;
extracting a life degradation curve based on the function curve; and
performing life degradation analysis on the battery based on the life degradation curve.
These limitation(s) highlighted in (bold) is/are directed to an abstract idea and would fall within the “Mental Processes” and “Mathematical Concepts” groupings of abstract ideas. The above portion(s) of the claim(s) constitute(s) an abstract idea because:
The limitation(s) regarding “estimating an open circuit voltage of the battery based on the battery data”, as drafted, is an act of observation and evaluation that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim language precludes the Step(s) from practically being performed in the mind. For example, “estimating” in the context of this claim encompasses the user manually estimating an open circuit voltage.
Further, the limitation regarding “estimating an open circuit voltage of the battery based on the battery data”, as drafted, falls within the “Mathematical Concepts” groupings of abstract ideas. This interpretation is supported in the specification as shown by para[0053] – para[0054] of the specification as filed, which is an explicit recitation of an equation corresponding to the claimed limitation. 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).
The limitation(s) regarding “establishing a function curve between a state of charge and the open circuit voltage of the battery”, as drafted, is an act of observation and evaluation that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim language precludes the Step(s) from practically being performed in the mind. For example, “establishing” in the context of this claim encompasses the user manually establishing a curve between a state of charge and an open circuit voltage.
Further, the limitation regarding “establishing a function curve between a state of charge and the open circuit voltage of the battery”, as drafted, falls within the “Mathematical Concepts” groupings of abstract ideas. This interpretation is supported in the specification as shown by para[0066] – para[0067] of the specification as filed, which is an explicit recitation of an equation corresponding to the claimed limitation. 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).
The limitation(s) regarding “extracting a life degradation curve based on the function curve”, as drafted, is an act of observation and evaluation that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim language precludes the Step(s) from practically being performed in the mind. For example, “extracting” in the context of this claim encompasses the user manually extracting a life degradation curve.
Further, the limitation regarding “extracting a life degradation curve based on the function curve”, as drafted, falls within the “Mathematical Concepts” groupings of abstract ideas. This interpretation is supported in the specification as shown by para[0071] – para[0072] of the specification as filed, which is an explicit recitation of an equation corresponding to the claimed limitation. 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).
The limitation(s) regarding “performing life degradation analysis on the battery based on the life degradation curve”, as drafted, is an act of observation and evaluation that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim language precludes the Step(s) from practically being performed in the mind. For example, “performing” in the context of this claim encompasses the user manually performing an analysis to determine degradation of a battery.
Further, referring to the MPEP 2106.04, the claim limitations are analogous to a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Further, if a claim limitation, under its broadest reasonable interpretation, recites mathematical relationships, mathematical formulas or equations, and mathematical calculations, then it fall within the “Mathematical Concepts” groupings of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application because the non- abstract additional elements of the claims do not impose meaningful limits on practicing the abstract idea(s) recited in the preceding claim(s). In particular, the claims recited the additional elements of:
The limitation(s) regarding “wherein the device comprises a power station that repeatedly switches between charging and discharging, wherein the current of the battery is irregular, wherein the device has a cluster-box-cell structure comprising cells connected in series” does/do not integrate the abstract idea into a practical application, because it is recited at such a high-level of generality that it is viewed as generally linking the use of the judicial exception to power stations. Generally linking the use of the judicial exception to a particular technological environment or field of use, fails to integrate the abstract ideas into a practical application, because the claim does not specify what practical application the claim is directed to.
The limitation(s) regarding “obtaining battery data of a device, wherein the battery data comprises a voltage and a current of a battery of the device” does/do not integrate the abstract idea into a practical application because the claim does not specify what practical application the claim is directed to. Rather the limitation is recited at such a high-level of generality that it amounts to no more than adding insignificant extra- solution activity to the judicial exception, i.e. data gathering. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they are regarded as data gathering steps necessary or routine to implement the abstract idea.
As such Examiner does NOT view that the claims:
-Improve the functioning of a computer, or to any other technology or technical field;
-Apply the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b);
-Effect a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c); or
-Apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception – see MPEP 2106.05(e) and Vanda Memo.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements amount to no more than mere instructions to apply the exception using a generic computer component, or are well-understood, routine, and conventional (WURC) data gathering functions.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “power station” is/are seen as generally linking the use of the judicial exception to a particular technological environment. Linking a judicial exception to a technological environment cannot provide an inventive concept. Similarly, with regards to the additional element(s) of “obtaining battery data” is/are viewed as insignificant extra-solution activity, such as mere data gathering in a conventional way and, therefore, does not provide an inventive concept.
Examiner further notes that such additional elements are viewed to be well- understood, routine, and conventional (WURC) as evidenced by: Wang et al. (US 20120105069 A1), Li et la. (Li, Ming, et al. "A battery SOC estimation method based on AFFRLS-EKF." Sensors 21.17 (2021): 5698.), Xiong et al. (CN 106980091 A), Gismero et al. (Gismero, Alejandro, Daniel-Ioan Stroe, and Erik Schaltz. "Comparative study of state of charge estimation under different open circuit voltage test conditions for lithium-ion batteries." IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2020.), and Patil et al. (US 20210325470 A1).
Considering the claim as a whole, one of ordinary skill in the art would not know the practical application of the present invention since the claims do not apply or use the judicial exception in some meaningful way. As currently claimed, Examiner views that the additional elements do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, because the claims fails to recite clearly how the judicial exception is applied in a manner that does not monopolize the exception because the limitation regarding “power station” and “battery data” can be viewed as a field of use, necessary data gathering, and any device and do not impose a meaningful limitation describing what problem is being remedied or solved.
Dependent claims 2-15 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additionally recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as detailed below: there are no additional element(s) in the dependent claims that adds a meaningful limitation to the abstract idea to make the claims significantly more than the judicial exception (abstract idea).
Claims 6-7 and 11-12 recite limitations regarding data gathering steps and insignificant application necessary or routine to implement the abstract idea and thus are not significantly more than the abstract idea and viewed to be well known routine and conventional as evidenced by the prior art shown above.
Claims 2-5 and 7-8, and 13-15 further limit the abstract idea with an abstract idea, such as an “Mental Processes” and “Mathematical Concepts”, and thus the claims are still directed to an abstract idea without significantly more.
Claims 9-11 recites generic computer components performing the generic computer function of receiving, storing, and comparing data such that it amounts to no more than mere instruction to apply the exception using a generic computer component.
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(s) 1, 5, and 7-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20120105069 A1) in view of Li et al. (US 20220416330 A1), hereinafter Li’330, and Balasingam et al. (US 20140244225 A1).
Regarding Claim 1. Wang teaches:
A method for analyzing battery life degradation, comprising:
obtaining battery data of a device, wherein the battery data comprises a voltage and a current of a battery of the device (See Fig. 1, para[0016], and para[0038]: A voltage device 206, such as a voltmeter, may be disposed between the first node 202 and the second node 204 to obtain a reading of the measured battery voltage 207 (V). An electric current measuring device 208 measures an electric current 209 (I) at first node 202.);
estimating an open circuit voltage of the battery based on the battery data (See Fig. 1, Fig. 2, para[0018], para[0029], and para[0039]: The measured battery voltage (V) 207 may be represented by the relationship: V=OCV+Vdl+IR. A battery state estimator may be applied to the measured battery parameters to estimate the battery states including the open-circuit voltage OCV.);
establishing a function curve between a state of charge and the open circuit voltage of the battery (See Fig. 2, Fig. 3, para[0023], para[0030], and para[0047]: This includes analyzing derivatives of an OCV-SOC relationship of a battery cell during an extended discharge event, wherein OCV is the open circuit voltage and SOC is one of state of charge and a state of discharge of the battery cell. This includes calculating the OCV-SOC relationship of the cathode electrode using the previously determined cell discharge voltage. OCV-SOC curves that depict OCV-SOC relations of the cathode and the anode for the new battery cell, i.e., the cathode discharge response curve 340 and the anode charge response curve 345.);
extracting a life degradation curve based on the function curve (See Fig. 2, Fig. 4, Fig. 5, para[0006], para[0023] – para[0024}, para[0031], and para[0033] – para[0035]: It is known to use differential voltage analysis, i.e., dV/dQ vs. V, to determine the source of capacity fade for lithium-ion batteries. It is known to use differential charge analysis, i.e., dQ/dV vs. Q, to determine the capacity fade for lithium-ion batteries and to quantify the composition change in materials. A differential curve technique is applied to directly monitor the state of health (SOH) of the lithium-ion battery cell 100. This includes analyzing derivatives of an OCV-SOC relationship of a battery cell during an extended discharge event, wherein OCV is the open circuit voltage and SOC is one of state of charge and a state of discharge of the battery cell. The differential curve technique includes determining one of a potential-derivative, i.e., dV/dQ vs. Q and an associated differential voltage curve, and a charge-capacity-derivative, i.e., dQ/dV vs. V and an associated differential charge curve.); and
performing life degradation analysis on the battery based on the life degradation curve (See Fig. 2, para[0006], para[0024] – para[0026]: It is known to use differential voltage analysis, i.e., dV/dQ vs. V, to determine the source of capacity fade for lithium-ion batteries. It is known to use differential charge analysis, i.e., dQ/dV vs. Q, to determine the capacity fade for lithium-ion batteries and to quantify the composition change in materials. FIG. 2 schematically shows a flowchart depicting a process for monitoring and evaluating components that affect SOH of the battery cell 100 using a differential curve technique.).
Wang is silent as to the language of:
wherein the device comprises a power station that repeatedly switches between charging and discharging, wherein the current of the battery is irregular, wherein the device has a cluster-box-cell structure comprising cells connected in series.
Nevertheless Li’330 teaches:
wherein the device comprises a power station (See para[0004]: energy storage power stations.)
that repeatedly switches between charging and discharging (See para[0066]: The cycle performance and power performance of the battery cell will be significantly differentiated during the long-term repeated charging and discharging process.),
wherein the device has a cluster-box-cell structure comprising cells connected in series (See para[0004]: A plurality of battery cells in series/parallel to obtain battery groups, battery packs, or system electrical cabinets.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang wherein the device comprises a power station that repeatedly switches between charging and discharging, wherein the device has a cluster-box-cell structure comprising cells connected in series such as that of Li’330. Li’330 teaches, “A lithium-ion battery has advantages of small size, high energy density, high power density, multiple recycling times and long storage time” (See para[0003]). One of ordinary skill would have been motivated to modify Wang, because using a power station with cells connected in series would have helped to obtain a large capacity energy storage unit with high energy and power density, as recognized by Li’330.
Li’330 is silent as to the language of:
wherein the current of the battery is irregular.
Nevertheless Balasingam teaches:
wherein the current of the battery is irregular (See Fig. 15B and para[0050]: In a first operational mode, the battery 105 may be attached to a heavy and varying load. In other words, the load 615 may be utilizing a relatively high voltage with a dynamic or varying current draw (or a high voltage load drawing variable current).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang wherein the current of the battery is irregular such as that of Balasingam. Balasingam teaches, “determining an operational mode of a battery based on a load associated with the battery, selecting one of the equivalent circuit models based on the determined operational mode, and calculating a state of charge of charge (SOC) of the battery using the selected equivalent circuit model” (See para[0004]). One of ordinary skill would have been motivated to modify Wang, because determining that a battery has irregular current would have helped to select an associated equivalent circuit model, as recognized by Balasingam.
Regarding Claim 5. Wang teaches:
The method as in claim 1,
wherein the step of extracting the life degradation curve based on the function curve comprises:
calculating capacity differentials based on the function curve (See Fig. 2, Fig. 4, Fig. 5, para[0006], para[0023] – para[0024}, para[0031], and para[0033] – para[0035]: It is known to use differential voltage analysis, i.e., dV/dQ vs. V, to determine the source of capacity fade for lithium-ion batteries. It is known to use differential charge analysis, i.e., dQ/dV vs. Q, to determine the capacity fade for lithium-ion batteries and to quantify the composition change in materials. A differential curve technique is applied to directly monitor the state of health (SOH) of the lithium-ion battery cell 100. This includes analyzing derivatives of an OCV-SOC relationship of a battery cell during an extended discharge event, wherein OCV is the open circuit voltage and SOC is one of state of charge and a state of discharge of the battery cell. The differential curve technique includes determining one of a potential-derivative, i.e., dV/dQ vs. Q and an associated differential voltage curve, and a charge-capacity-derivative, i.e., dQ/dV vs. V and an associated differential charge curve.),
which is performed every time the state of charge changes during a complete charging and discharging process (See para[0023]: A differential curve technique is applied to directly monitor the state of health (SOH) of the lithium-ion battery cell 100. This includes analyzing derivatives of an OCV-SOC relationship of a battery cell during an extended discharge event, wherein OCV is the open circuit voltage and SOC is one of state of charge and a state of discharge of the battery cell.); and
obtaining the life degradation curve based on the capacity differentials (See Fig. 2, para[0006], para[0024] – para[0026]: It is known to use differential voltage analysis, i.e., dV/dQ vs. V, to determine the source of capacity fade for lithium-ion batteries. It is known to use differential charge analysis, i.e., dQ/dV vs. Q, to determine the capacity fade for lithium-ion batteries and to quantify the composition change in materials. FIG. 2 schematically shows a flowchart depicting a process for monitoring and evaluating components that affect SOH of the battery cell 100 using a differential curve technique.).
Regarding Claim 7. Wang teaches;
The method as in claim 1,
wherein before the step of obtaining the battery data of the device, the method further comprises:
obtaining a piece of battery data in advance, and analyzing an actual working condition of the cluster-box-cell structure of the power station based on the piece of battery data obtained in advance (See para[0007], para[0023] – para[0025], and para[0036]: A first state of health parameter of the battery cell corresponding to the comparison of the measured charge-capacity-derivative with the preferred anode charge-capacity-derivative of the anode curve is determined. And, a second state of health parameter of the battery cell corresponding to the comparison of the measured charge-capacity-derivative with the preferred cathode charge-capacity-derivative of the cathode curve is determined. The source of the cell aging may also be determined based upon the information related to the loss of cathode material, anode material and active lithium.).
Regarding Claim 8. Wang teaches:
The method as in claim 1,
wherein the step of performing life degradation analysis on the battery based on the life degradation curve comprises:
analyzing a life degradation mechanism of the battery based on variations of different peaks (See para[0024] – para[0025] and para[0043] – para[0045]: Quantitatively comparing the measured signature peaks includes comparing the total measured anode capacity for the measured signature peaks with the preferred total anode capacity, with the comparison preferably made in units of Amp-hours difference between the signature peaks.),
position shifts of the peaks (See Fig. 5, Fig. 6, para[0024] – para[0025], and para[0046]: The electrochemical behaviors of each of the cathode and the anode are deconvoluted from charge/discharge characteristics of the battery cell and quantified based upon the magnitude and voltage position shifting of peaks identified in either of the differential curves.), and
sharpness variations of the peaks of the life degradation curve (See para[0046] and para[0053]: During in-use operation of the battery cell, changes in position and size of the peaks at the anode and the cathode are monitored using the differential dV/dQ analysis. Capacity fading behaviors of the anode and the cathode and information on the active lithium loss are determined by monitoring the shifting in peak position(s), shrinking magnitude of the peaks, and shrinking in distance between the peaks.),
wherein analyzing the life degradation mechanism of the battery further comprises:
analyzing a loss of circulating lithium and a loss of a negative active material through the variations of the peaks of the life degradation curve (See para[0007], para[0023] – para[0025], and para[0036]: The source of the cell aging may also be determined based upon the information related to the loss of cathode material, anode material and active lithium.).
Regarding Claim 9. Wang teaches:
A non-transitory computer-readable storage medium (See para[0059]: memory.), storing a computer program, wherein when the computer program is executed by a processor (See para[0059]: microprocessor.), the method for analyzing battery life degradation as in claim 1 is implemented.
Regarding Claim 10. Wang teaches:
An electronic device, comprising a processor and a memory (See para[0059]: memory.), wherein the memory is configured to store a computer program, and the processor is configured to execute the computer program stored in the memory (See para[0059]: microprocessor.), so that the electronic device performs the method for analyzing battery life degradation as in claim 1.
Claim(s) 2-4 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20120105069 A1) in view of Li et al. (US 20220416330 A1), and Balasingam et al. (US 20140244225 A1) as applied to claim 1 above, and further in view of Li et la. (Li, Ming, et al. "A battery SOC estimation method based on AFFRLS-EKF." Sensors 21.17 (2021): 5698.).
Regarding Claim 2. Wang is silent as to the language of:
The method as in claim 1,
wherein the step of establishing the function curve between the state of charge and the open circuit voltage of the battery comprises:
establishing an adaptive iterative calculation model, and
estimating the open circuit voltage of the battery by using the adaptive iterative calculation model; and
obtaining the function curve between the state of charge and the open circuit voltage of the battery by polynomial fitting.
Nevertheless Li teaches:
establishing an adaptive iterative calculation model (See Fig. 1, Fig. 3, Abstract, and page 2: an adaptive forgetting factor regression least-squares–extended Kalman filter (AFFRLS–EKF) SOC estimation strategy by designing the forgetting factor of least squares algorithm to improve the accuracy of SOC estimation under the change of battery charge and discharge conditions.), and
estimating the open circuit voltage of the battery by using the adaptive iterative calculation model (See Fig. 1, Fig. 3, Abstract, and Page 3 – Page 4: According to Kirchhoff’s voltage law, the circuit terminal voltage VT is expressed as follows: VT = VOC − RsI − V1 − V2.); and
obtaining the function curve between the state of charge and the open circuit voltage of the battery by polynomial fitting (See Fig. 2, Fig. 3, Abstract, Page 2, and Page 4: The second-order Thevenin equivalent circuit model (2-order ECM) of the battery was established, and the SOC–OCV relationship was obtained. The 11th-order polynomial fitting was selected, and the functional relationship between VOC and SOC was obtained as follows:
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.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang by establishing an adaptive iterative calculation model, and estimating the open circuit voltage of the battery by using the adaptive iterative calculation model; and obtaining the function curve between the state of charge and the open circuit voltage of the battery by polynomial fitting such as that of Li. Li teaches, “An adaptive forgetting factor regression least-squares algorithm (AFF–RLS) was designed to improve the model parameter identification accuracy, and then the battery SOC is estimated by the extended Kalman filter algorithm” (See Page 2). One of ordinary skill would have been motivated to modify Wang, because using adaptive iterative calculation model would have helped to improve model parameter identification accuracy , as recognized by Li.
Regarding Claim 3. Wang teaches:
The method as in claim 2,
wherein the step of establishing the adaptive iterative calculation model, and estimating the open circuit voltage of the battery by using the adaptive iterative calculation model comprises:
establishing a model based on a first-order resistor-capacitor (RC) equivalent circuit (See Fig. 1 and para[0016]: FIG. 1 schematically shows a diagram of an equivalent circuit (or model) 200 of a lithium-ion battery cell 100.).
Wang is silent as to the language of:
establishing the adaptive iterative calculation model based on a RC equivalent circuit;
performing bilinear transformation on the adaptive iterative calculation model;
determining a to-be-estimated-parameter matrix and an input variable matrix; and
determining the open circuit voltage of the battery based on the to-be-estimated-parameter matrix and the input variable matrix.
Nevertheless Li teaches:
establishing the adaptive iterative calculation model based on a first-order RC equivalent circuit (See Fig. 1 and Page 2: The second-order Thevenin equivalent circuit model (2-order ECM) of the battery was established, and the SOC–OCV relationship was obtained.);
performing bilinear transformation on the adaptive iterative calculation model (See Page 5: To ensure the consistency of system stability before and after transformation, the function was transformed from the s domain to the z domain by the bilinear transformation method.);
determining a to-be-estimated-parameter matrix and an input variable matrix (See Page 5: Therefore, Equation (10) can be rewritten as follows in matrix form:
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Equations (10)–(13) will be used in the RLS algorithm to estimate model parameters. Then the battery model parameters can be obtained by Equation (9) after θ(k) is estimated.); and
determining the open circuit voltage of the battery based on the to-be-estimated-parameter matrix and the input variable matrix (See Page 5 – Page 6: Equation (8) can be rewritten in the difference equation form as:
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. where E(k) = VT(k) − Voc(k). Extended Kalman filter (EKF) can make the optimal estimation of the target state under the minimum variance, which is often used in SOC estimation of lithium iron phosphate batteries. For the nonlinear system, the state equation of discrete space is as follows:
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where xk represents the state of the system at time k.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang by establishing the adaptive iterative calculation model based on a RC equivalent circuit; performing bilinear transformation on the adaptive iterative calculation model; determining a to-be-estimated-parameter matrix and an input variable matrix; and determining the open circuit voltage of the battery based on the to-be-estimated-parameter matrix and the input variable matrix such as that of Li. Li teaches, “An adaptive forgetting factor regression least-squares algorithm (AFF–RLS) was designed to improve the model parameter identification accuracy, and then the battery SOC is estimated by the extended Kalman filter algorithm” (See Page 2). One of ordinary skill would have been motivated to modify Wang, because using adaptive iterative calculation model would have helped to improve model parameter identification accuracy , as recognized by Li.
Regarding Claim 4. Wang is silent as to the language of:
The method as in claim 2,
wherein the step of obtaining the function curve between the state of charge and the open circuit voltage of the battery by polynomial fitting comprises:
setting a forgetting factor, and setting an initial value of the forgetting factor, wherein the forgetting factor represents a degree to which an estimation result at a previous moment is forgotten;
adaptively adjusting the forgetting factor based on a preset condition during each iteration of the adaptive iterative calculation model;
inputting the voltage and the current of the battery and the state of charge of the battery into the adaptive iterative calculation model to obtain the open circuit voltage; and
obtaining the function curve between the state of charge and the open circuit voltage of the battery by polynomial fitting.
Nevertheless Li teaches:
setting a forgetting factor, and setting an initial value of the forgetting factor, wherein the forgetting factor represents a degree to which an estimation result at a previous moment is forgotten (See Abstract, Page 2, Page 6,: An adaptive forgetting factor regression least-squares algorithm (AFF–RLS) was designed to improve the model parameter identification accuracy, and then the battery SOC is estimated by the extended Kalman filter algorithm. Therefore, the forgetting factor λ is designed to reduce the instantaneous error of the estimated parameters and increase the stability of the system. Since λ varies within the range of 0–1, the higher its value is, the stronger the anti-interference ability of the system will be.);
adaptively adjusting the forgetting factor based on a preset condition during each iteration of the adaptive iterative calculation model (See Abstract, Page 6: Therefore, the forgetting factor λ is designed to reduce the instantaneous error of the estimated parameters and increase the stability of the system. Since λ varies within the range of 0–1, the higher its value is, the stronger the anti-interference ability of the system will be. In order to increase the robustness of the system, an adaptive weighting factor λ(k) is introduced, which is adjusted according to the status of the current charge and discharge and the magnitude of identification error:
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.);
inputting the voltage and the current of the battery and the state of charge of the battery into the adaptive iterative calculation model to obtain the open circuit voltage (See Page 3 and Fig. 3:
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.); and
obtaining the function curve between the state of charge and the open circuit voltage of the battery by polynomial fitting (See Fig. 2, Fig. 3, Abstract, Page 2, and Page 4: The second-order Thevenin equivalent circuit model (2-order ECM) of the battery was established, and the SOC–OCV relationship was obtained. The 11th-order polynomial fitting was selected, and the functional relationship between VOC and SOC was obtained as follows:
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.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang by setting a forgetting factor, and setting an initial value of the forgetting factor, wherein the forgetting factor represents a degree to which an estimation result at a previous moment is forgotten; adaptively adjusting the forgetting factor based on a preset condition during each iteration of the adaptive iterative calculation model; inputting the voltage and the current of the battery and the state of charge of the battery into the adaptive iterative calculation model to obtain the open circuit voltage; and obtaining the function curve between the state of charge and the open circuit voltage of the battery by polynomial fitting such as that of Li. Li teaches, “An adaptive forgetting factor regression least-squares algorithm (AFF–RLS) was designed to improve the model parameter identification accuracy, and then the battery SOC is estimated by the extended Kalman filter algorithm” (See Page 2). One of ordinary skill would have been motivated to modify Wang, because using adaptive iterative calculation model would have helped to improve model parameter identification accuracy , as recognized by Li.
Regarding Claim 13. Wang is silent as to the language of:
The method as in claim 4,
wherein the forgetting factor ranges from 0.9 to 1.
Nevertheless Li teaches:
wherein the forgetting factor ranges (See Page 6: Therefore, the forgetting factor λ is designed to reduce the instantaneous error of the estimated parameters and increase the stability of the system. Since λ varies within the range of 0–1, the higher its value is, the stronger the anti-interference ability of the system will be.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang wherein the forgetting factor ranges such as that of Li. Li teaches, “An adaptive forgetting factor regression least-squares algorithm (AFF–RLS) was designed to improve the model parameter identification accuracy, and then the battery SOC is estimated by the extended Kalman filter algorithm” (See Page 2). One of ordinary skill would have been motivated to modify Wang, because using adaptive iterative calculation model would have helped to improve model parameter identification accuracy , as recognized by Li.
Li discloses the claimed invention except for:
wherein the forgetting factor ranges from 0.9 to 1.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to optimize the forgetting factor range of Li to range from 0.9 to 1, since it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable ranges involves only routine skill in the art. In re Aller, 220 F.2d 454, 456, 105 USPQ 233, 235. Li teaches, “Therefore, the forgetting factor λ is designed to reduce the instantaneous error of the estimated parameters and increase the stability of the system. Since λ varies within the range of 0–1, the higher its value is, the stronger the anti-interference ability of the system will be” (See Page 6). One would have been motivated to optimize Li in order to increase the anti-interference ability of the sytem, as recognized by Li.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20120105069 A1) in view of Li et al. (US 20220416330 A1), hereinafter Li’330, and Balasingam et al. (US 20140244225 A1) as applied to claim 1 above, and further in view of Xiong et al. (CN 106980091 A).
Regarding Claim 6. Wang is silent as to the language of:
The method as in claim 1,
wherein the step of obtaining the battery data of the device comprises:
continuously obtaining the battery data of the device at a preset data sampling interval.
Nevertheless Xiong teaches:
continuously obtaining the battery data of the device at a preset data sampling interval (See Page 2 and Page 3: performing real-time vehicle data collection, using a forgetting factor based on least squares parameter matrix of online identification. L is the memory length (memory length) of automatically selecting, and Ts is the sampling interval.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang by continuously obtaining the battery data of the device at a preset data sampling interval such as that of Xiong. Xiong teaches, “real-time extracting open circuit voltage and other impedance parameters in the parameter matrix obtained from the identification “ (See Page 2). One of ordinary skill would have been motivated to modify Wang, because continuously obtaining battery data would have helped to obtain data and perform calculations in real-time, as recognized by Xiong.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20120105069 A1) in view of Li et al. (US 20220416330 A1), and Balasingam et al. (US 20140244225 A1) as applied to claim 1 above, and further in view of Barsoukov et al. (US 20040128089 A1).
Regarding Claim 11. Wang is silent as to the language of:
The method as in claim 1,
wherein the device comprises a battery management system (BMS) and the BMS comprises a built-in chip, wherein the built-in chip collects a working time, the current, the voltage, a temperature, the state of charge of the battery.
Nevertheless Barsoukov teaches:
wherein the device comprises a battery management system (BMS) and the BMS comprises a built-in chip (See Fig. 1 and para[0035]: Referring to FIG. 1, a "battery fuel gauge system" 10 includes a "battery fuel gauge" integrated circuit chip 11.),
wherein the built-in chip collects a working time (See para[0085]: That value of t represents the total run-time t_total that could be supplied to the present load by the battery at the time at which the present discharge cycle started.),
the current (See Fig. 2 and para[0040]: reading present values of battery voltage, current, and temperature produced on internal digital bus 18 of fuel gauge chip 11.),
the voltage (See Fig. 2 and para[0040]: reading present values of battery voltage, current, and temperature produced on internal digital bus 18 of fuel gauge chip 11.),
a temperature (See Fig. 1 and para[0035]: a temperature sensor circuit 14.),
the state of charge of the battery (See Fig. 1 and para[0100]: The curve shown in FIG. 6A can be used to obtain values of the battery state of charge SOC from the measured values of the open circuit battery voltage.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang wherein the device comprises a battery management system (BMS) and the BMS comprises a built-in chip, wherein the built-in chip collects a working time, the current, the voltage, a temperature, the state of charge of the battery such as that of Barsoukov. Barsoukov teaches, “It is another object of the invention to provide a circuit and method which avoid inaccuracies in determination to present battery capacity, usable battery capacity, usable energy, and/or remaining run-time caused by frequent load switching” (See para[0012]). One of ordinary skill would have been motivated to modify Wang, because using a built-in chip would have helped to avoid inaccuracies when determining battery capacity and remaining run-time, as recognized by Barsoukov.
Claim(s) 12 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20120105069 A1) in view of Li et al. (US 20220416330 A1), Balasingam et al. (US 20140244225 A1), and Xiong et al. (CN 106980091 A) as applied to claim 6 above or Wang et al. (US 20120105069 A1) in view of Li et al. (US 20220416330 A1), Balasingam et al. (US 20140244225 A1), Li et la. (Li, Ming, et al. "A battery SOC estimation method based on AFFRLS-EKF." Sensors 21.17 (2021): 5698.) as applied to claim 3 above, and further in view of Kim et al. (US 20230384387 A1).
Regarding Claim 12. Wang is silent as to the language of:
The method as in claim 6,
wherein the preset data sampling interval is 15 s.
Nevertheless Kim teaches:
wherein the preset data sampling interval is 15 s (See para[0047]: For example, the sampling time period Δt may be (e.g., may be set to) 0.1 seconds, 0.5 seconds, 2 seconds, 5 seconds, or 10 seconds. The sampling time period Δt may correspond to (e.g., may be appropriately set according to) an electrical system in which the battery 110 is used.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang wherein the preset data sampling interval is 15 s such as that of Kim. Kim teaches, “The sampling time period Δt may correspond to (e.g., may be appropriately set according to) an electrical system in which the battery 110 is used” (See para[0047]). One of ordinary skill would have been motivated to modify Wang, because using a preset data sampling interval would have helped to set a sampling interval appropriately according to the electrical system in which the battery is used, as recognized by Kim.
Regarding Claim 14. Wang is silent as to the language of:
The method as in claim 13,
wherein the forgetting factor is configured to stabilize a final estimation result, wherein when the forgetting factor is 1, it indicates that a result of a previous iteration of the adaptive iterative calculation model is completely preserved, and when the forgetting factor is 0.9, it indicates that 90% of the result of the previous iteration is preserved.
Nevertheless Kim teaches:
wherein the forgetting factor is configured to stabilize a final estimation result, wherein when the forgetting factor is 1, it indicates that a result of a previous iteration of the adaptive iterative calculation model is completely preserved, and when the forgetting factor is 0.9, it indicates that 90% of the result of the previous iteration is preserved (See para[0104]: The value of the forgetting factor λ(k) is an integer value between 0 and 1, and as the value of the forgetting factor λ(k) approaches 1, the present voltage value V.sub.t(k) and the present current value I.sub.L(k) affects the first to third parameter values α.sub.1(k), α.sub.2(k), and α.sub.3(k) for a long time, and as the value of the forgetting factor λ(k) approaches 0, the present voltage value V.sub.t(k) and the present current value I.sub.L(k) affects the first to third parameter values α.sub.1(k), α.sub.2(k), and α.sub.3(k) only for a short time.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang wherein the forgetting factor is configured to stabilize a final estimation result, wherein when the forgetting factor is 1, it indicates that a result of a previous iteration of the adaptive iterative calculation model is completely preserved, and when the forgetting factor is 0.9, it indicates that 90% of the result of the previous iteration is preserved such as that of Kim. One of ordinary skill would have been motivated to modify Wang, because using a forgetting factor would have helped to determine for how long input values affect model parameters, as recognized by Kim.
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20120105069 A1) in view of Li et al. (US 20220416330 A1), Balasingam et al. (US 20140244225 A1), and Li et la. (Li, Ming, et al. "A battery SOC estimation method based on AFFRLS-EKF." Sensors 21.17 (2021): 5698.) as applied to claim 3 above, and further in view of Gill et al. (US 20230393217 A1).
Regarding Claim 15. Wang is silent as to the language of:
The method as in claim 3,
wherein the to-be-estimated-parameter matrix, denoted as xk, and the input variable matrix, denoted as Ak, are given by:
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wherein UI,k-1 represents a terminal voltage at a moment k-1, Uocy,k represents an open circuit voltage at a moment k, ii,k represents a current at the moment k, and ii,k-1 represents a current at the moment k-1, a1, a2, and a3 are coefficients related to model parameters, which vary during parameter estimation, the moment k represents a present moment of sampling, and the moment k-1 represents a previous moment of the sampling, and xk[0] represents the first parameter of the matrix xk.
Nevertheless Gill teaches:
wherein the to-be-estimated-parameter matrix, denoted as xk, and the input variable matrix, denoted as Ak, are given by:
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wherein UI,k-1 represents a terminal voltage at a moment k-1, Uocy,k represents an open circuit voltage at a moment k, ii,k represents a current at the moment k, and ii,k-1 represents a current at the moment k-1, a1, a2, and a3 are coefficients related to model parameters, which vary during parameter estimation, the moment k represents a present moment of sampling, and the moment k-1 represents a previous moment of the sampling, and xk[0] represents the first parameter of the matrix xk (See para[0050] – para[0051]: Finally, the RLS model is of the form:
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. Where the regressor ϕ and parameters ϑ are given by
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80
520
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.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang wherein the to-be-estimated-parameter matrix, denoted as xk, and the input variable matrix, denoted as Ak, are given by:
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,
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such as that of Gill. Gill teaches, “The method and system advantageously uses sparse Gaussian process regression and is scalable, since the upper and lower bounds of all electrical parameters are derived for all cells in a pack” (See para[0005). One of ordinary skill would have been motivated to modify Wang, because using a to-be-estimated-parameter matrix and an input variable matrix would have helped to derive electrical parameters for all cells in a pack, as recognized by Gill.
Response to Arguments
Applicant's arguments filed 12/10/2015 have been fully considered but they are not persuasive.
Applicant argues that: The above limitations specify that the claim is directed to the practical application of power station battery management, and does not monopolize the alleged judicial exception.
Applicant’s arguments with respect to the rejection of the amended independent claim 1 under 35 USC 101 have been fully considered but are not persuasive. Referring to the MPEP 2106.04(a)(2), Step 2A: whether a claim is directed to a judicial exception, “Step 2A is a two-prong inquiry, in which examiners determine in Prong One whether a claim recites a judicial exception, and if so, then determine in Prong Two if the recited judicial exception is integrated into a practical application of that exception.” As described in further detail under the 35 USC 101 rejection above, the non-abstract additional elements of the independent claims are seen as either generally linking the use of the judicial exception to a particular technological environment or field of use; adding insignificant extra-solution activity to the judicial exception, i.e. necessary data gathering; or mere instructions to implement an abstract idea on a computer. Further, the non-abstract additional elements are not seen as integrating the claim as a whole into a practical application because the non- abstract additional elements are well understood, routine, and conventional activity that as shown by the recited references is widely prevalent or in common use in the relevant industry. As the amended claims 1 both recite a judicial exception and are not integrated into a practical application the 35 USC 101 rejection is maintained.
Applicant argues that: Further, the above limitations, among others, improve the maintenance of power station battery cells, especially those that repeatedly switch between charging and discharging.
Applicant’s arguments with respect to the rejection of the amended independent claims 1, 6, and 11 under 35 USC 101 have been fully considered but are not persuasive. As described in further detail under the 35 USC 101 rejection above, the non-abstract additional elements of the independent claims are seen as either generally linking the use of the judicial exception to a particular technological environment or field of use; adding insignificant extra-solution activity to the judicial exception, i.e. necessary data gathering; or mere instructions to implement an abstract idea on a computer. Further, the non-abstract additional elements are not seen as integrating the claim as a whole into a practical application because the non-abstract additional elements are well understood, routine, and conventional activity that as shown by the recited references is widely prevalent or in common use in the relevant industry. As the amended claims 1 both recite a judicial exception and are not integrated into a practical application the 35 USC 101 rejection is maintained.
Applicant argues that: Specifically, to expedite prosecution and without conceding to the Examiner's positions, Applicant has amended independent claim 1 to recite, in part, "wherein the device comprises a power station that repeatedly switches between charging and discharging, wherein the current of the battery is irregular, wherein the device has a cluster-box-cell structure comprising cells connected in series", which is not disclosed by Wang.
Applicant’s arguments with respect to claim(s) 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant argues that: None of them discloses a life degradation analysis method for battery cells used in a power station that repeatedly switches between charging and discharging.
Applicant’s arguments with respect to claim(s) 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 CARTER W FERRELL whose telephone number is (571)272-0551. The examiner can normally be reached Monday - Friday 10 am - 8 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine T. 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.
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/CARTER W FERRELL/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857