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 .
Status of Claims
Claims 1-12 are currently pending and have been examined.
Claims 1-12 have been rejected.
Priority
The instant application does not claim the benefit of priority under 35 U.S.C 119(e) or under 35 U.S.C. § 120, 121, or 365(c) to any prior applications. Accordingly, the effective filing date for the instant application is 2/19/2024.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f), is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: an input unit and an equivalent circuit generation unit in claim 7, a first model generation unit in claim 8, a diagnosis unit in claim 10, and a second model generation unit in claim 11. The specification provides that each of these units are software modules implemented on a generic computer device with corresponding hardware (see the disclosure in ¶ 0050).
Because these claim limitations are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f).
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 1-12 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1-12 are drawn to a method or device, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a method for battery diagnosis. Independent claim 7 recites an apparatus for battery diagnosis.
These independent claims recite the following steps best characterized as a mental process under MPEP § 2106.04(a)(2)(III) citing the abstract idea grouping for mental processes in general.
Independent claim 1:
training a first model by using training data in which at least one frequency band and a characteristic impedance component of a learning battery mapped to each frequency band are labeled as a basic equivalent circuit of the learning battery
receiving a target impedance component measured by applying the at least one frequency band to a target battery; and
generating a predicted equivalent circuit of the target battery to be used for battery diagnosis by inputting the target impedance component to the first model.
Independent claim 7:
a first model configured to output a predicted equivalent circuit in response to receiving at least one frequency band and a battery impedance component of each frequency band;
receive a target impedance component measured by applying the at least one frequency band to a target battery; and
generate a predicted equivalent circuit of the target battery to be used for battery diagnosis by inputting the target impedance component to the first model.
Under the broadest reasonable interpretation of the limitations of training a model at a broad level of generality, reading measurement data, and generating a prediction for battery diagnosis, these limitations are best characterized as applying a mental process to a generic computing environment - see MPEP § 2106.04(a)(2)(III)(c)(2). Examiner notes that, in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claims also recite both a mental process and a mathematical concept consistent with Example 47 claim 2.
Dependent claim 2 recites, in part, wherein the at least one frequency band comprises a frequency corresponding to a maximum point area or a minimum point area of a semicircular shape, or an ohmic resistance area in a curve of an impedance distribution measured by applying frequencies of an entire range to the learning battery.
Dependent claim 3 recites, in part, generating the training data, wherein the generating of the training data comprises: receiving an impedance distribution of the learning battery for frequencies of an entire range; generating a basic equivalent circuit of the learning battery from the impedance distribution; identifying a characteristic impedance component and a characteristic frequency component corresponding to at least one predefined characteristic impedance area in the impedance distribution; and labeling the characteristic impedance component and the characteristic frequency component as the basic equivalent circuit.
Dependent claim 4 recites, in part, determining a state of the target battery by analyzing the predicted equivalent circuit.
Dependent claim 5 recites, in part, wherein the determining of the state of the target battery comprises: training a second model by using training data in which physical characteristic information identified by analyzing the basic equivalent circuit is labeled as state information of the learning battery; and determining a state of the target battery by inputting physical characteristic information identified by analyzing the predicted equivalent circuit to the second model.
Dependent claim 6 recites, in part, wherein the physical characteristic information comprises battery impedance information, and the state information comprises whether there is a battery failure and/or a cause of the failure.
Dependent claim 8 recites, in part, train the first model by using training data in which a characteristic impedance component and a characteristic frequency component of a learning battery are labeled as a basic equivalent circuit of the learning battery.
Dependent claim 9 recites, in part, receive an impedance distribution of a learning battery for frequencies of an entire range, generate a basic equivalent circuit of the learning battery from the impedance distribution, identify a characteristic frequency component corresponding to at least one predefined characteristic impedance area in the impedance distribution, and generate training data in which the characteristic impedance component and the characteristic frequency component are labeled as the basic equivalent circuit.
Dependent claim 10 recites, in part, a second model configured to output a battery state when receiving physical characteristic information; and a second model configured to output a battery state when receiving physical characteristic information; and determine a battery state of the target battery by inputting physical characteristic information identified by analyzing the predicted equivalent circuit to the second model.
Dependent claim 11 recites, in part, train the second model by using training data in which physical characteristic information identified by analyzing the basic equivalent circuit is labeled as a battery state of the learning battery.
Each of these steps of the preceding dependent claims only serve to further limit or specify the features of independent claims 1 or 7 accordingly, and hence are nonetheless directed towards fundamentally the same mental process abstract idea grouping as the independent claim and utilize the additional elements analyzed below in the expected manner.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claim 7 recites an input unit and an equivalent circuit generation unit. Claim 8 recites a first model generation unit. Claim 10 recites a diagnosis unit. Claim 11 recites a second model generation unit. The specification provides that each of these units are software modules implemented on a generic computer device with corresponding hardware (see the disclosure in ¶ 0050). Claim 12 recites a non-transitory computer-readable recording medium. The specification provides a generic configuration of the computer-readable medium and any storage device for storing data (see the disclosure in ¶ 0056). The use of the modules and non-transitory computer-readable recording medium, in this case to performing the battery diagnosis method of claim 1, only recites computer and corresponding hardware as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claim 7 recites an input unit and an equivalent circuit generation unit. Claim 8 recites a first model generation unit. Claim 10 recites a diagnosis unit. Claim 11 recites a second model generation unit. Claim 12 recites a non-transitory computer-readable recording medium. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (MPEP § 2106.07(a)(III)(A) integrating the evidentiary requirements in making a § 101 rejection as established in Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1-12 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Santoni et al., A guide to equivalent circuit fitting for impedance analysis and battery state estimation, 82 J of Energy Storage (Jan. 17, 2024)[hereinafter Santoni] in view of Doonyapisut et al., Analysis of Electrochemical Impedance Data: Use of Deep Neural Networks, 5 Advanced Intelligent Systems (2023) [hereinafter Doonyapisut].
As per claim 1, Santoni teaches on the following limitations of the claim:
a battery diagnosis method comprising: is taught in the Abstract on p. 1 (teaching on a method for analyzing electrochemical impedance spectra of lithium batteries using equivalent circuit models)
training a first model ... in which at least one frequency band and a characteristic impedance component of a learning battery mapped to each frequency band are labeled as a basic equivalent circuit of the learning battery is taught in the § 3. Fitting an equivalent circuit model on p. 4 and § 3.2. Ordering parameters on p. 5-6 (teaching on optimizing a first equivalent circuit model for impedance analysis within particular frequency ranges)
receiving a target impedance component measured by applying the at least one frequency band to a target battery; and is taught in the § 3.4. ECM fitting example on p. 7-8 (teaching on applying new impedance curves of a battery to the first equivalent circuit model to identify the circuit model)
generating a predicted equivalent circuit of the target battery to be used for battery diagnosis by inputting the target impedance component to the first model is taught in the § 5. Battery state estimation on p. 12 and 5.3. Exploratory data analysis on p. 14 (teaching on determining a battery state estimation utilizing a second machine learning model applying the equivalent circuit model of the battery)
Santoni fails to teach the following limitation of claim 1. Doonyapisut, however, does teach the following:
training a first model by using training data is taught in the 2.4. Training and Evaluation of the EIS Model Classification on p. 5 (teaching on the equivalent circuit model being a neural network trained on a labeled dataset)
One of ordinary skill in the art at the effective filing date would replace the first equivalent circuit model of Santoni with the neural network trained model of Doonyapisut with the motivation of “evaluat[ing] the classification and prediction of EIS parameters in the circuit models under the account and provide interpretation results automatically” (Doonyapisut in the 1. Introduction on p. 2 col 2).
Independent claim 7 and dependent claim 8 are rejected under a similar rational.
As per claim 2, the combination of Santoni and Doonyapisut discloses all of the limitations of claim 1. Santoni also discloses the following:
the battery diagnosis method of claim 1, wherein the at least one frequency band comprises a frequency corresponding to a maximum point area or a minimum point area of a semicircular shape, or an ohmic resistance area in a curve of an impedance distribution measured by applying frequencies of an entire range to the learning battery is taught in the § 3.3. Initial values on p. 6-7 and fig. 8 on p. 7 (teaching on the frequency band points being assigned such that the lowest observed frequency up to the local minimum in the imaginary part before the rightmost Randles arc)
As per claim 3, the combination of Santoni and Doonyapisut discloses all of the limitations of claim 1. Santoni also discloses the following:
the battery diagnosis method of claim 1, further comprising generating the training data, wherein the generating of the training data comprises: receiving an impedance distribution of the learning battery for frequencies of an entire range; generating a basic equivalent circuit of the learning battery from the impedance distribution; identifying a characteristic impedance component and a characteristic frequency component corresponding to at least one predefined characteristic impedance area in the impedance distribution; and labeling the characteristic impedance component and the characteristic frequency component as the basic equivalent circuit is taught in the § 3. Fitting an equivalent circuit model on p. 4 and § 3.2. Ordering parameters on p. 5-6 (teaching on optimizing (treated as synonymous to training) the first equivalent circuit model for impedance analysis within particular frequency ranges based on a mathematical correlation between the labeled impedance distribution data in a range from high to low frequencies)
Dependent claim 9 is rejected under a similar rational.
As per claim 4, the combination of Santoni and Doonyapisut discloses all of the limitations of claim 1. Santoni also discloses the following:
the battery diagnosis method of claim 1, further comprising determining a state of the target battery by analyzing the predicted equivalent circuit is taught in the § 5. Battery state estimation on p. 12 and 5.3. Exploratory data analysis on p. 14 (teaching on determining a battery state estimation utilizing a second machine learning model applying the equivalent circuit model of the battery)
As per claim 5, the combination of Santoni and Doonyapisut discloses all of the limitations of claim 4. Santoni also discloses the following:
the battery diagnosis method of claim 4, wherein the determining of the state of the target battery comprises: training a second model by using training data in which physical characteristic information identified by analyzing the basic equivalent circuit is labeled as state information of the learning battery; and determining a state of the target battery by inputting physical characteristic information identified by analyzing the predicted equivalent circuit to the second model is taught in the § 5. Battery state estimation on p. 12, 5.3. Exploratory data analysis on p. 14, and § 5.4. Predicting SOC and SOH by Gaussian process regression (teaching on determining a battery state estimation by training and utilizing a second trained machine learning model applying the equivalent circuit model of the battery and corresponding battery operational and circuit parameters)
Dependent claim 10 is rejected under a similar rational.
As per claim 6, the combination of Santoni and Doonyapisut discloses all of the limitations of claim 5. Santoni also discloses the following:
the battery diagnosis method of claim 5, wherein the physical characteristic information comprises battery impedance information, and the state information comprises whether there is a battery failure and/or a cause of the failure is taught in the § 5. Battery state estimation on p. 12, 5.3. Exploratory data analysis on p. 14, and § 5.4. Predicting SOC and SOH by Gaussian process regression (teaching on the battery state estimation including the state of charge (treated as synonymous to impedance information) and state of health (treated as synonymous to battery failure) measures)
Dependent claim 11 is rejected under a similar rational.
As per claim 12, the combination of Santoni and Doonyapisut discloses all of the limitations of claim 1. Santoni also discloses the following:
A non-transitory computer-readable recording medium having recorded thereon a computer program for performing the battery diagnosis method of claim 1 is taught in the § I. Introduction on p. 1-2 (teaching on applying the learning method on a machine for battery diagnostics)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Olufemi Issac Olayiwola et al., Photovoltaic Cell/Module Equivalent Electric Circuit Modeling Using Impedance Spectroscopy, 56(2) IEEE Transactions on Industry Applications 1690-1701 (2020) teaching on dynamic alternating current equivalent electric circuit (AC-EEC) modeling utilizing impedance changes under certain frequency ranges in the § A. IS Results for Cell Characterization on p. 1695-1697
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN LYNN JACKSON whose telephone number is (571)272-5389. The examiner can normally be reached Monday-Friday 8:30AM-4:30PM ET.
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, Arleen M Vazquez can be reached at 571-272-2619. 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.
/JORDAN L JACKSON/Primary Examiner, Art Unit 2857
Prosecution Insights