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 .
Election/Restrictions
Claims 7-13, and 19 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected subcombinations, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 05/28/2026.
Specification
The disclosure is objected to because of the following informalities:
In [0053], “we L” should be --the L-- to correct a typo.
In [0083], “a pseudo OCT” should be -- a pseudo OCV-- to correct a typo.
In [00104], “may combined with” should be --may be combined with-- to correct a grammatical error.
Appropriate correction is required.
Drawings
The drawings are objected to because many texts in FIGs. 4, 6-9, and 11-16 are too small or blurry, and hard to read. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 1, 2, 15, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by OYAMA (US 20210382114 A1).
Regarding claim 1, OYAMA teaches a method for managing battery performance (i.e., “a battery diagnosis method”; see Abstract) comprising:
measuring voltage response data for a lithium-ion battery (i.e., “lithium-ion batteries”; see [0001]) in response to a current pulse perturbation injected into the lithium-ion battery (i.e., “a voltage measuring instrument that measures a voltage response made in response to application of the single current pulse”; see [0035]); and
determining in real-time, based on the measured voltage response data, resultant degradation data representative of estimated physical degradation of the lithium-ion battery (i.e., “obtains a chronopotentiogram indicating a change in the voltage response over time and performs normalization of the chronopotentiogram… makes a battery diagnosis by using a correlation between normalized data regarding a secondary battery for extracting a battery state expressing factor, the normalized data being prepared in advance, and a battery state expressing factor”; see [0036]-[0038]).
Regarding claim 2, OYAMA teaches
generating battery diagnostic and management data based on the resultant degradation data (i.e., “makes a diagnosis regarding the state of health, the state of charge, or the temperature of the secondary battery that is a measurement target”; see [0052]).
Regarding claims 15 and 20, the claim recites the same substantive limitations as claim 1 and is rejected by applying the same teachings (see, also, “voltage measuring instrument”, “a first data processing device,” and “a second data processing device” in [0035]-0039]).
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 14 is rejected under 35 U.S.C. 103 as being unpatentable over OYAMA.
Regarding claim 14, OYAMA teaches:
wherein the current pulse perturbation comprises one or more rectangle pulses (i.e., “application of the single current pulse”; see [0035]; see [0180] and FIG. 5 showing rectangle pulse) applied to the lithium-ion battery
OYAMA does not explicitly disclose (see only the underlined):
wherein the current pulse perturbation comprises one or more rectangle pulses applied to the lithium-ion battery for a duration of up to 3 minutes.
However OYAMA teaches a pulse with width of 2 second (see [0089]), 5 second, or 10 second (see [0178]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to select a pulse of width that is short enough to effect meaningful response, such that the current pulse perturbation comprises one or more rectangle pulses applied to the lithium-ion battery for a duration of up to 3 minutes, as claimed. The rationale would be to optimize the pulse application duration to effect meaningful voltage response for the measurement.
Allowable Subject Matter
Claims 3-6 and 16-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claims 3-6 and 16-18, the closest prior art of record fails to teach the features of claim 3 (as the representative): “wherein determining the resultant degradation data comprises: determining incremental capacity (IC) behavior for the lithium-ion battery based on the voltage response data provided to at least age one trained machine learning (ML) engine implementing an IC prediction model,” in combination with the rest of the claim limitations as claimed and defined by the Applicant. Note that incremental capacity (IC) is a term of art, viewed in light of the IC curve defined in the specification (e.g., [0045]).
OYAMA teaches using machine learning (ML) to infer battery SOH, temperature, SOC, SOP, and SOL, etc. It does not teach or suggest using the ML to determine incremental capacity (IC) behavior, as claimed.
TORAI (US 20210325469 A1) teaches using a neural net with voltage V of the storage battery as the input vector and differential capacity characteristic dQ/dV as output. But the voltage vector is collected when the battery is in operation (step 810). It does not require injecting current pulse for measuring the voltage response. Thus, the voltage vector is different from the voltage response as claimed.
Li ("State Estimation in Lithium-ion Batteries Using Pulse Perturbation and Feedforward Neural Networks" Columbia University, MS Thesis, 2020) teaches predicting battery stored charge, available capacity, and peak power using a feedforward neural network (FNN) with cell voltage response to an injected current as the input. It does not use FNN or other ML engine to determine incremental capacity (IC) behavior, as claimed.
Weng et al. ("On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression" Journal of Power Sources 235 (2013) 36-44) teaches determining an IC curve through support vector regression (SVR) by using SVR to fit a reverse of charging curve (Q-V). It is different from using ML to determine IC behavior (e.g., features) from a voltage response of a current pulse, as claimed.
None of the closest prior art of record teach or suggest the above indicated features as claimed.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Li et al. ("Health and performance diagnostics in Li-ion batteries with pulse-injection-aided machine learning" Applied Energy 315 (2022) 119005) teaches using pulse perturbation voltage with FNN for inferring SoC, SoH, an SoP of a lithium-ion battery.
Han et al. ("A Comparative Study of Charging Voltage Curve Analysis and State of Health Estimation of Lithium-ion Batteries in Electric Vehicle" Automotive Innovation (2019) 2:263-275) teaches a method to fit and analyze the charging voltage curve based on a neural network, in comparison to the existing point counting method and the polynomial curve fitting method.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN C KUAN whose telephone number is (571)270-7066. The examiner can normally be reached M-F: 9:00AM-5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached at (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN C KUAN/Primary Examiner, Art Unit 2857
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