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 .
Preliminary Amendment
The preliminary amendment to the list of claims as filed on April 12, 2024 is accepted by the Examiner. Claims 1-17 are now pending in the application.
IDS
The information disclosure statement (IDS) submitted on April 12, 2024, October 2, 2024, December 3, 2024 and June 25, 2026 are being considered by the Examiner.
Drawing
The drawing filed on April 12, 2024 is accepted by the Examiner.
Specification
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim rejection – 35 U.S.C. §112
Claims 1-17 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite:
In reference to claim 1: the variable N needs to be defined as it indicated as first to Nth cell; for instance, “N” as an integer. Further, on page 3, last three lines of the instant claim, “any cell” and “other cells” renders the claim indefinite because the claim includes elements not actually disclosed (those encompassed by "any cell" and “other cells”), thereby rendering the scope of the claim unascertainable. See MPEP § 2173.05(d).
With regard to claim 2: the instant claim has similar issue as claim 1 and further, the claim states “the standardized value of the error” is the ratio of “error” minus “the first average” to “the first standard deviation” in which seem to have dimensionally incorrect expression.
With regard to claim 3: the variable “M” needs to be defined, such as if it an integer or any designation appropriate for the application.
With regard to claim 4: in additional to similar issues as claims 1 and 3, the designation of “I” or “ii” is unnecessary.
With regard to claim 5: the variables “p” and “n” need to be defined; and include similar issues as claim 1 of the instant application.
With regard to claims 6-9: they depend on their respective base claims and include similar issues.
In reference to claim 10: the instant claim is analogous to claim 1 of the apparatus, and includes similar issues.
With regard to claims 11-17: the instant claims are analogous to claims 2-8; and include similar issues.
Claims 1 and 10 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
Claims 2-9 and 11-17 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Art of Interest
In reference to claims 1-17: Schuler et al. (U.S. Patent No. 9,608,544, hereon Schuler) discloses an energy supply system having an energy storage device. The energy storage device has a plurality of energy storage modules that are serially connected in at least one energy supply branch, each comprising an energy storage cell module having at least one energy storage cell, and a coupling device having coupling elements configured to selectively switch the energy storage cell module into the respective energy supply branch, or to circumvent same, and comprising a plurality of driver devices, each dedicated to one of the energy storage modules, and coupled to one of the energy storage modules, and which are configured to actuate the coupling elements of the coupling devices according to a driver signal. The energy supply system further comprises a control device, which is coupled to the driver devices by way of a control signal line, and which is configured to emit a control signal to the driver devices by way of the control signal line, which is provided with a physical control signal parameter, into the value of which a control mode for the driver devices is coded. The driver devices are configured to receive the control signal, and to generate the respective driver signal subject to the value of the control signal parameter, and a control mode assignment that can be predetermined (see Schuler, Abstract).
The instant application differs in that “acquire first cell voltage time-series data through the voltage sensor in a former part of the charging range; determine predicted cell voltage time-series data in a latter part of the charging range by applying a deep learning model to the first cell voltage time-series data; acquire second cell voltage time-series data through the voltage sensor in the latter part of the charging range; and determine an error between the second cell voltage time-series data and the predicted cell voltage time-series data;; and determine that any cell having in which the determined error is larger than corresponding errors of other cells in at least one of the plurality of charging ranges is one of the latent defective cells”, in combination with the rest of the claim limitations as claimed and defined by the Applicants.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Severson et al. (U.S. PAP 2019/0113577) discloses a method of using data-driven predictive modeling to predict and classify battery cells by lifetime is provided that includes collecting a training dataset by cycling battery cells between a voltage V1 and a voltage V2, continuously measuring battery cell voltage, current, can temperature, and internal resistance during cycling, generating a discharge voltage curve for each cell that is dependent on a discharge capacity for a given cycle, calculating, using data from the discharge voltage curve, a cycle-to-cycle evolution of cell charge to output a cell voltage versus charge curve Q(V), generating transformations of ΔQ(V), generating transformations of data streams that include capacity, temperature and internal resistance, applying a machine learning model to determine a combination of a subset of the transformations to predict cell operation characteristics, and applying the machine learning model to output the predicted battery operation characteristics.
Hess (U.S. PAP 2010/0198536) discloses a battery state of health estimator and similar method, system and computer product is disclosed providing for an estimate of a state of health (SOH) of one or more batteries, comprising, estimating a sampling of internal resistances of the one or more batteries, generating a time history of the internal resistance over a predetermined amount of time, generating a cumulative internal resistance histogram from the time history, calculated a final estimate of internal resistance of said one or more batteries which represents the calculated SOH of said one or more batteries and comparing the calculated SOH to a predetermined critical resistance threshold.
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/ELIAS DESTA/
Primary Examiner, Art Unit 2857
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