DETAILED ACTION
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 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.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/17/2024 and 03/04/2026 follow the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over DESCHAMPS et al. hereinafter DESCHAMPS (WO 2022200241 A1) in view of Dou et al. hereinafter Dou (US 20200358147 A1).
With respect to claim 1, DESCHAMPS discloses a method (Figs. 4-5a) comprising:
transmitting acoustic signals through a battery cell via one or more first transducers (Process 100 includes a first step 102 of emission of an acoustic wave, called a probe wave, into the battery, para. [0090]);
receiving response signals in response to the acoustic signals at one or more second transducers (During step 104, the part of the probe wave transmitted by the battery, called the transmitted wave, is captured by an acoustic receive, para. [0095]);
and
determining characteristics of the battery cell based on at least the response signals (The correlation model links the amplitude of the frequency component to the value of the aging indicator, para. [0100]), the score indicating an estimated number of charge-discharge cycles that the battery cell will go through prior to reaching a threshold capacity (The 200 method can be applied to any rechargeable or non-rechargeable electric battery, particularly electrochemical, for which one wishes to monitor the evolution over time of an aging indicator, para. [0103]; The process 200 further includes a step 202 to determine whether the measured value of the aging indicator satisfies at least one predetermined condition in relation to at least one predetermined threshold value, para. [0105]; estimate the number of charge-discharge cycles that can still be carried out before reaching the threshold value of remaining capacity from the evolution of the amplitude of the test wave, para. [0180]).
DESCHAMPS discloses the claimed subject matter except explicitly disclosing determining performance score for the battery cell.
Dou invention related to the area of measuring process characteristics including electrolyte distribution in a battery cell discloses performance score for the battery cell (an aggregate or reduced-dimension acoustic metric or acoustic score may be derived from the 2D spatial distributions of acoustic features, para. [0086]).
Accordingly, it would have been obvious to one of ordinary skill in the art to modify DESCHAMPS to include using the response signal to determine a performance score for the battery cell because Dou teaches deriving a reduced-dimension acoustic score from acoustic signal to evaluate battery characteristics (see Dou, para. [0086], [0088]) and applying such scoring to the response signals of DESCHAMPS would have been a predictable use of known acoustic analysis techniques. With respect to claim 2, DESCHAMPS and Dou disclose the method of claim 1, DESCHAMPS further discloses determining one or more acoustic metrics indicative of one or more physical characteristics of the battery cell using the response signals (determination of a value for said battery aging indicator as a function of: of the amplitude of said at least one representative frequency component in said transmitted wave, para. [0011]).
With respect to claim 3, DESCHAMPS and Dou disclose the method of claim 2, DESCHAMPS further discloses the cycle life performance score is determined using a trained machine learning model (in said transmitted test waves, of at least one representative frequency component whose amplitude varies according to the aging of said reference battery; construction of said correlation model as a function of said values of the aging indicator and the values of the amplitude of at least one representative frequency component, para. [0055]).
With respect to claim 4, DESCHAMPS and Dou disclose the method of claim 3, DESCHAMPS further discloses the trained machine learning model receives, as input, at least the one or more acoustic metrics and provides, as output, the cycle life performance score (at least one analysis module configured to determine a value for an aging indicator of said battery based on: of the amplitude of at least one representative frequency component in said transmitted wave, and of a correlation model, previously determined, applicable to said battery, and linking said amplitude to said aging indicator, para. [0064]).
With respect to claim 5, DESCHAMPS and Dou disclose the method of claim 3, DESCHAMPS further discloses the machine learning further receives, as input, at least one score, the at least one score being indicative of a physical quality of the battery cell after completion of at least one stage of a battery manufacturing process (The correlation model links the amplitude of the frequency component to the value of the aging indicator. This correlation model is predetermined during a preliminary step, para. [0100]).
With respect to claim 6, DESCHAMPS and Dou disclose the method of claim 5, DESCHAMPS further discloses the at least one score includes one or more of: an aging score indicative of a quality of aging of the battery cell (There are mathematical models based on electrical parameters of the battery such as the voltage across the battery terminals, the current delivered by the battery, the battery temperature, etc. These models are primarily based on a correlation between the battery's aging parameter (SoH or RUL) and the battery's storage capacity, para. [0006]).
With respect to claim 7, DESCHAMPS and Dou disclose the method of claim 3, DESCHAMPS further discloses the trained machine learning model further receives, as input, ground truth information on measured cycle life of one or more defect free battery cells corresponding to the battery cell (determination of a value for said battery aging indicator as a function of: of the amplitude of said at least one representative frequency component in said transmitted wave, and of a correlation model, previously determined, applicable to said battery, and linking said amplitude to said aging indicator, para. [0011]).
With respect to claim 8, DESCHAMPS and Dou disclose the method of claim 2, DESCHAMPS further discloses the one or more acoustic metrics include: localized material inhomogeneity; a ratio of structured material; a degree of acoustic similarity between the response signals; spread of acoustic character of the response signals; and spatial variation in material structure (the stationary nature of an acoustic signal is verified if the transmitted acoustic signal exhibits at least one periodic part of constant frequency and amplitude over time. When the transmitted signal exhibits an evolution of frequency and/or amplitude over time, the signal is considered to be in transient regime (phase generally observed at the beginning and end of the signal transmission), para. [0038]).
With respect to claim 9, DESCHAMPS and Dou disclose the method of claim 1, DESCHAMPS further discloses the acoustic signals are transmitted across a number of different locations of the battery cell (step 106 determines by how much the amplitude of at least one transmitted frequency component was reduced during the propagation of the probe wave in the battery, para. [0098]).
With respect to claim 10, DESCHAMPS and Dou disclose the method of claim 1, DESCHAMPS further discloses outputting the cycle life performance score on a graphical user interface (see Figs. 6a-7g).
With respect to claim 11, DESCHAMPS discloses a battery inspection system, comprising:
a plurality of transducers (Tx and Rx transducers 402, 404); and
a controller (analysis module 406) communicatively coupled to the plurality of transducers (402, 404), the controller being configured to:
send one or more commands to a first subset of the plurality of transducers for transmitting acoustic signals through a battery cell (Process 100 includes a first step 102 of emission of an acoustic wave, called a probe wave, into the battery, para. [0090]);
receive, from a second subset of the plurality of transducers, response signals in response to the acoustic signals transmitted through the battery cell (During step 104, the part of the probe wave transmitted by the battery, called the transmitted wave, is captured by an acoustic receive, para. [0095]); and
determine characteristics of the battery cell based on at least the response signals (The correlation model links the amplitude of the frequency component to the value of the aging indicator, para. [0100]), the score indicating an estimated number of charge-discharge cycles that the battery cell will go through prior to reaching a threshold retention capacity (The 200 method can be applied to any rechargeable or non-rechargeable electric battery, particularly electrochemical, for which one wishes to monitor the evolution over time of an aging indicator, para. [0103]; The process 200 further includes a step 202 to determine whether the measured value of the aging indicator satisfies at least one predetermined condition in relation to at least one predetermined threshold value, para. [0105]; estimate the number of charge-discharge cycles that can still be carried out before reaching the threshold value of remaining capacity from the evolution of the amplitude of the test wave, para. [0180]).
DESCHAMPS discloses the claimed subject matter except explicitly disclosing performance score for the battery cell.
Dou invention related to the area of measuring process characteristics including electrolyte distribution in a battery cell discloses performance score for the battery cell (an aggregate or reduced-dimension acoustic metric or acoustic score may be derived from the 2D spatial distributions of acoustic features, para. [0086]).
Accordingly, it would have been obvious to one of ordinary skill in the art to modify DESCHAMPS to include using the response signal to determine a performance score for the battery cell because Dou teaches deriving a reduced-dimension acoustic score from acoustic signal to evaluate battery characteristics (see Dou, para. [0086], [0088]) and applying such scoring to the response signals of DESCHAMPS would have been a predictable use of known acoustic analysis techniques.
With respect to claim 12, DESCHAMPS and Dou disclose the method of claim 12, DESCHAMPS further discloses the controller is further configured to determine one or more acoustic metrics indicative of one or more physical characteristics of the battery cell using the response signals (determination of a value for said battery aging indicator as a function of: of the amplitude of said at least one representative frequency component in said transmitted wave, para. [0011]).
With respect to claim 13, DESCHAMPS and Dou disclose the method of claim 12, DESCHAMPS further discloses the cycle life performance score is determined using a trained machine learning model (in said transmitted test waves, of at least one representative frequency component whose amplitude varies according to the aging of said reference battery; construction of said correlation model as a function of said values of the aging indicator and the values of the amplitude of at least one representative frequency component, para. [0055]).
With respect to claim 14, DESCHAMPS and Dou disclose the method of claim 13, DESCHAMPS further discloses the trained machine learning model receives, as input, at least the one or more acoustic metrics and provides, as output, the cycle life performance score (at least one analysis module configured to determine a value for an aging indicator of said battery based on: of the amplitude of at least one representative frequency component in said transmitted wave, and of a correlation model, previously determined, applicable to said battery, and linking said amplitude to said aging indicator, para. [0064]).
With respect to claim 15, DESCHAMPS and Dou disclose the method of claim 13, DESCHAMPS further discloses the machine learning further receives, as input, at least one score, the at least one score being indicative of a physical quality of the battery cell after completion of at least one stage of a battery manufacturing process (The correlation model links the amplitude of the frequency component to the value of the aging indicator. This correlation model is predetermined during a preliminary step, para. [0100]).
With respect to claim 16, DESCHAMPS and Dou disclose the method of claim 5, DESCHAMPS further discloses the at least one score includes one or more of: an aging score indicative of a quality of aging of the battery cell (There are mathematical models based on electrical parameters of the battery such as the voltage across the battery terminals, the current delivered by the battery, the battery temperature, etc. These models are primarily based on a correlation between the battery's aging parameter (SoH or RUL) and the battery's storage capacity, para. [0006]).
With respect to claim 17, DESCHAMPS and Dou disclose the method of claim 13, DESCHAMPS further discloses the trained machine learning model further receives, as input, ground truth information on measured cycle life of one or more defect free battery cells corresponding to the battery cell (determination of a value for said battery aging indicator as a function of: of the amplitude of said at least one representative frequency component in said transmitted wave, and of a correlation model, previously determined, applicable to said battery, and linking said amplitude to said aging indicator, para. [0011]).
With respect to claim 18, DESCHAMPS and Dou disclose the method of claim 12, DESCHAMPS further discloses the one or more acoustic metrics include: localized material inhomogeneity; a ratio of structured material; a degree of acoustic similarity between the response signals; spread of acoustic character of the response signals; and spatial variation in material structure (the stationary nature of an acoustic signal is verified if the transmitted acoustic signal exhibits at least one periodic part of constant frequency and amplitude over time. When the transmitted signal exhibits an evolution of frequency and/or amplitude over time, the signal is considered to be in transient regime (phase generally observed at the beginning and end of the signal transmission), para. [0038]).
With respect to claim 19, DESCHAMPS and Dou disclose the method of claim 11, DESCHAMPS further discloses the acoustic signals are transmitted across a number of different locations of the battery cell (step 106 determines by how much the amplitude of at least one transmitted frequency component was reduced during the propagation of the probe wave in the battery, para. [0098]).
With respect to claim 20, DESCHAMPS and Dou disclose the method of claim 11, DESCHAMPS further discloses the controller is further configured to output the cycle life performance score on a graphical user interface (see Figs. 6a-7g).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220397610 A1 discloses an energy storage system can comprise a stack of multiple battery modules, a plurality of ultrasound emitter transducers, a plurality of ultrasound receiving transducers, one or more excitation modules, one or more capture modules, and an ultrasound battery management system. Each ultrasound emitter transducer and each ultrasound receiving transducer can be acoustically coupled to a surface of a respective one of the battery modules. The excitation module(s) can be electrically interfaced with the plurality of ultrasound emitter transducers, and the capture module(s) can be electrically interface with the plurality of ultrasound receiving transducers. The ultrasound battery management system controller can be configured to initiate battery module ultrasound interrogation sequences.
US 20220349948 A1 discloses an energy storage device management system can include a management portion for charging/discharging an energy storage device and an ultrasound interrogation portion for passing ultrasound energy through the energy storage device during charge/discharge cycles. A memory stores a stream of capture data instances derived from ultrasound energy exiting the energy storage device and baseline ultrasound data instances corresponding with the energy storage device during normal charging/discharging thereof. A processor can compare each capture data instance with the baseline ultrasound data and detect abnormal operating states of the energy storage device. A warning system can issue a notification when abnormal operating states are detected.
US 20210175553 A1 discloses Systems and methods for acoustic signal-based analysis, include obtaining acoustic response signal data of at least a portion of a battery cell, the acoustic response signal data comprising waveforms generated by transmitting one or more acoustic excitation signals into at least the portion of the battery cell and recording response vibration signals to the one or more acoustic excitation signals. One or more metrics are determined from at least the acoustic response signal data, the one or more metrics being determined based on correlation of the one or more metrics to one or more characteristics of battery cells and a reference model is generated from the one or more metrics. A test battery can be evaluated using the reference model. Actionable insights or recommendations can be generated based on the evaluation. The reference model can also be updated based on the evaluation.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GEDEON M KIDANU whose telephone number is (571)270-0591. The examiner can normally be reached 8-4.
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/GEDEON M KIDANU/Examiner, Art Unit 2855
/KRISTINA M DEHERRERA/Supervisory Patent Examiner, Art Unit 2855 3/23/26