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 .
This office action is in response to communication filed on 6/17/2026. Claims 5 and 14 have been canceled. Claims 1-4, 6-13, and 15-20 are pending on this application.
Response to Arguments
Applicant's arguments filed 6/17/2026 have been fully considered but they are not persuasive.
Response to Remarks
Regarding claim 1, applicant asserts that Yonaga fails to teach a computing system configured to receive comparative dispersion images of a second dry electrode mixture (Remarks page 12).
Examiner respectfully disagrees.
Yonaga teaches analyzing dispersibility of a CBA domain using SEM-EDX images (section 2.2). The output of the analysis, using a range of 500x300 pixels in two SEM-EDX (section 2.2), is interpreted to be the comparative dispersion images since the dispersibility is evaluated. In section 1, Yonaga teaches that the morphology of electrode powder mixture changes with different conditions of the dry powder mixing. This is interpreted to mean that there is a first dry mixture and at least one second dry powder mixture with different conditions. Therefore it would be obvious to apply the analyzing step to the second dry powder mixture and Yonaga thus teaches the limitation of claim 1.
Duquesnoy was relied upon to teach calculating a target mixing condition for the second dry electrode mixture based on machine-learned data of the dispersion images (see page 5 of the previous Office Action). Duquesnoy teaches predicting process parameters (Conclusions section), which are interpreted to be mixing conditions, for a dry electrode mixture (Appendix A.1) using a machine learning model to identify an optimal electrode and the manufacturing parameters (abstract). Therefore the combination of Yonaga and Duquesnoy teach determining mixing conditions.
In response to applicant's argument, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
Regarding claim 13, applicant asserts that Yonaga fails to teach wherein the target mixing condition includes a mixing time or a mixing speed (Remarks page 13).
Examiner respectfully disagrees.
Yonaga teaches that the rotation speed of the mixer is set to 1,000, 3,000, and 10,000 rpm (section 2.1) This is interpreted to be a target mixing condition of the mixer. Therefore Yonaga teaches the limitation as claimed.
Claim Rejections - 35 USC § 103
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 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) 1-3, 5, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yonaga et al. ("Effects of dry powder mixing on electrochemical performance of lithium-ion battery electrode using solvent-free dry forming process." Journal of Power Sources 581 (10/15/2023): 233466, pages 1-10, retrieved from the Internet on 3/9/2026) in view of Müller et al. ("Deep learning-based segmentation of lithium-ion battery microstructures enhanced by artificially generated electrodes." Nature communications 12.1 (10/27/2021): 6205, pages 1-12, retrieved from the Internet on 3/9/2026) and Duquesnoy et al. ("Machine learning-assisted multi-objective optimization of battery manufacturing from synthetic data generated by physics-based simulations." Energy Storage Materials 56 (2/2023): pages 50-61, retrieved from the Internet on 3/9/2026).
Regarding claim 1, Yonaga teaches a system for calculating a mixing condition for a dry electrode (section 1, We investigate how the morphology of electrode powder mixture changes with different conditions of dry powder mixing, and also how the morphology of electrode powder mixture affects the electrode structure and electrochemical performance of the batteries fabricated by a dry electrode forming process) comprising:
a microscope (section 2.2, scanning electron microscopy) configured to measure dispersion images of a first dry electrode mixture (section 2.2, In addition, compositional analysis was carried out using the energy dispersive X-ray (EDX) spectrometer (QUANTAX FlatQUAD, Bruker AXS GmbH, Germany) to obtain element mapping images) for each mixing condition (section 2.1, The rotation speed of the mixer was set to 1,000, 3,000, and 10,000 rpm), wherein an electrode active material (section 2.1, LiNi1/Co1/Mn1/3O2), a conductive material (section 2.1, acetylene black) and a binder in the first dry electrode mixture (section 2.1, polyvinylidene difluoride) are mixed by a mixer (section 1, LiB electrodes are conventionally manufactured by coating an electrode slurry consisting of active materials (AMs), conductive and binding additives (CBA), and solvent onto a metal foil. Dry powder mixing of the electrode powder, that is the initial step in the dry electrode forming process, can be carried out using a high-shear mixer); and
a computing system configured to receive comparative dispersion images of a second dry electrode mixture (section 1, different conditions of dry powder mixing implies at least a second dry powder mixture; section 2.2, The size distribution, dispersibility, and tortuosity of the CBA domain were analyzed using the SEM-EDX images of the electrode crosssections. It would be necessary to have an system to perform the analysis).
Yonaga fails to teach a computing system configured to machine-learn the dispersion images of the first dry electrode mixture. However Müller teaches machine-learning images of an electrode mixture (abstract, Here, we demonstrate a methodology for using deep learning tools to achieve reliable segmentations of volumetric images of electrodes on which standard segmentation approaches fail due to insufficient contrast).
Therefore taking the combined teachings of Yonaga and Müller as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Müller into the system of Yonaga. The motivation to combine Müller and Yonaga would be to enable higher-level battery model and simulation verification (page 10 right side fifth paragraph of Müller).
Yonaga also fails to teach wherein the computing system is configured to calculate a target mixing condition for the second dry electrode mixture based on machine-learned data of the dispersion images.
However Duquesnoy teaches calculate a target mixing condition for a dry electrode mixture (section 1, algorithms like the Bayesian Optimization (BO) framework supported by a probabilistic approach, constitutes powerful tools to solve optimization problems and perform inverse design; Table 2; Conclusions, Such an approach predicts the process parameters to adopt in order to manufacture the so-found optimal electrode; Appendix A.1) based on machine-learned data of images (abstract, Secondly, the generated dataset is used to train deterministic ML models to implement a fast multi-objective optimization, to identify an optimal electrode and the manufacturing parameters to adopt in order to fabricate it).
Therefore taking the combined teachings of modified Yonaga and Duquesnoy as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Duquesnoy into the system of modified Yonaga. The motivation to combine Duquesnoy and modified Yonaga would be to provide a computationally cheaper method for calculating the electrode properties
(Conclusions of Duquesnoy).
Regarding claim 2, the modified system of Yonaga teaches a system wherein the mixing condition is a mixing time or a mixing speed by the mixer (section 2.1 of Yonaga, The rotation speed of the mixer was set to 1,000, 3,000, and 10,000 rpm).
Regarding claim 3, the modified system of Yonaga teaches a system wherein: when the mixing condition is the mixing time by the mixer, linear speeds of mixers used to mix the first dry electrode mixture and the second dry electrode mixture are set to be the same; or
when the mixing condition is the mixing speed by the mixer, operating times of the mixers used to mix the first dry electrode mixture and the second dry electrode mixture are set to be the same (section 2.1 of Yonaga, The rotation speed of the mixer was set to 1,000, 3,000, and 10,000 rpm (equivalent to a peripheral velocity of 0.628, 1.88, and 6.28 m/s), and the processing time was set to between 1 and 30 min at each rotation speed; section 3.2 of Yonaga, The electrodes were fabricated from the electrode powder mixture dry mixed for 30 min at the three different rotation speeds).
Regarding claim 5, the modified system of Yonaga teaches a system wherein:
components and composition ratios of the first dry electrode mixture and the second dry electrode mixture are the same, but amounts of the first dry electrode mixture and the second dry electrode mixture are different; or
the components and the composition ratios of the first dry electrode mixture and the second dry electrode mixture are the same (section 2.1 of Yonaga), but systems for respectively mixing the first dry electrode mixture and the second dry electrode mixture are different (section 1 of Yonaga, Dry powder mixing of the electrode powder, that is the initial step in the dry electrode forming process, can be carried out using a high-shear mixer, a beads mill, or a planetary mixer. It would be obvious to select any desired mixer).
Regarding claim 19, the claim recite similar subject matter as claim 5 and is rejected for the same reasons as stated above.
Regarding claim 20, the claim recite similar subject matter as claims 1-2 and is rejected for the same reasons as stated above.
Claim(s) 4 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yonaga et al. ("Effects of dry powder mixing on electrochemical performance of lithium-ion battery electrode using solvent-free dry forming process." Journal of Power Sources 581 (10/15/2023): 233466, pages 1-10, retrieved from the Internet on 3/9/2026), Müller et al. ("Deep learning-based segmentation of lithium-ion battery microstructures enhanced by artificially generated electrodes." Nature communications 12.1 (10/27/2021): 6205, pages 1-12, retrieved from the Internet on 3/9/2026) and Duquesnoy et al. ("Machine learning-assisted multi-objective optimization of battery manufacturing from synthetic data generated by physics-based simulations." Energy Storage Materials 56 (2/2023): pages 50-61, retrieved from the Internet on 3/9/2026) in view of Fujiwara et al (US20230378471).
Regarding claim 4, the modified system of Yonaga fails to teach a system wherein the target mixing condition is a mixing time or a mixing speed by the mixer when a binder included in the second dry electrode mixture satisfies predetermined fibrillization conditions.
However Fujiwara teaches wherein the target mixing condition is a mixing time or a mixing speed by the mixer (para. [0170]) when a binder included in a dry electrode mixture satisfies predetermined fibrillization conditions (para. [0140]-[0141]).
Therefore taking the modified Yonaga with Fujiwara as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Fujiwara into the system of modified Yonaga. The motivation to combine Fujiwara and modified Yonaga would be to reduce an electrical resistance value and achieving strength of an electrode (para. [0033] of Fujiwara).
Regarding claim 18, the claim recite similar subject matter as claim 4 and is rejected for the same reasons as stated above.
Claim(s) 8 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yonaga et al. ("Effects of dry powder mixing on electrochemical performance of lithium-ion battery electrode using solvent-free dry forming process." Journal of Power Sources 581 (10/15/2023): 233466, pages 1-10, retrieved from the Internet on 3/9/2026), Müller et al. ("Deep learning-based segmentation of lithium-ion battery microstructures enhanced by artificially generated electrodes." Nature communications 12.1 (10/27/2021): 6205, pages 1-12, retrieved from the Internet on 3/9/2026) and Duquesnoy et al. ("Machine learning-assisted multi-objective optimization of battery manufacturing from synthetic data generated by physics-based simulations." Energy Storage Materials 56 (2/2023): pages 50-61, retrieved from the Internet on 3/9/2026) in view of Hunt et al (GB2546522A).
Regarding claim 8, the modified system of Yonaga fails to teach a system further comprising an electrical conductivity measurer configured to measure electrical conductivities of the first dry electrode mixture under the respective mixing conditions,
wherein the computing system is configured to:
further machine-learn the electrical conductivities;
further receive comparative electrical conductivities of the second dry electrode mixture; and
calculate the target mixing condition for the second dry electrode mixture based on the machine-learned data of the dispersion images and the electrical conductivities.
However Hunt teaches an electrical conductivity measurer configured to measure electrical conductivities of a dry electrode mixture under respective mixing conditions (abstract, flow over a set of electrons),
wherein the computing system is configured to:
further machine-learn the electrical conductivities (page 26, a set of preprogrammed non-linear functions; page 35, by means of a simple multi-variable regression analysis, an artificial intelligence system);
further receive comparative electrical conductivities of a second dry electrode mixture (page 29, electrically conducting); and
calculate the target mixing condition for the second dry electrode mixture based on the machine-learned data of the dispersion images and the electrical conductivities (page 35, provide a multi-parameter fit…by means of a simple multi-variable regression analysis, an artificial intelligence system).
Therefore taking the modified Yonaga with Hunt as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Hunt into the system of modified Yonaga. The motivation to combine Hunt and modified Yonaga would be to determine the accuracy of predicted capacitances (abstract of Hunt).
Regarding claim 13, the claim recite similar subject matter as claims 1, 2, and 8 and is rejected for the same reasons as stated above.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yonaga et al. ("Effects of dry powder mixing on electrochemical performance of lithium-ion battery electrode using solvent-free dry forming process." Journal of Power Sources 581 (10/15/2023): 233466, pages 1-10, retrieved from the Internet on 3/9/2026), Müller et al. ("Deep learning-based segmentation of lithium-ion battery microstructures enhanced by artificially generated electrodes." Nature communications 12.1 (10/27/2021): 6205, pages 1-12, retrieved from the Internet on 3/9/2026), Duquesnoy et al. ("Machine learning-assisted multi-objective optimization of battery manufacturing from synthetic data generated by physics-based simulations." Energy Storage Materials 56 (2/2023): pages 50-61, retrieved from the Internet on 3/9/2026) and Hunt (GB2546522) in view of Fujiwara et al (US20230378471).
Regarding claim 10, the modified system of Yonaga fails to teach a system wherein the machine-learned data comprises a comparative mixing condition which is a mixing condition when the binder in the first dry electrode mixture satisfies predetermined fibrillization conditions.
However Fujiwara teaches a comparative mixing condition which is a mixing condition (para. [0170]) when the binder in the first dry electrode mixture satisfies predetermined fibrillization conditions (para. [0140]-[0141]).
Therefore taking the modified Yonaga with Fujiwara as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Fujiwara into the system of modified Yonaga. The motivation to combine Fujiwara and modified Yonaga would be to reduce an electrical resistance value and achieving strength of an electrode (para. [0033] of Fujiwara).
Allowable Subject Matter
Claims 6, 7, 9, 11, 12, and 15-17 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.
Conclusion
THIS ACTION IS MADE FINAL. 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 LEON VIET Q NGUYEN whose telephone number is (571)270-1185. The examiner can normally be reached Mon-Fri 11AM-7PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached at 571-272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LEON VIET Q NGUYEN/Primary Examiner, Art Unit 2663
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