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 .
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) 21-26,32 and 36-37 is/are rejected under 35 U.S.C. 103 as being unpatentable over Balasingam et al. (US Patent No. 10,664,562) in view of Park et al. (US Pub. No. 2021/0143481).
Regarding claims 21-26,32 and 36-37, Balasingam teaches a method of managing battery performance, the method comprising:
obtaining, via a measurement device (110), measurements of one or more parameters related to one or more cells (105);
generating or updating, based on the measurements, a machine learning model (col. 45, ll. 65-67);
generating, using the machine learning module, cell performance prediction data for using in managing at least one cell (col. 46, ll. 1-3);
wherein each cell comprises a cathode, a separator, and an anode (implicit);
wherein the one or more parameters correspond to a plurality of different types of data; and wherein the measurements comprise one or more of: measurements of cells or cell components before formation or cycling; measurements from formation cycles for one or more cells; measurements from a number of cycles after formation for one or more cells; and measurements of characteristics of cell components prior to cell assembly (the offline data collection module 555 is configured to collect different types of data, e.g. OCV and SOC, col. 5, ll. 24-39);
wherein the one or more parameters comprises initial coulombic efficiency (col. 33, ll. 65-67) [claim 22];
wherein the one or more parameters comprise second cycle coulombic efficiency (col. 22, ll. 19-20) [claim 23];
wherein the one or more parameters comprises at least one or more characteristics of cell components or raw materials prior to assembly (data associated with the battery 105, col. 11, ll. 37-52) [claim 24];
wherein the one or more parameters comprise cell impedance values (col. 4, ll. 35-38) [claim 25];
wherein the one or more parameters comprise open-circuit voltage (col. 5, ll. 18-20) [claim 26];
wherein at least one parameter of the one or more parameters during cycling of the cell (col. 6, ll. 1-40) [claim 32];
wherein amounts of measured data are different for at least two different types of data (the offline data collection module 555 is configured to collect different types of data, e.g. OCV and SOC, col. 5, ll. 24-39) [claim 36];
wherein the machine learning model is configured to perform regression or classification based on predictions (col. 46, ll. 29-34) [claim 37].
Balasingam does not specifically disclose a silicon-dominant anode.
Park et al. discloses inorganic coating in silicon-dominant cell and further discloses a cell comprising silicon-dominant anode (Park’s element 503, fig. 5).
It would have been obvious to one having an ordinary skill in the art before the effective filing date of the claimed invention to incorporate the cell comprising silicon-dominant anode, as taught by Park, into the system of Balasingam’s battery cells, since silicon-dominant anode is a type of battery that is well-known for its high energy density due to high electrochemical capacity.
Claims 27-28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Balasingam in view of Park, and further in view of Xiao et al. (2018/0006329 A1).
Regarding claims 27-28, Balasingam, as modified by Park, does not explicitly disclose plurality of parameters comprise cell thickness and/or impedance after degassing. Xiao et al. discloses electrochemical cells that include lewis acid, Lewis base complex electrolyte additives and further discloses impedance values impedance after degassing (Par. [069]).
It would have been obvious before the effective filing date of the claimed invention to a person having skill in the art to incorporate the parameters such as impedance values and impedance after degassing, as taught by Zhu into the method of managing battery performance because those parameters are typical parameters in determining the state of health of the battery.
Claims 29-31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Balasingam in view of Park, and further in view of Hsu et al. (US 2012/0215337).
Regarding claims 29-30, Balasingam, as modified by Park, does not disclose measuring at least one parameter of the plurality of parameters before or during a formation process. Hsu discloses two-dimensional multi-process products multi-tools advanced process control and further discloses measuring subset of plurality of parameters before formation process (Hsu's para. [062-064]) and during formation process (Step 25, fig. 1). It would have been obvious before the effective filing date of the claimed invention to a person having skill in the art to incorporate the steps of measuring subset of plurality of parameters, as taught by Hsu into the method of managing battery performance since the subset of plurality of parameters during and after the formation process since those parameters are typical parameters in determining the state of health of the battery.
Regarding claim 31, Balasingam, as modified by Park and Hsu, teaches all the claimed limitations except for the at least one parameter comprises a voltage reached during a first 10% of the formation cycle, however it would have been an obvious matter of design choice to incorporate date of voltage reached during a first 10% of the formation cycle as taught, since such percentage of initial capacity of the battery is typical data to estimate the battery’s health and facilitate training the machine learning model.
Claim 33 is/are rejected under 35 U.S.C. 103 as being unpatentable over Balasingam in view of Park et al. (US 2021/0143481 A1) and further in view of Moon (US 2019/0157668 A1).
Regarding claim 33, Balasingam, as modified by Park, does not explicitly disclose the cycle life is defined as a number of cycles to reach 60-80% of initial capacity. Moon discloses method for preparing silicon nanocomposite dispersion using plasma and anode active material and lithium secondary battery and further discloses number of cycles to reach 60-80% of initial capacity (Moon's fig. 8). It would have been obvious to one having an ordinary skill in the art before the effective filing date of the claimed invention to incorporate Moon’s method of defining the cycle life into said method of managing battery performance because such percentage of initial capacity of the battery is typical to determine the state of health.
Claims 34-35 is/are rejected under 35 U.S.C. 103 as being unpatentable over Balasingam in view of Park, and further in view of Baek et al. (US Pub. No. 2013/0268466).
Regarding claim 34-35, Balasingam, as modified by Park, does not specifically teach training the machine learning model using one or more machine learning (ML) algorithm, and wherein at least a portion of the measurement is used during the training, wherein the one or more machine learning algorithms comprises long-short term neural networks. Baek teaches a method of managing battery performance comprises training the machine learning model using one or more machine learning (ML) algorithm, and wherein at least a portion of the measurement is used during the training, wherein the one or more machine learning algorithms comprises long-short term neural networks (para. 117-119). It would have been obvious to one having an ordinary skill in the art before the effective filing date of the claimed invention to incorporate the step of training the machine learning model as taught by Baek into said method of managing a battery performance in order to facilitate an accurate estimation of the battery’s state of health (SOH).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 21-36 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-14 of U.S. Patent No. 12,140,641 (Patent ‘641). Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-14 of Patent ‘641 anticipate claims 21-36 of the current application.
Claims of Current Application
Claims of US Patent No. 12,140,641
21. A method of managing battery performance, the method comprising: obtaining, via a measurement device, measurements of one or more parameters relating to one or more cells; generating or updating, based on the measurements, a machine learning model; and generating, using the machine learning model, cell performance prediction data for use in managing at least one cell; wherein each cell comprises a cathode, a separator, and a silicon-dominant anode; wherein the measurements of the one or more parameters correspond to a plurality of different types of data; and wherein the measurements comprise one or more of: measurements of cells or cell components before formation or cycling; measurements from formation cycles for one or more cells; measurements from a number of cycles after formation for one or more cells; and measurements of characteristics of cell components prior to cell assembly.
22. The method of claim 21, wherein the one or more parameters comprise initial coulombic efficiency.
23. The method of claim 21, wherein the one or more parameters comprise second cycle coulombic efficiency.
24. The method of claim 21, wherein the one or more parameters comprise at least one parameter related to one or more characteristics of cell components or raw materials prior to assembly.
25. The method of claim 21, wherein the one or more parameters comprise cell impedance values.
26. The method of claim 21, wherein the one or more parameters comprise open-circuit voltage.
27. The method of claim 21, wherein the one or more parameters comprise cell thickness.
28. The method of claim 21, wherein the one or more parameters comprise impedance after degassing.
29. The method of claim 21, further comprising measuring at least one parameter of the one or more parameters before a formation process.
30. The method of claim 21, further comprising measuring at least one parameter of the one or more parameters during a formation process.
31. The method of claim 30, wherein the at least one parameter comprises a voltage reached during a first 10% of a first formation cycle.
32. The method of claim 21, further comprising measuring at least one parameter of the one or more parameters during cycling of the cell.
33. The method of claim 21, wherein the cycle life is defined as a number of cycles to reach 60-80% of initial capacity.
34. The method of claim 21, further comprising training the machine learning model using one or more machine learning (ML) algorithms, and wherein at least a portion of the measurements is used during the training.
35. The method of claim 34, wherein the one or more machine learning (ML) algorithms comprise at least one of logistic regression, lasso regression, AdaBoost regression, AdaBoost classification, XGBoost regression, XGBoost classification, random forest regression, random forest classification, multi-layer perception, long-short-term-memory neural networks, and Bayesian networks.
36. The method of claim 21, wherein amounts of measured data are different for at least two different types of data.
1. A method of managing battery performance, the method comprising: providing a cell comprising a cathode, a separator, and a silicon-dominant anode; obtaining, via a measurement apparatus, measurements of one or more parameters relating to the cell, wherein at least a portion of the measurements of at least one parameter is obtained before formation or cycling of the cell; and managing the cell based on the one or more parameters, wherein the managing comprises determining cycle life of the cell based on the one or more parameters using a machine learning model; wherein the measurements of the one or more parameters correspond to a plurality of different types of data, and wherein amounts of measured data are different for at least two different types of data.
2. The method of claim 1, wherein the one or more parameters comprise initial coulombic efficiency.
3. The method of claim 1, wherein the one or more parameters comprise second cycle coulombic efficiency.
4. The method of claim 1, wherein the one or more parameters comprise at least one parameter related to one or more characteristics of cell components or raw materials prior to assembly.
5. The method of claim 1, wherein the one or more parameters comprise cell impedance values.
6. The method of claim 1, wherein the one or more parameters comprise open-circuit voltage.
7. The method of claim 1, wherein the one or more parameters comprise cell thickness.
8. The method of claim 1, wherein the one or more parameters comprise impedance after degassing.
9. The method of claim 1, further comprising measuring at least one parameter of the one or more parameters before a formation process.
10. The method of claim 1, further comprising measuring at least one parameter of the one or more parameters during a formation process.
11. The method of claim 10, wherein the at least one parameter comprises a voltage reached during a first 10% of a first formation cycle.
12. The method of claim 1, further comprising measuring at least one parameter of the one or more parameters during cycling of the cell.
13. The method of claim 1, wherein the cycle life is defined as a number of cycles to reach 60-80% of initial capacity.
14. The method of claim 1, wherein the machine learning model utilizes one or more of the following: logistic regression, lasso regression, AdaBoost regression, AdaBoost classification, XGBoost regression, XGBoost classification, random forest regression, random forest classification, multi-layer perception, long-short-term-memory neural networks, and Bayesian networks.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MINH Q PHAN whose telephone number is (571)270-3898. The examiner can normally be reached Mon-Fri 9am-5pm.
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MINH Q. PHAN
Primary Examiner
Art Unit 2852
/MINH Q PHAN/Primary Examiner, Art Unit 2852
