CTNF 18/442,826 CTNF 72204 Detailed Action Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 2. Claims 1-16 are pending. Claim Rejections - 35 USC § 103 07-20-aia AIA 3. 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. 07-21-aia AIA 4. Claim (s) 6-10, 12-14, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shih et al “Shih” (TH 2001001165 A) and Fernando et al “Fernando” (WO 2022123905 A1) . (Please see the attached copies of Shih and Fernando that number paragraphs in the same manner as that used in the Action). 5. Regarding claim 6, Shih shows a battery manufacturing system comprising a trained model implemented on a processor of a manufacturing line that produces battery cells (para 54, 71 show a battery production system, para 69, 71 show using a trained machine learning model, para 81 shows the model and other software are implemented on a processor). Shih para 54, 71 discuss battery production quality and efficiency, Shih para 71, 76 show detecting predefined conditions such as measured gas discharge/leakage and battery self-/over-discharge, and Shih para 69, 71 shows measured data also includes charge/discharge cycling gas analysis of battery cells, but Shih does not explicitly show that the trained model is configured to cause removal of certain of the battery cells from the manufacturing line responsive to the trained model identifying the certain of the battery cells as being subject to a predefined condition based on measured data from the certain of the battery cells on the manufacturing line provided to the trained model. Fernando however does show the trained model is configured to cause removal of certain of the battery cells from the manufacturing line responsive to the trained model identifying the certain of the battery cells as being subject to a predefined condition based on measured data from the certain of the battery cells on the manufacturing line provided to the trained model (Fernando para 83, 89, 112-113 show using a trained model in a production system of batteries to remove any battery identified as subject to a defective condition, based on data from the batteries in the manufacturing process provided to the trained model, and para 109, 112 show the data is measured data such as via a sensor during the manufacturing process). It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to have this removal process as is done in Fernando, in the battery production system of Shih, because it would provide an efficient way to maintain high battery production quality. Those batteries with identified conditions would be removed. 6. Regarding claim 7, the predefined condition is an impending self-discharge of the certain of the battery cells (Shih para 76 shows the predefined condition may be a sudden self-/over-discharge of particular battery cells). 7. Regarding claim 8, the predefined condition is an impending venting of gases from the certain of the production battery cells (Shih para 71 show the predefined condition may be gas discharge from particular battery cells). 8. Regarding claim 9, the predefined condition is an impending thermal event of the certain of the production battery cells (Shih para 61, 70 shows a predefined condition may be temperature increase or effect on battery). 9. Regarding claim 10, the measured data is measured formation cycling gas data (Shih para 69, 71 shows measured data also includes charge/discharge cycling gas analysis of battery cells). 10. Regarding claim 12, the measured data is measured current leakage data (para 71 shows the measured data also includes gas emissions and gas discharge/leakage from the battery). 11. Regarding claim 13, the model is a machine learning model (Shih para 55, 59, 71 show the model is a machine learning model). 12. Regarding claim 14, Shih shows a battery manufacturing system comprising a model implemented on a processor of a manufacturing line that produces production battery cells (para 54, 71 show a battery production system, para 69, 71 show using a machine learning model, para 81 shows the model and other software are implemented on a processor), and trained on formation cycling gas analyzer data of training battery cells (Shih para 69, 71 shows the model is trained using charge/discharge cycling gas analysis data of battery cells). Shih para 54, 71 discuss battery production quality and efficiency, Shih para 71, 76 show detecting predefined conditions such as measured gas discharge/leakage and battery self-/over-discharge, and Shih para 69, 71 shows measured data also includes charge/discharge cycling gas analysis of battery cells, but Shih does not explicitly show that the trained model is configured to cause removal of certain of the production battery cells from the manufacturing line responsive to the model identifying the certain of the production battery cells as being subject to an impending venting of gases based on gas data measured during a formation cycling stage of the certain of the production battery cells. Fernando however does show the trained model is configured to cause removal of certain of the production battery cells from the manufacturing line responsive to the model identifying the certain of the production battery cells as being subject to an predefined condition based on measured data during a manufacturing process of the certain of the production battery cells (Fernando para 83, 89, 112-113 show using a trained model in a production system of batteries to remove any battery identified as subject to a defective condition, based on data from the batteries in the manufacturing process provided to the trained model, and para 109, 112 show the data is measured data such as via a sensor during the manufacturing process). It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to have this removal process as is done in Fernando, in the battery production system of Shih - in which the measured data is gas data measured during a formation cycling stage of the battery cells as explained above and the identified condition is the impending venting of gases (Shih para 71 show the predefined condition may be gas discharge from particular battery cells) - because it would provide an efficient way to maintain high battery production quality. Those batteries identified as having impending venting/discharge of gases would be removed. 13. Regarding claim 16, the model is a machine learning model (Shih para 55, 59, 71 show the model is a machine learning model) . 07-21-aia AIA 14. Claim (s) 1-5, 11, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shih et al “Shih” (TH 2001001165 A) and Xu et al “Xu” (CN 111445462 A) and Fernando et al “Fernando” (WO 2022123905 A1) . (Please see the attached copies of Shih, Xu, and Fernando that number paragraphs in the same manner as that used in the Action). 15. Regarding claim 1, Shih shows a method comprising training a model implemented on a processor on formation cycling gas analyzer data of training battery cells (para 69, 71 shows training a machine learning model on training data based on charge/discharge cycling gas analysis of battery cells, para 81 shows the model and other software are implemented on a processor), and current leakage data of the training battery cells to generate a trained model (para 71 shows the training data to generate the trained model also includes gas emissions and gas discharge/leakage from the battery). Shih does not explicitly say the training data also includes infrared thermal imaging data of the training battery cells. Xu para 77, 87, 90 however does train a model using training data which includes infrared thermal imaging data of the (training) battery cells. Xu para 79, 81 show this is used to train the model to detect gas leakage. It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to includes infrared thermal imaging data of the training battery cells as is done in Xu, in the training data of Shih, because it would provide an efficient way to train a model that uses gas analysis data of battery cells. Shih para 54, 71 discuss battery production quality and efficiency, and Xu para 77, 79 mention the battery industrial production quality, and Shih para 71, 76 show detecting predefined conditions such as gas discharge and battery self-/over-discharge, but neither Shih nor Xu explicitly that the trained model during deployment with a line configured to manufacture production battery cells causes removal of certain of the production battery cells from the line responsive to measurements associated with the production battery cells on the line being identified by the trained model as corresponding to a predefined condition. Fernando however does show the trained model during deployment with a line configured to manufacture production battery cells causes removal of certain of the production battery cells from the line responsive to measurements associated with the production battery cells on the line being identified by the trained model as corresponding to a predefined condition (Fernando para 83, 89, 113 show using a deployed trained model in a production system of batteries to remove any battery identified as corresponding to a defective condition). It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to have this removal process as is done in Fernando, in the battery production system of Shih, especially as modified by Xu, because it would provide an efficient way to maintain high battery production quality. Those batteries with identified conditions such as gas leakage would be removed. 16. Regarding claim 2, the predefined condition is an impending self-discharge of the certain of the production battery cells (Shih para 76 shows the predefined condition may be a sudden self-/over-discharge of particular battery cells). 17. Regarding claim 3, the predefined condition is an impending venting of gases from the certain of the production battery cells (Shih para 71 show the predefined condition may be gas discharge from particular battery cells). 18. Regarding claim 4, the predefined condition is an impending thermal event of the certain of the production battery cells (Shih para 61, 70 shows a predefined condition may be temperature increase or effect on battery. Xu para 77 shows a specific condition may be a temperature characteristic corresponding to gas leakage). 19. Regarding claim 5, the model is a machine learning model (Shih para 55, 59, 71 show the model is a machine learning model). 20. Regarding claim 11, in addition to that mentioned for claim 6, Shih para 69, 71 show the measured data includes formation cycling data such as for gas analysis, but Shih and Fernando do not explicitly show the measured data includes infrared thermal imaging data. Xu para 77, 87, 90 however does show using measured infrared thermal imaging data of the battery cells. Xu para 79, 81 show this is utilized to use the model to detect gas leakage. It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to includes infrared thermal imaging data of the training battery cells as is done in Xu, in the training data of Shih, because it would provide an efficient way to use a model that utilizes gas analysis data of battery cells. 21. Regarding claim 15, in addition to that mentioned for claim 14, Shih para 69, 71 show the measured data includes formation cycling data such as for gas analysis, but Shih and Fernando do not explicitly show the model is further trained on infrared thermal imaging data of the training battery cells so as to be configured to cause removal of other of the production battery cells from the manufacturing line responsive to the model identifying the other of the production battery cells as being subject to an impending thermal event based on infrared thermal imaging data measured during the formation cycling stage of the other of the production battery cells. Xu para 77, 87, 90 however does train a model using training data which includes infrared thermal imaging data of the (training) battery cells. Xu para 79, 81 show this is used to train the model to detect gas leakage. It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to includes infrared thermal imaging data of the training battery cells as is done in Xu, in the training data of Shih, because it would provide an efficient way to train a model that uses gas analysis data of battery cells. Given this combination, and as combined with Fernando, it would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to remove a battery in the battery production system of Shih as modified by Xu - in which the measured data would be thermal imaging data measured during a formation cycling stage of the battery cells and the identified condition is an impending thermal event (Shih para 61, 70 shows an identified condition may be temperature increase or effect on battery. Xu para 77 shows a specific condition may be a temperature characteristic corresponding to gas leakage) - because it would provide an efficient way to maintain high battery production quality. Those batteries identified as having impending thermal event would be removed . Conclusion 07-96 AIA 22. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : a) Kurtulus (US 12100868 B1) shows a machine learning model to detect faults in batteries. b) Chen (CN 114022420 A) shows a method for identifying defects in battery production to make a training dataset to train a machine learning model. c) Zhou (CN 117054884 A) shows training a machine learning model for a battery self-discharging detection method. 23. Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN PAUL SAX whose telephone number is (571)272-4072. The examiner can normally be reached Monday - Friday, 9:30 - 6:00 Est. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor Usmaan Saeed can be reached at 571-272-4046. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /STEVEN P SAX/Primary Examiner, Art Unit 2146 Application/Control Number: 18/442,826 Page 2 Art Unit: 2146 Application/Control Number: 18/442,826 Page 3 Art Unit: 2146 Application/Control Number: 18/442,826 Page 4 Art Unit: 2146 Application/Control Number: 18/442,826 Page 5 Art Unit: 2146 Application/Control Number: 18/442,826 Page 6 Art Unit: 2146 Application/Control Number: 18/442,826 Page 7 Art Unit: 2146 Application/Control Number: 18/442,826 Page 8 Art Unit: 2146 Application/Control Number: 18/442,826 Page 9 Art Unit: 2146 Application/Control Number: 18/442,826 Page 10 Art Unit: 2146 Application/Control Number: 18/442,826 Page 11 Art Unit: 2146 Application/Control Number: 18/442,826 Page 12 Art Unit: 2146
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