CTNF 18/113,217 CTNF 77333 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Response to Arguments Applicant’s arguments with respect to claims 34-57 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Claim Objections Claims 34, 41, and 52 are objected to because of the following informalities: Claims 34, 41, and 52, “the manufacturing tolerances of subsequent steps in the series” lacks clear antecedent basis in the claims for “the manufacturing tolerances”. Appropriate corrections are required. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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 Claim s 34-38, 41-45, 48-49 and 52-55 are rejected under 35 U.S.C. 103 as being unpatentable over Putman, U.S. Patent Application Publication No. U.S. 2020/0401120 A1 (‘120) in view of Poulin, U.S. Patent Application Publication No. U.S. 2023/0004685 A1 (‘685) . As per claim 34, the operational limitations are rejected for at least the same rationale as applied with respect to claim 41 from below. Further, ‘120 discloses the additional system components of a measuring device, a processing device, and a non-transitory storage device comprising code that, when executed by the processing device, cause the system to perform the operational limitations (e.g. See ‘120; [0096] – [ [0097] and [0100]). As per claim 35, the rejection rationale as applied to claim 42 is incorporated herein. As per claim 36, the rejection rationale as applied to claim 43 is incorporated herein. As per claim 37, the rejection rationale as applied to claim 44 is incorporated herein. As per claim 38, the rejection rationale as applied to claim 45 is incorporated herein. As per claim 41, ‘120 discloses a method of manufacturing an object (e.g. See ‘120; [0020], which disclose a manufacturing process for producing a final product) , the method comprising: receiving actual values of attributes from a measuring device after each step of a series of steps for manufacturing an object (e.g., See ‘120; [0051], which discloses measuring values after process stations and providing them to a controller) ; processing each actual value using a plurality of machine learning models to determine a target value of an attribute for a subsequent step in the series to account for manufacturing tolerances of preceding steps in the series (e.g., See ‘120; [0034], which discloses using machine learning models to evaluate the measured values) , wherein the plurality of machine learning models determines target values for attributes by running a plurality of simulations using (i) the actual values of the attributes achieved by the preceding steps (e.g., See ‘120; [0067], which discloses using prior station information to adjust control inputs for later stations) ; and providing the target value to a manufacturing system configured to perform the subsequent step to achieve the target value of the attribute (e.g., See ‘120; [0061], which discloses providing calculated control inputs to station controllers). However, ‘120 does not specifically disclose that the plurality of machine learning models determines target values for attributes by (ii) running a plurality of simulations uses the manufacturing tolerances of subsequent steps in the series. ‘685 discloses this feature by disclosing using a machine learning model and theoretical modeling to predict deviations using component data and process parameters (e.g., See ‘685; [0095] – [0096]). It would have been obvious to one of ordinary skill in the art at the time the invention was made to have incorporated the teachings of ‘685 into ‘120 for the purpose of improving later manufacturing adjustments using predicted process deviations before manufacturing continues, thereby improving the accuracy of target values for later manufacturing steps. As per claim 42, ‘120’s combined system (‘120 in view of ‘685) further discloses that each step in the series of steps has a respective manufacturing tolerance (e.g., See ‘120; [0038], which discloses known limitations for each process station). As per claim 43, ‘120’s combined system further discloses training the plurality of machine learning models using historical data comprising (i) historical actual values measured after historical performance of each step of the steps to manufacture historical objects (e.g., See ‘120; [0051], which discloses training machine learning models using measured values from each process station) and (ii) historical test results obtained by testing the historical objects (e.g., See ‘120; [0056], which discloses comparing expected values to actual final output measurements). As per claim 44, ‘120’s combined system further discloses that after providing the target value to the manufacturing system, causing the manufacturing system to perform the subsequent step to achieve the target value of the attribute (e.g., See ‘120; [0061], which discloses providing control inputs to station controllers to control the process stations). As per claim 45, ‘120’s combined system further discloses that after the series of steps is performed, (1) perform one or more tests on the object to obtain test results (e.g., See ‘120; [0056], which discloses obtaining actual final output measurements) , and (2) retrain the plurality of machine learning models using data comprising the actual values measured after performance of each step of the series of steps and the test results (e.g., See ‘120; [0061] – [0062], which discloses continuously improving the machine learning models using measured values obtained during operation of the manufacturing process). As per claim 48, ‘120’s combined system further discloses that at least one step in the series of steps is intended to achieve multiple target values for respective attributes (e.g., See ‘120; [0024], which discloses station setpoints for multiple control values). As per claim 49, ‘120’s combined system further discloses that the plurality of machine learning models is trained using one or more reinforcement learning algorithms (e.g., See ’120; [0034], which discloses machine learning techniques to include reinforcement learning). As per claim 52, the operational limitations and the system components of a measuring device, processing device, and storage device are rejected for at least the same rationale as applied with respect to claim 34. Further, ‘120 discloses the additional system component of a manufacturing device configured to perform a series of steps for manufacturing an object by describing process stations having tools and equipment that perform the process steps, as well as the step of performing the subsequent step to achieve the target value of the attribute (e.g., See ‘120; [0021] – [0023]). As per claim 53, the rejection rationale as applied to claim 35 is incorporated herein. As per claim 54, the rejection rationale as applied to claim 36 is incorporated herein. As per claim 55, the rejection rationale as applied to claim 38 is incorporated herein . 07-22-aia AIA Claim s 39, 46 and 56 are rejected under 35 U.S.C. 103 as being unpatentable over Putman, U.S. Patent Application Publication No. U.S. 2020/0401120 A1 (‘120) in view of Poulin, U.S. Patent Application Publication No. U.S. 2023/0004685 A1 (‘685) , as applied to claim s 34, 41 and 52, respectively, from above, and further in view of Liu, U.S. Patent Application Publication No. 2011/0108142 A1 (‘142) . As per claims 39, 46 and 56, ‘120 in view of ‘685 does not disclose the combination of claimed features. ‘142 discloses these features by disclosing that the object is a vapor chamber (e.g., See ‘142; [0003] and [0013]) , wherein a first step in the series of steps comprises manufacturing an outer shell (e.g., See ‘142; [0035] – [0036], which discloses manufacturing a casing for the vapor chamber) , wherein a second step in the series of steps comprises adding powder to the outer shell to create a porous wicking structure on an interior of the outer shell (e.g., See ‘142; [0040] – [0042], which discloses forming a powder porous wick structure layer in the vapor chamber) , and wherein a third step in the series of steps comprises adding water to the interior of the outer shell (e.g., See ‘142; [0043], which discloses filling water into the vapor chamber). It would have been obvious to one of ordinary skill in the art at the time the invention was made to have incorporated the teachings of ‘685 into ‘120 in view of ‘685 for the purpose of applying known ML control techniques to vapor chamber manufacturing, thereby improving control of vapor chamber manufacturing operations by adjusting later steps based on measured results from earlier steps . 07-22-aia AIA Claim s 40, 47 and 57 are rejected under 35 U.S.C. 103 as being unpatentable over Putman, U.S. Patent Application Publication No. U.S. 2020/0401120 A1 (‘120) in view of Poulin, U.S. Patent Application Publication No. U.S. 2023/0004685 A1 (‘685) , as applied to claim s 34, 41 and 52, respectively, from above, and further in view of CHeng, U.S. Patent Application Publication No. 2006/0197245 A1 (‘245) . As per claims 40, 47 and 57, ‘120 in view of ‘685 does not disclose the combination of claimed features. ‘245 discloses these features by disclosing that the object is a heat pipe (e.g., See ‘245; [0003]) , wherein a first step in the series of steps comprises manufacturing a tubular outer shell (e.g., See ‘245; [0038], inserting green tape into a hollow casing) , wherein a second step in the series of steps comprises adding powder to the tubular outer shell to create a porous wicking structure on an interior of the tubular outer shell (e.g., See ‘245; [0039], which discloses sintering the green tape in the casing to form a wick on the inner wall of the casing) , and wherein a third step in the series of steps comprises adding water to the interior of the tubular outer shell (e.g., See ‘245; [0040], which discloses filling working fluid into the casing and sealing the casing). It would have been obvious to one of ordinary skill in the art at the time the invention was made to have incorporated the teachings of ‘685 into ‘120 in view of ‘245 for the purpose of applying known ML control techniques to heat pipe manufacturing, thereby improving control of heat pipe manufacturing operations by adjusting later steps based on measured results from earlier steps . 07-22-aia AIA Claim 50 is rejected under 35 U.S.C. 103 as being unpatentable over Putman, U.S. Patent Application Publication No. U.S. 2020/0401120 A1 (‘120) in view of Poulin, U.S. Patent Application Publication No. U.S. 2023/0004685 A1 (‘685) , as applied to claim 41, from above, and further in view of Fahrenkopf, U.S. Patent Application Publication No. 2021/0247744 A1 (‘744) . As per claim 50, ‘120 in view of ‘685 does not disclose that the plurality of machine learning models is trained using one or more Q learning algorithms. ‘744 discloses these features by disclosing using Q learning to train a reinforcement leaning model for manufacturing process control (e.g., See ‘744; [0036] and [0043]). It would have been obvious to one of ordinary skill in the art at the time the invention was made to have incorporated the teachings of ‘744 into ‘120 for the purpose of improving selection of later manufacturing actions by learning which actions lead to better manufacturing results . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim 51 is 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. References Considered but Not Relied Upon The following references were considered but were not relied upon with respect to any prior art rejections: (1) US 7,970,588 B2, which discloses using empirical process data to adapt models for predicting and controlling manufacturing output values; (2) US 10,921,782 B2, which discloses using machine learning for real time adaptive control of additive manufacturing process parameters; (3) US 10,664,743 B2, which discloses modeling a process using measurements from an earlier subprocess to predict later subprocess outputs; (4) US 12,153,401 B2, which discloses adjusting downstream manufacturing station instructions using machine learning quality predictions from earlier state data; and (5) US 11,829,873 B2, which discloses using trained inverted models to predict manufacturing inputs for semiconductor process results. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to RONALD D HARTMAN JR whose telephone number is (571)272-3684. The examiner can normally be reached M-F 8:30 - 4:30 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, Mohammad Ali can be reached at (571) 272-4105. 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. /RONALD D HARTMAN JR/Primary Patent Examiner, Art Unit 2119 May 30, 2026 /RDH/ Application/Control Number: 18/113,217 Page 2 Art Unit: 2119 Application/Control Number: 18/113,217 Page 3 Art Unit: 2119
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