DETAILED ACTION
Claims 1-8 are pending, and claims 1-2 are currently under review.
Claims 3-8 are withdrawn.
Claims 9-16 are cancelled.
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 .
Response to Amendment
The amendment filed 11/04/2025 has been entered. Claims 1-8 remain(s) pending in the application. Applicant’s amendments to the Claims have overcome each and every 101 and 112(b) rejection previously set forth in the Non-Final Office Action mailed 8/11/2025.
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.
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.
Claim(s) 1-2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yokoyama et al. (US 2021/0155999) in view of either one of Cao et al. (CN109492335, machine translation referred to herein) or Schiefer et al. (1999, A neural network controls the galvannealing process).
Regarding claim 1, Yokoyama et al. discloses a method of determining an amount of hydrogen emitted from a steel after galvanizing and heating, which directly correlates to an amount of hydrogen still within said steel after galvanizing and heating (ie. downstream of the reheating processes) [abstract]; wherein said method includes steps of providing a continuous annealing line including an annealing process, galvanizing process (ie. coating), and alloying (ie. reheating) process [0177]; followed by measuring hydrogen emission by controlling heating after alloying (ie. acquiring reheating parameter) and measuring hydrogen after alloying (ie. “predicting hydrogen…”) [0127].
Yokoyama et al. does not teach this step is done using machine learning. However, this would have been obvious in view of the prior art. Cao et al. discloses that it is commonly known and conventional to control continuous annealing lines with neural network machine learning control methods as a popular control method for achieving dynamic control [0005]. Alternatively, Schiefer et al. discloses that it is known to utilize a neural network controller (ie. machine learning) to improve galvannealing control and measurements [abstract, “conclusion”]. Therefore, it would have been obvious to modify the method of Yokoyama et al. by performing control with neural network machine learning as taught by either Cao et al. or Schiefer et al. for the aforementioned benefits. The examiner notes that the suggested combination of the aforementioned prior art would naturally result in input of data into a computer model executed by a processor and output of data of hydrogen content predictions because neural network machine learning control would be recognized by one of ordinary skill to naturally be performed by a computer having a processor and memory as claimed.
Regarding claim 2, the aforementioned prior art discloses the method of claim 1 (see previous). Yokoyama et al. further discloses that hydrogen embrittlement is affected by annealing conditions such as temperature (ie. input data of annealing process parameter), wherein said annealing temperature is directly related to composition (ie. Ac3 point) as would have been recognized by one of ordinary skill [0178].
Claim(s) 1-2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hirashima et al. (WO2018146828, US 2020/0032364 referred to as English translation) in view of either one of Cao et al. (CN109492335, machine translation referred to herein) or Schiefer et al. (1999, A neural network controls the galvannealing process).
Regarding claim 1, Hirashima et al. discloses a method of determining an amount of diffusible hydrogen content in steel after galvanizing and heating (ie. downstream of the reheating processes) [abstract, 0025]; wherein said method includes steps of providing a continuous line and further includes annealing line including an annealing process, galvanizing process (ie. coating), and alloying (ie. reheating) process [0081-0096]; followed by measuring hydrogen emission by controlling heating after alloying (ie. acquiring reheating parameter) and measuring hydrogen after alloying (ie. “predicting hydrogen…”) [0014].
Alternatively, Hirashima et al. does not expressly teach a continuous galvanizing line. However, continuous galvanizing lines are well known, common, and conventional means of heat treating and galvanizing steels such that it would have been obvious to perform a continuous heat treatment and galvanizing method as would have been recognized by one of ordinary skill.
Hirashima et al. does not teach this step is done using machine learning. However, this would have been obvious in view of the prior art. Cao et al. discloses that it is commonly known and conventional to control continuous annealing lines with a neural network machine learning control methods as a popular control method for achieving dynamic control [0005]. Alternatively, Schiefer et al. discloses that it is known to utilize a neural network controller (ie. machine learning) to improve galvannealing control and measurements [abstract, “conclusion”]. Therefore, it would have been obvious to modify the method of Hirashima et al. by performing control with neural network machine learning as taught by either Cao et al. or Schiefer et al. for the aforementioned benefits. The examiner notes that the suggested combination of the aforementioned prior art would naturally result in input of data into a computer model executed by a processor and output of data of hydrogen content predictions because neural network machine learning control would be recognized by one of ordinary skill to naturally be performed by a computer having a processor and memory as claimed..
Regarding claim 2, the aforementioned prior art discloses the method of claim 1 (see previous). Hirashima et al. further discloses that hydrogen content is directly affected by steel composition inclusions such as P, S, Al, etc. [0040-0058]. Accordingly, one of ordinary skill would have been motivated to acquire steel composition measurements as input data when performing prediction of hydrogen content as suggested by the prior art combination because Hirashima et al. expressly teaches that hydrogen content is further influenced by steel inclusions.
Response to Arguments
Applicant's arguments, filed 11/04/2025, regarding the 103 rejections have been fully considered but they are not persuasive.
Applicant argues that the prior art does not teach acquiring transformation rate data as required in the claims. The examiner cannot concur. The claim merely recites “acquiring at least one parameter selected from operation parameters…and transformation rate information…” (emphasis added). Under broadest reasonable interpretation, the examiner cannot concur with applicant because the claim does not particularly require transformation rate information and is met by acquiring operation parameters as explained above.
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 NICHOLAS A WANG whose telephone number is (408)918-7576. The examiner can normally be reached usually M-Th: 7-5.
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/NICHOLAS A WANG/Primary Examiner, Art Unit 1734