DETAILED ACTION
Claim Rejections - 35 USC § 103
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) 1-7 and 9-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bettinger et al. (2019/0361409) in view of Schlang et al. (6,807,449). Regarding claims 1,11 and 18, Bettinger discloses a roll forming apparatus comprising a rolling mill ([0027], lines 16-23) having rolling stands (15; [0124], lines 1-4) configured to perform a roll forming process on a metal strip (14). Bettinger discloses a computer program ([0065], lines 1-7). The roll forming apparatus comprises at least one processing circuit (6) with programmable circuitry configured to be programmed by machine readable instructions (programming; [0083] and [0105], lines 3-4) including rolling data ([0118] and [0125], lines 10-17) to generate a 3D representation ([0012], lines 6-7; [0118], lines 10-11; [0143], lines 5-6) including imaging [0128] by acquiring data (M; [0080], [0131]) of the metal strip (14). Bettinger discloses machine learning and modelling ([0105], lines 1-5; [0166], lines 10-12) based on the rolling data acquired during rolling at the upstream and downstream positions and training the model with a deep or convolution neural network (9; [0045], lines 1-5 and [0107], lines 7-11 and 17-21) to provide output (evaluation results A; [0121], lines 1-2) to control adjustment of the roll forming process ([0121], lines 3-6) wherein rollers of the rolling stands are moved ([0126], lines 4-7]) by roller control means (5) in response to the output (A). Bettinger discloses sensors (2; [0079]) configured to acquire the measured data (M) but does not specifically recite a plurality of upstream sensors and downstream sensors which include at least a first upstream sensor and a second downstream sensor.
Schlang teaches a roll forming device (2; Fig. 2) comprising a plurality of rollers (4) and a metal strip (5) that moves through the roll forming device from an inlet (16) to an outlet (17). The roll forming device (2) includes at least one processor circuit (controller, 10) and a sensing device (12) comprising at least a first sensor and a second sensor of the sensing device (12) wherein the at least first and second sensors (downward arrows toward sensing device 12, Fig. 2) are configured to measure strip properties (col. 6, lines 15-27) at rolling stands locations between rollers (4) at upstream to downstream locations from the inlet (16) to the outlet (17) of the rolling mill. Schlang teaches a neural network (33; col. 5, lines 10-15) and that variables (31) which are input to the neural network include strip thickness, strip width and strip composition (col. 5, lines 28-32). It would have been obvious to the skilled artisan prior to the effective filing date of the present invention to modify the roll forming device of Bettinger to include a first upstream sensor and a second downstream sensor as taught by Schlang in order to sense and strip parameter values at a plurality of locations as the strip moves through the rolling mill and provide the sensed strip parameters to the neural network.
Regarding claims 2-4,10 and 12-14, Bettinger discloses ([0128], lines 1-6) that the image data is an area between successive roller stands (15) in a multi-stand rolling mill and that a comparison with a difference manipulated variable upstream ([0128], lines 9-12) is made to determine a condition (meandering, [0130], lines 4-6) and that the image data is taken at a strip head (strip inlet; [0129], lines 1-3) and between the rolling stands ([013], lines 1-3) and Schlang teaches that lateral displacement and tension are measured at a plurality of positions between individual rolling stands (col. 6, lines 22-25). It is obvious modification to take multiple measurements in the rolling mill of Bettinger as taught by Schlang so as to provide a plurality of measurements as the strip moves longitudinally.
Regarding claim 5, Bettinger discloses machine learning over a plurality of different production cycles ([0121], lines 4-8) comprising subsequent rolling passes including the next rolling pass.
Regarding claims 6 and 16, Bettinger discloses a flatness adjustment optimization ([0120],[0122]).
Regarding claims 7,9,15,17 and 19 Bettinger discloses a material twist adjustment ([0130], lines 4-6) comprising meandering which is a value of how much the strip material twists, flares and bends (deviates) from a straight line through the rolling mill.
Regarding claim 20, Bettinger determines a condition (meandering, [0130], lines 4-6).
Regarding claim 21, Bettinger discloses a plurality of roll stands (15) and a first sensor (2; Fig. 1) and Schlang teaches that a sensor (12) is located near to an inlet (16) of the rolling mill (2; Fig. 2). It is an obvious modification to locate sensors in the rolling mill of Bettinger near to an inlet or outlet as taught by Schlang.
Regarding claim 22, Bettinger discloses that the machine learning is based on parameters (cross section and flatness) of a processed material ([0125], lines 10-12) after passage through the rolling stands.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bettinger et al. (2019/0361409) in view of Schlang et al. (6,807,449) and further in view of Neugnadenfelder (DE 202019103020). Regarding claim 8, Bettinger discloses a cloud remote computing [0068] but does specifically recite point cloud data output. Neugnadenfelder teaches a camera device (Fig. 6) that is a three-dimensional sensor ([0012] and [0014], lines 3-5) for sensing a contour of a strip material (2) and teaches outputting the data as point cloud data ([0014], lines 10-13) which comprises points in three axes to model the material (3D representation; [0015], lines 4-6). It would have been obvious to the skilled artisan prior to the effective filing date of the present invention to output the 3D data of Bettinger as point cloud data as taught by Neugnadenfelder in order to represent a detailed digital model of the strip.
Response to Arguments
Applicant's arguments filed 4-14-2026 have been fully considered. The new references to Bettinger and Schlang have been used to respond to Applicant’s amendment direction to deep, graph or convolution neural networks which are disclosed by Bettinger. Regarding claims 7,9,15,17 and 19, no roller means which are configured to bend, flare or twist a material to form a purposely bent or trough type roll formed material are claimed so the twist, bend and flare limitations are read as the bending and twisting of the material as it passes through rolling mill stands which is correctable by the neural network evaluation of Bettinger. Meandering of a strip of material during rolling is a bending and twisting of the material which makes the strip deviate laterally from a straight line when passing through rolling mill stands.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Ziegelaar et al. (6,766,394) teaches that meandering comprises skewing and twisting of a metal strip (12; Fig. 4) and teaches an upstream sensor (51) and a downstream sensor (61).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 EDWARD THOMAS TOLAN whose telephone number is (571)272-4525. The examiner can normally be reached M-F 7:30-5.
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, Chris Templeton can be reached at 571-270-1477. 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.
/EDWARD T TOLAN/Primary Examiner, Art Unit 3725
Prosecution Insights