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 .
Response to Arguments
Applicant's arguments filed 07/16/2026 have been fully considered but they are not persuasive. Applicant’s arguments will now be addressed:
Applicant argues (Remarks; bottom of page 4) that prior art Chen (Chen et al., CN 105631873 A) “fails to disclose the features as follows: the preset defective-product detection rule may be set as such: computed yield rate of the first A number of voice coils currently manufactured decreases for consecutively B times, where A and B are positive integers”. This argument is moot since prior art Chen was not used to teach this limitation. Prior art Ushiku (Ushiku et al., US 2006/0085165 A1) was previously used to teach this limitation (from previous dependent claim 4; now canceled) and will be addressed later with regards to further arguments.
Applicant also argues (Remarks; p. 5, 1st paragraph) that “The Examiner cited paragraph [0062] of Ushiku as allegedly teaching detecting a yield rate decrease, and then inferred that since a yield rate is a ratio, its decrease occurs at least twice, satisfying the meaning of 'consecutively B times. Applicants respectfully submit that this inference is an unsupported logical leap that violates the requirement under MPEP § 2144.03 and § 2141.02 that an obviousness analysis must be grounded in the explicit teachings of the cited references”. The Examiner respectfully disagrees and hopes to further clarify the rejection and reasoning. Ushiku teaches computed yield rate (computing a yield rate) ([0062]) of the first A number of products currently manufactured (based on products, such as a semiconductor device, being manufactured) (Abstract and [0061]) (wherein the results (workmanship) data are classified into the groups Gr1 to Gr9 which correspond to the product number) (Figs. 9 and 10; [0061]) decreases for consecutively B times, where A and B are positive integers (wherein the quality control process can measure results including detecting that the yield rate is decreased) ([0061-0062]). First, the Examiner would like to point out that if the yield rate is decreasing it means that the product that is being created (first number of products being a positive integer) is going down at least once (one being a positive integer), since the claim language states “decreases for consecutively B (or 1) times, since B is a positive integer, and so is the number 1. Thus, based on the broadest reasonable interpretation of the claim language, Ushiku teaches computed yield rate of the first A number of products currently manufactured decreases for consecutively B times, where A and B are positive integers, as described above. Second, the Examiner would like to point out that if Applicant means for the yield rate to decrease 2 or more times that the claim should reflect that, however, this would still be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention. This is because Ushiku teaches detecting that the yield rate has decreased ([0062]) and based on detecting that the yield rate decreases, desires to improve the yield rate for the manufacturing process ([0006] and [0065]). This, in turn, would require the system/user to detect if based on changing the parameters ([0058-0059]) that the yield rate has changed (which can be for the better, yield increases, or for the worse, yield continues to decrease). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that the yield rate could obviously be checked multiple times, since when the system changes the parameters for the next production ([0058-0059]) that production would be checked to see if the yield has continued to go down. Third, the Examiner would like to point out that the claim language states, “the preset defective-product detection rule may be set as such:…”. Based on the broadest reasonable interpretation of the claim language, the limitation is to detect if a yield rate decreases, and computing that the yield rate decreases for a first number of voice coils for consecutive times, is merely a suggestion of one way the rule “may be set as such”. Thus, the Examiner points to Applicant’s arguments (bottom of page 5 to the top of page 6) that states “whereas monitoring the consecutive decline trend of a yield rate within a specific rolling window (first A products) for consecutively B times is a predictive trend monitoring (Trend Analysis) mechanism, the purpose of which is to provide an early warning before the yield rate completely falls below a critical threshold, so as to filter out random fluctuations and identify systematic deterioration trends” is not recited in the rejected claim(s), and that further adding these limitations found in the arguments would further clarify the claim language to potentially overcome the prior art of record. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant also argues (Remarks; p. 5, 4th paragraph) that Ushiku’s determination “is a static state determination of a process defect that has already occurred-a single-instance recognition of the fact that the yield rate is at a low level and is not a mechanism for dynamically and consecutively counting yield rate change trends. Ushiku nowhere discloses: (i) a technical scheme for computing a yield rate for a specific quantity window (first A products); (ii) a control logic for consecutively counting the number of yield rate decreases (consecutively B times); or (iii) the use of any such consecutive count as a trigger condition”. The Examiner respectfully disagrees. Ushiku teaches (i) a technical scheme for computing a yield rate for a specific quantity window (first A products) (detecting a yield rate for a specific product; i.e. results data into groups Gr1 to Gr9 which correspond to the product numbers) (Figs. 9 and 10; [0061-0062]); (ii) a control logic for consecutively counting the number of yield rate decreases (consecutively B times) (counting that the yield rate went down at least 1 time) ([0062]); or (iii) the use of any such consecutive count as a trigger condition (based on the yield rate being decreased revising the manufacturing conditions corresponding to the operation parameters for the desired product quality) ([0059]).
Applicant also argues (Remarks; bottom of page 5 to the top of page 6) that “whereas monitoring the consecutive decline trend of a yield rate within a specific rolling window (first A products) for consecutively B times is a predictive trend monitoring (Trend Analysis) mechanism, the purpose of which is to provide an early warning before the yield rate completely falls below a critical threshold, so as to filter out random fluctuations and identify systematic deterioration trends”. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “monitoring the consecutive decline trend of a yield rate within a specific rolling window” and “a predictive trend monitoring (Trend Analysis) mechanism, the purpose of which is to provide an early warning before the yield rate completely falls below a critical threshold, so as to filter out random fluctuations and identify systematic deterioration trends”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Lastly, Applicant argues (Remarks; p. 6, 1st paragraph) that “Ushiku is directed to semiconductor device manufacturing processes; its visualized data table analysis methodology is also significantly different in technical application context from the online quality control of voice coil winding processes”. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Prior art Yu (Yu et al., CN 115440497) teaches inspecting, in real-time (the online monitoring unit 13 for online real-time monitoring parameter of the product wound by the winding machine) (Fig. 1; p. 4; 2nd paragraph), a coil manufactured in the coil winding process (product wound by the winding machine) (p. 4; 2nd paragraph) via machine vision inspection (a CCD vision detection module) (p. 4; 2nd paragraph) according to a preset defective-product detection rule (such as when the product appearance is a blemish) (p. 4; 2nd paragraph). Although neither teaches that the coil is a specific “voice” coil, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that a voice coil is an obvious variant of a type of coil and that since prior art Obata (Obata et al., US 2019/0265686 A1) teaches that a large number of estimator-learning data sets are prepared by producing a wide variety of products based on a wide variety of materials, environments, facility states, and commands and by obtaining, from the products, combinations of a wide variety of parameter data and control parameter commands ([0060]), that one of the wide variety of products could obviously be a “voice” coil.
The invoking of 35 USC 112(f), with regards to claim 8, has been withdrawn due to Applicant canceling claim 8.
The 35 USC 101 rejection made to claim 10 has been withdrawn due to Applicant canceling claim 10.
Claim 1 has been amended with the limitations from (now canceled) claim 3; claims 3 and 8-10 have been canceled.
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.
Claim(s) 1, 2, 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al., CN 115440497 (Yu), Obata et al., US 2019/0265686 A1 (Obata), and further in view of Ushiku et al., US 2006/0085165 A1 (Ushiku).
Regarding claim 1, Yu teaches a coil winding real-time (the online monitoring unit 13 for online real-time monitoring parameter of the product wound by the winding machine) (Fig. 1; p. 4; 2nd paragraph) quality control method (the online monitoring unit for online real-time monitoring of the product wound by the winding machine) (p. 4; 2nd paragraph), comprising:
S102: inspecting, in real-time (the online monitoring unit 13 for online real-time monitoring parameter of the product wound by the winding machine) (Fig. 1; p. 4; 2nd paragraph), a coil manufactured in the coil winding process (product wound by the winding machine) (p. 4; 2nd paragraph) via machine vision inspection (a CCD vision detection module) (p. 4; 2nd paragraph) according to a preset defective-product detection rule (such as when the product appearance is a blemish) (p. 4; 2nd paragraph), and acquiring a defective-coil image of a corresponding coil which meets the preset defective-product detection rule (acquiring an image, when the image corresponds to the appearance being a blemish) (p. 4; 2nd paragraph);
S103: obtaining, via a self-taught machine learning unit (artificial intelligence (AI) system) (p. 1; Abstract and p. 4; 2nd paragraph), a quality inspection result from the defective-coil image (realizing that the result is an image of the coil with a blemish; analyzing and comparing) (p. 4; 2nd and 3rd paragraphs);
S104: adjusting (the winding instruction adjusting unit 15) (Fig. 1; p. 4; 4th paragraph) a corresponding parameter input factor based on the qualitative influence model and the quality inspection result (based on the result of the analyzing and comparing adjust the winding instruction parameter for correction) (p. 4; 2nd, 3rd, and 4th paragraphs);
S105: inspecting, in real time (the online monitoring unit 13 for online real-time monitoring parameter of the product wound by the winding machine) (Fig. 1; p. 4; 2nd paragraph), via machine vision inspection (CCD vision detection module) (p. 4; 2nd paragraph), a coil formed according to a parameter input factor-adjusted coil winding process (wherein the new adjusted parameter is set and a new product is created) (p. 4; 1st paragraph), and outputting a parameter-adjusted result in a case that the coil meets a preset non-defective product detection rule (data analysis, adjusting, and so on for multiple cycles until finished; i.e. a correct winding) (p. 4; 3rd and 4th paragraphs);
S106: feeding back the parameter-adjusted result to the self-taught machine learning unit, so that manufacturing rolls back from S102 to continue the parameter input factor-adjusted coil winding process (wherein the parameter is sent back to create the next winding coil for correction and the data analysis, adjusting and so on for multiple cycles until finished) (p. 4; 3rd and 4th paragraphs).
Yu teaches a control system of intelligent winding machine with an artificial intelligence system (p. 1; Abstract). However, Yu does not explicitly teach “S101: building a qualitative influence model defining relevancy between a parameter input factor and a quality inspection result in a coil winding process”.
Obata teaches a coil winding real-time quality control (a product quality management system) (Fig. 1; Abstract) (wherein the product can be a coil) (Fig. 1; [0026]) method, comprising:
S101: building a qualitative influence model (product parameter data generator 7; which may be implemented by machine learning, such as a deep learning convolutional neural network) (Fig. 1; [0032]) defining relevancy between a parameter input factor (parameter input; predetermined information of specification(s)) (Fig. 1; [0032]) and a quality inspection result (quality from the exterior of the product 12a) (Fig. 1; [0032]) in a coil winding process (of the coil winding process) (Fig. 1; [0025]);
S102: inspecting, a coil (coil 12a) (Fig. 1; [0032]) manufactured in the coil winding process (manufactured in the coil winding process) (Fig. 1; [0025]) via machine vision inspection (the camera 5 is an optical sensor that optically picks up an image of an exterior of an imaging target to obtain image data of the imaging target) (Fig. 1; [0030]) according to a preset defective-product detection rule (wherein the control parameter estimator 9 determines whether a product is a defective-free product or a defective product based on a binary rule) (Fig. 1; [0066]), and acquiring a defective-coil image of a corresponding coil which meets the preset defective-product detection rule (wherein the coil image is defective when it meets the binary rule of being defective (defective or defective-free) (Fig. 1; [0066]);
S103: obtaining, via a self-taught machine learning unit (using a convolutional neural network) (Fig. 16; [0097]), a quality inspection result from the defective-coil image (detecting an error between the two kinds of image data) (Fig. 16; [0097]);
S104: adjusting a corresponding parameter input factor based on the qualitative influence model and the quality inspection result (adjusting the control parameter command that minimizes the error) ([0097]);
S105: inspecting, via machine vision inspection (the camera 5 is an optical sensor that optically picks up an image of an exterior of an imaging target to obtain image data of the imaging target) (Fig. 1; [0030]), a voice coil formed according to a parameter input factor-adjusted voice coil winding process (inspecting the coil based on the adjusted parameter) ([0094]), and outputting a parameter-adjusted result in a case that the voice coil meets a preset non-defective product detection rule (obtain resulting parameter data from the product 12 and to use the resulting parameter data as a feedback value to estimate and adjust an active parameter so that the resulting parameter data is closer to target resulting parameter data) ([0061] and [0094]);
S106: feeding back the parameter-adjusted result to the self-taught machine learning unit, so that manufacturing rolls back from S102 to continue the parameter input factor-adjusted voice coil winding process (it is possible to cause the determination node to learn by error back propagation using training data labels; feedback resulting parameter data and vision data of the product) ([0066] and [0094-0097]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yu to include building a model since it enables the production facility to stably produce products having a target resulting parameter (Obata; [0075]); which improves the function to manage product quality, such as improving the yield rate (Obata; [0075]).
Although neither teaches that the coil is a specific “voice” coil, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that a voice coil is an obvious variant of a type of coil and that since Obata teaches that a large number of estimator-learning data sets are prepared by producing a wide variety of products based on a wide variety of materials, environments, facility states, and commands and by obtaining, from the products, combinations of a wide variety of parameter data and control parameter commands ([0060]), that one of the wide variety of products could obviously be a “voice” coil.
Yu teaches wherein the preset defective-product detection rule referred to in step S102 may be set as such (such as when the product appearance is a blemish) (p. 4; 2nd paragraph). Obata teaches wherein the preset defective-product detection rule referred to in step S102 may be set as such (wherein the control parameter estimator 9 determines whether a product is a defective-free product or a defective product based on a binary rule) (Fig. 1; [0066]); wherein the control parameter estimator estimates an active parameter necessary for controlling the production facility to produce a product having target resulting parameter contents ([0075]); and wherein this improves the function to manage product quality, such as improving the yield quality ([0075]).
However, neither explicitly teaches the preset defective-product detection rule may be set as such: “computed yield rate of first A number of voice coils currently manufactured decreases for consecutively B times, where A and B are positive integers”.
Ushiku teaches a method for determining a failure of a manufacturing condition (Abstract); wherein the inspection tool inspects and measures workmanship of the plurality of products ([0053]); and wherein a computed yield rate (computing a yield rate) ([0062]) of the first A number of products currently manufactured (based on products, such as a semiconductor device, being manufactured) (Abstract and [0061]) (wherein the results (workmanship) data are classified into the groups Gr1 to Gr9 which correspond to the product number) (Figs. 9 and 10; [0061]) decreases for consecutively B times, where A and B are positive integers (wherein the quality control process can measure results including detecting that the yield rate is decreased) ([0061-0062]).
First, the Examiner would like to point out that if the yield rate is decreasing it means that the product that is being created (first number of products being a positive integer) is going down at least once (one being a positive integer), since the claim language states “decreases for consecutively B (or 1) times, since B is a positive integer, and so is the number 1. Thus, based on the broadest reasonable interpretation of the claim language, Ushiku teaches computed yield rate of the first A number of products currently manufactured decreases for consecutively B times, where A and B are positive integers, as described above. Second, the Examiner would like to point out that if Applicant means for the yield rate to decrease 2 or more times that the claim should reflect that, however, this would still be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention. This is because Ushiku teaches detecting that the yield rate has decreased ([0062]) and based on detecting that the yield rate decreases, desires to improve the yield rate for the manufacturing process ([0006] and [0065]). This, in turn, would require the system/user to detect if based on changing the parameters ([0058-0059]) that the yield rate has changed (which can be for the better, yield increases, or for the worse, yield continues to decrease). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that the yield rate could obviously be checked multiple times, since when the system changes the parameters for the next production ([0058-0059]) that production would be checked to see if the yield has continued to go down. Third, the Examiner would like to point out that the claim language states, “the preset defective-product detection rule may be set as such:…”. Based on the broadest reasonable interpretation of the claim language, the limitation is to detect if a yield rate decreases, and computing that the yield rate decreases for a first number of voice coils for consecutive times, is merely a suggestion of one way the rule “may be set as such”.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of prior arts to include detecting a decrease in yield rate so that the manufacturing system can be improved in increase the yield rate for each of the manufacturing processes (Ushiku; [0006] and [0065]).
Regarding claim 2, Yu teaches wherein the preset defective-product detection rule referred to in step S102 is set as detection of a voice coil not conforming with requirements of the voice coil winding process for consecutively N times, where N is a positive integer (wherein the process can be thought that the above adjustment is not finished at one time, but data analysis, adjusting, and so on for multiple cycles to be finished) (p. 4; 4th paragraph).
Regarding claim 5, Yu teaches wherein in step S104, the corresponding parameter input factor is adjusted remotely (the control system can remotely finish the control, monitoring and adjusting of the wound product, improves the remote control capability of the winding machine) (p. 1; Abstract and p. 3; 4th paragraph).
Regarding claim 6, Yu teaches wherein in step S102, the machine vision inspection is CCD (charge coupled device) inspection (a CCD vision detection module) (p. 4; 2nd paragraph).
Regarding claim 7, Yu teaches wherein in step S105, the machine vision inspection is a dual-inspection scheme including CCD (charge coupled device) inspection (a CCD vision detection module) (p. 4; 2nd paragraph) and AOI (Auto Optical Inspection) (wherein the whole winding machine process is full flow automation) (p. 1; Abstract and the bottom of page 4 to the top of page 5).
Claim(s) 4 is rejected under 35 U.S.C. 103 as being unpatentable over Yu et al., CN 115440497 (Yu), Obata et al., US 2019/0265686 A1 (Obata), Ushiku et al., US 2006/0085165 A1 (Ushiku), and further in view of Chen et al., CN 105631873 A (Chen).
Regarding claim 4, Yu teaches wherein the preset non-defective product detection rule referred to in step S105 is set as detection of a voice coil conforming with requirements of the voice coil winding process (data analysis, adjusting, and so on for multiple cycles until finished; i.e. a correct winding) (p. 4; 3rd and 4th paragraphs). Obata teaches wherein the preset non-defective product detection rule referred to in step S105 is set as detection of a voice coil conforming with requirements of the voice coil winding process (obtain resulting parameter data from the product 12 and to use the resulting parameter data as a feedback value to estimate and adjust an active parameter so that the resulting parameter data is closer to target resulting parameter data) ([0061] and [0094]). Although neither teaches that the coil is a specific “voice” coil, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that a voice coil is an obvious variant of a type of coil and that since Obata teaches that a large number of estimator-learning data sets are prepared by producing a wide variety of products based on a wide variety of materials, environments, facility states, and commands and by obtaining, from the products, combinations of a wide variety of parameter data and control parameter commands ([0060]), that one of the wide variety of products could obviously be a “voice” coil. Ushiku teaches a method for determining a failure of a manufacturing condition (Abstract); and wherein the inspection tool inspects and measures workmanship of the plurality of products ([0053]).
However, none of them explicitly teaches that the process is conforming “for consecutive M times, where M is a positive integer”.
Chen teaches a flexible film reeling quality visual detecting method (p. 2; 5th paragraph); wherein by using a non-contact visual detection mode to the feed coil end performing real-time detection (p. 2; 5th paragraph); and wherein the process is conforming for consecutive M times, where M is a positive integer (once the process conforms based on repeating the steps until the calculated relative offset is zero, thereby completing the correction process) (p. 5; 3rd – 8th paragraphs (specifically steps (i) – (iv))).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of prior arts to include constructing a certain algorithm to accurately acquire the film into a roll effect of quantization data and corresponding higher precision, faster response and convenient automatic operation mode to execute the real-time correction of various winding structures (Chen; p. 2, 5th paragraph).
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.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J VANCHY JR whose telephone number is (571)270-1193. The examiner can normally be reached Monday - Friday 9am - 5pm.
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/MICHAEL J VANCHY JR/Primary Examiner, Art Unit 2666 Michael.Vanchy@uspto.gov
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