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 .
Claim Objections
Claims 1-24 are objected to because of the following informalities:
Regarding Claims 1-24, each claim should be numbered consecutively. In particular, Claim 1 recites “a)” that should be “1.” and Claim 16 recites “b)” that should be “16.”
Appropriate correction is required.
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.
Claim(s) 1, 10, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Uneno et al. (WO2018101457A1 -hereinafter Uneno -Note: As the machine translation attached) in view of Taniai et al. (US20230245437A1 -hereinafter Taniai).
Regarding Claim 1, Uneno teaches a method for predicting anomalies in a manufacturing line (see Abstract; Uneno: “a manufacturing line control system (10), comprising a processing unit (20) wherefrom a processed article (101) is obtained … The manufacturing line control system (10) further comprises an inspection unit (40) which inspects the processed article (101).”), the method comprising operating at least one processor to:
receive a sequence of images of one or more workpieces in the manufacturing line; (see page 8, first paragraph; Uneno: “The inspection unit 40 includes an imaging unit 41 that images the workpiece 101 processed by the processing unit 20 and an illumination unit 42 that illuminates the workpiece 101.” See page 13, third paragraph: “The inspection unit 40 stores inspection data while sequentially inspecting the workpiece 101 (imaging and image processing).”)
extract feature data from the sequence of images (see page 8, third paragraph; Uneno: “Here, the operation of each unit included in the inspection unit 40 will be described. The image processing unit 43 processes the captured image obtained by the imaging unit 41 with preset contents to obtain processed data.”), the feature data comprising a representation of …an appearance of the one or more workpieces in the manufacturing line; (see page 8, third paragraph; Uneno: “Processed data refers to data obtained as a result of image processing. For example, an image obtained by performing various types of filter processing (for example, binarization processing, averaging processing, expansion processing, contraction processing, etc.) on a captured image. It is. Alternatively, it is numerical data such as coordinate data, number, and degree of coincidence obtained by various feature extraction processes (area measurement, blob count, edge detection process, pattern matching process, etc.). Alternatively, it is a combination of a captured image, an image subjected to various filter processes, and numerical data obtained by various feature extraction processes.”)
generate one or more annotations to locate the one or more anomalies within the images of the manufacturing line; and (see page 11, last paragraph; Uneno: “When the difference value for each unit identification code is out of the range, it is determined that an abnormality has occurred, and the minute chip occurrence flag and the unit identification code of the unit 30 in which the minute chip has occurred (concave portion 141 for fiber stacking) The detected information is transmitted to the notification unit 80 and the control unit 90. The notification unit 80 displays whether it is normal or abnormal on the screen of the display unit 81. In the case of an abnormality, a message based on the detection information is displayed, so that the operator can easily know the location where the abnormality has occurred and the content of the abnormality.”)
generate at least one notification to identify the anomalies, the at least one notification comprising the one or more annotations. (see page 8, paragraph 6; Uneno: “The notification unit 80 notifies the unit identification code and the determination result. A display unit 81 for displaying the unit identification code corresponding to the determination result and the determination result is provided. The notification unit 80 causes the screen of the display unit 81 to display whether it is normal or abnormal at an appropriately set timing such as every hour, every hour, every day, or the like.”)
However, it does not explicitly teach the feature data comprising a representation of a motion …of the one or more workpieces in the manufacturing line; apply the feature data to a predictive model to detect one or more anomalies in the manufacturing line;
Taniai from the same or similar field of endeavor teaches:
the feature data comprising a representation of a motion …of the one or more workpieces in the manufacturing line; (see [0013]; Taniai: “In the model generation apparatus according to the above aspect, the one or more training images may include a plurality of images captured continuously. The real number to be computed using regression may be an estimate for a motion of an object in the plurality of images.”)
apply the feature data to a predictive model to detect one or more anomalies in the manufacturing line; (see [0169]; Taniai: “When the detection apparatus 2C is used to perform visual inspection of products (in other words, the object RC is a defect in a product), the controller may determine whether a product has a defect based on the estimate for the detected position of the defect obtained through the processing in step S202.” See [0112]: “In step S202, the controller 21 operates as the regression unit 212, and refers to the training-result data 125 and defines the trained neural network module 5. The controller 21 then computes a real number using regression from the obtained target images 221 with the trained neural network module 5.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Uneno to include Taniai’s features of comprising a representation of a motion of the one or more workpieces in the manufacturing line; and applying the feature data to a predictive model to detect one or more anomalies in the manufacturing line. Doing so would improve the accuracy of computing a real number from one or more images using regression with a neural network. (Taniai, [0005])
Regarding Claim 10, the combination of Uneno and Taniai teaches all the limitations of claim 1 above, Uneno further teaches further comprising operating the at least one processor to pre-process the sequence of images. (see page 8, paragraph 3; Uneno: “The image processing unit 43 processes the captured image obtained by the imaging unit 41 with preset contents to obtain processed data. Processed data refers to data obtained as a result of image processing. For example, an image obtained by performing various types of filter processing (for example, binarization processing, averaging processing, expansion processing, contraction processing, etc.) on a captured image.”)
Regarding Claim 16, the limitations in this claim is taught by the combination of Uneno and Taniai as discussed connection with claim 1.
Claim(s) 2-3, 6-7, 17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Uneno in view of Taniai in view of Tsai et al. (US 20220051014 A1 -hereinafter Tsai).
Regarding Claim 2, the combination of Uneno and Taniai teaches all the limitations of claim 1 above; however, it does not explicitly teach comprising operating the at least one processor to: for each anomaly of the one or more anomalies, identify at least one image amongst the sequence of images showing the anomaly; select feature data associated with the anomaly; and apply the feature data associated with the anomaly to the predictive model to determine a classification to be associated with the anomaly.
Tsai from the same or similar field of endeavor teaches comprising operating the at least one processor to:
for each anomaly of the one or more anomalies, identify at least one image amongst the sequence of images showing the anomaly; (see [0039]; Tsai: “instructions 716 may include scanning an image to verify whether an object within the image includes any defects, such as a disk misalignment, broken parts, or the like.”)
select feature data associated with the anomaly; and (see [0040]; Tsai: “Data 718 may be retrieved, stored, and modified by the one or more processors 712 in accordance with the instructions 716.”)
apply the feature data associated with the anomaly to the predictive model to determine a classification to be associated with the anomaly. (see [0055]; Tsai: “Computing device 710 can classify any differences between the images as a defect …Additionally, using machine learning the system can be automatically tuned to detect more and different types of defects.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Uneno and Taniai to include Tsai’s features of operating the at least one processor to: for each anomaly of the one or more anomalies, identify at least one image amongst the sequence of images showing the anomaly; select feature data associated with the anomaly; and apply the feature data associated with the anomaly to the predictive model to determine a classification to be associated with the anomaly. Doing so would improve the defect detection and save on costs in the long term as these defects can be fixed before they become a larger issue. (Tsai, [0061] and [0070])
Regarding Claim 3, the combination of Uneno, Taniai, and Tsai teaches all the limitations of claim 2 above; however, it does not explicitly teach wherein the at least one notification comprises an indication of the classification associated with the anomaly.
Tsai from the same or similar field of endeavor teaches wherein the at least one notification comprises an indication of the classification associated with the anomaly. (see [0060]; Tsai: “a computer vision algorithm can be trained to notice different types of common defects on an object based on a detected defect image portion on an image containing the object.”)
The same motivation to combine Uneno, Taniai, and Tsai a set forth for Claim 2 equally applies to Claim 3.
Regarding Claim 6, the combination of Uneno, Taniai, and Tsai teaches all the limitations of claim 2 above, Uneno further teaches wherein the manufacturing line comprises a transport mechanism. (see page 10, paragraph 7; Uneno: “the manufacturing apparatus 110 includes a transport device 170 as a transfer transport mechanism that separates the absorber 105 from the concave portion 141 for stacking fibers and transfers it to the mount 109.”)
Regarding Claim 7, the combination of Uneno, Taniai, and Tsai teaches all the limitations of claim 6 above; however, it does not explicitly teach comprising operating the at least one processor to classify the anomaly as at least one of a missing part of a workpiece or a change in a synchronous speed of a workpiece along the transport mechanism.
Tsai from the same or similar field of endeavor teaches comprising operating the at least one processor to classify the anomaly as at least one of a missing part of a workpiece or a change in a synchronous speed of a workpiece along the transport mechanism. (see [0061]; Tsai: “computing device 710 can classify the defect type of a defect image as being a missing part.”)
The same motivation to combine Uneno, Taniai, and Tsai a set forth for Claim 2 equally applies to Claim 7.
Regarding Claim 17, the limitations in this claim is taught by the combination of Uneno, Taniai, and Tsai as discussed connection with claim 2.
Regarding Claim 19, the limitations in this claim is taught by the combination of Uneno, Taniai, and Tsai as discussed connection with claim 6.
Regarding Claim 20, the limitations in this claim is taught by the combination of Uneno, Taniai, and Tsai as discussed connection with claim 7.
Claim(s) 4-5 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Uneno in view of Taniai in view of Tsai in view of Agerstam et al. (US20190138423A1 -hereinafter Agerstam) in view of O’Connor (US 6551422 B1 -hereinafter O’Connor).
Regarding Claim 4, the combination of Uneno, Taniai, and Tsai teaches all the limitations of claim 2 above; however, it does not explicitly teach further comprising operating the at least one processor to: determine one or more corrective actions for the one or more anomalies based on the classifications associated with the one or more anomalies; define a set of operating commands for one or more actuators of the manufacturing line based on the one or more corrective actions; and operate the one or more actuators to implement the one or more corrective actions.
Agerstam from the same or similar field of endeavor teaches further comprising operating the at least one processor to: determine one or more corrective actions for the one or more anomalies based on the classifications associated with the one or more anomalies; (see [0104]; Agerstam: “The anomalous output of the example hybrid model 1048 is defined as a sensor anomaly and is represented as anomalous to indicate that the monitored system 108 is malfunctioning and a corrective action should be applied to the anomalous sensors.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Uneno, Taniai, and Tsai to include Agerstam’s features of determining one or more corrective actions for the one or more anomalies based on the classifications associated with the one or more anomalies. Doing so would improve the likelihood that the system operates as intended with substantially few or no errors. (Agerstam, [0016])
However, it does not explicitly teach:
define a set of operating commands for one or more actuators of the manufacturing line based on the one or more corrective actions; and
operate the one or more actuators to implement the one or more corrective actions.
O’Connor from the same or similar field of endeavor teaches:
define a set of operating commands for one or more actuators of the manufacturing line based on the one or more corrective actions; and (see Abstract; O’Conner: “a controller for selectively monitoring the sensors and controlling output actuators to maintain desired parameters of the metal treatment process,”)
operate the one or more actuators to implement the one or more corrective actions. (see column 4, lines 1-6; O’Conner: “if the controller receives input from the sensors indicating that a particular parameter is straying from its desired value, (e.g., pH is straying from the desired pH range), then the controller, via the output actuators, can take corrective or preventive action to keep the particular parameter within its desired value”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Uneno, Taniai, Tsai, and Agerstam to include O’Conner’s features of defining a set of operating commands for one or more actuators of the manufacturing line based on the one or more corrective actions; and operating the one or more actuators to implement the one or more corrective actions. Doing so would operate smoothly, efficiently, and on time. (O’Conner, column 1, lines 27-29)
Regarding Claim 5, the combination of Uneno, Taniai, Tsai, Agerstam, and O’Connor teaches all the limitations of claim 4 above, Taniai further teaches wherein the at least one notification comprises an indication of the one or more corrective actions. (see [0030]; Agerstam: “the system applicator 118 is provided with a description of an anomaly and determines how to modify operation of the monitored system 108 to correct the error detected by the anomaly detector 114.”)
The same motivation to combine Uneno, Taniai, Tsai, and Agerstam a set forth for Claim 4 equally applies to Claim 5.
Regarding Claim 18, the limitations in this claim is taught by the combination of Uneno, Taniai, Tsai, Agerstam, and O’Connor as discussed connection with claim 4.
Claim(s) 8-9, 11, 21, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Uneno in view of Taniai in view of Tsai in view of Yang (US20180194095A1 -hereinafter Yang).
Regarding Claim 8, the combination of Uneno, Taniai, and Tsai teaches all the limitations of claim 2 above; however, it does not explicitly teach wherein the manufacturing line comprises a bowl feeder.
Yang from the same or similar field of endeavor teaches wherein the manufacturing line comprises a bowl feeder. (see [0060]; Yang: “An sixth alternative of the first embodiment further comprises a bowl feeder capable of feeding a plurality of pins to the press head 15.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Uneno and Taniai to include Yang’s features of operating the at least one processor to pre-process the sequence of images comprises operating the at least one processor to align each image of the sequence of images. Doing so would issue an error message that the workpiece pair has a probable cracking event. (Yang, [0052])
Regarding Claim 9, the combination of Uneno, Taniai, Tsai, and Yang teaches all the limitations of claim 8 above, Yang further teaches comprising operating the at least one processor to classify the anomaly as at least one of an accumulation of workpieces within the bowl feeder, a misalignment of workpieces within the bowl feeder (see [0008]; Yang: “the controller unit is capable of detecting from at least one position alignment image a misalignment between the workpiece pair and issuing an error message that the alignment range is not met.”), or insufficient workpieces within a lower portion of the bowl feeder.
The same motivation to combine Uneno, Taniai, Tsai, and Yang set forth for Claim 8 equally applies to Claim 9.
Regarding Claim 11, the combination of Uneno and Taniai teaches all the limitations of claim 10 above; however, it does not explicitly teach wherein operating the at least one processor to pre-process the sequence of images comprises operating the at least one processor to align each image of the sequence of images.
Yang from the same or similar field of endeavor teaches wherein operating the at least one processor to pre-process the sequence of images comprises operating the at least one processor to align each image of the sequence of images. (see [0004]; Yang: “a position control camera capable of taking a plurality of position alignment images. The platform includes: (i) a base; (ii) an x-y stage including a floor with a central opening, a y-axis datum aligned along a first axis and an x-axis datum aligned along a second axis perpendicular to the first axis.”)
The same motivation to combine Uneno, Taniai, Tsai, and Yang set forth for Claim 8 equally applies to Claim 11.
Regarding Claim 21, the limitations in this claim is taught by the combination of Uneno, Taniai, Tsai, and Yang as discussed connection with claim 8.
Regarding Claim 22, the limitations in this claim is taught by the combination of Uneno, Taniai, Tsai, and Yang as discussed connection with claim 9.
Claim(s) 12 and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Uneno in view of Taniai in view of Naruse (US20210364447A1 -hereinafter Naruse).
Regarding Claim 12, the combination of Uneno and Taniai teaches all the limitations of claim 10 above; however, it does not explicitly teach wherein operating the at least one processor to pre-process the sequence of images comprises operating the at least one processor to: detect one or more moving workpieces in the sequence of images; segment each moving workpiece of the one or more moving workpieces in a first image of the sequence of images; select at least one moving workpiece of the one or more moving workpieces; and identify a region of interest for each selected moving workpiece in each image of the sequence of images.
Naruse from the same or similar field of endeavor teaches: wherein operating the at least one processor to pre-process the sequence of images comprises operating the at least one processor to:
detect one or more moving workpieces in the sequence of images; (see [0011]; Naruse: “an image-capturing unit which has a sensor that images at least one workpiece illuminated in a plurality of predetermined illumination light emission patterns and obtains a plurality of evaluation workpiece images that are associated with the predetermined illumination light emission patterns and the workpiece;”)
segment each moving workpiece of the one or more moving workpieces in a first image of the sequence of images; (see [0029]; Naruse: “by the image-capturing unit, imaging at least one workpiece illuminated in a plurality of predetermined illumination light emission patterns to acquire a plurality of evaluation workpiece images, each of which is associated with each predetermined illumination light emission pattern and each workpiece;”)
select at least one moving workpiece of the one or more moving workpieces; and (see [0108]; Naruse: “An image obtained by aligning, as column vectors, the evaluation workpiece images captured in these predetermined illumination light emission patterns is defined as fi, and a multi-channel image vector obtained by vertically aligning and organizing N (N patterns of) fi is defined as Expression (9) described below.”)
identify a region of interest for each selected moving workpiece in each image of the sequence of images. (see [0113]; Naruse: “Next, in Step S412 (image area setting step), the image area setting unit 142 extracts, for a combination of a plurality of different labels and at least one region of interest (ROI) with a predetermined shape (for example, a rectangular shape, a circular shape, or an oval shape), images in the predetermined region of interest from the evaluation workpiece images acquired in Step S410 on the basis of a user's instruction, for example, and sets image areas.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Uneno and Taniai to include Naruse’s features of operating the at least one processor to pre-process the sequence of images comprises operating the at least one processor to: detect one or more moving workpieces in the sequence of images; segment each moving workpiece of the one or more moving workpieces in a first image of the sequence of images; select at least one moving workpiece of the one or more moving workpieces; and identify a region of interest for each selected moving workpiece in each image of the sequence of images. Doing so would reduce missing of defects in workpieces and determination errors and thereby to improve inspection performance and inspection efficiency of workpieces. (Naruse, [0013])
Regarding Claim 23, the limitations in this claim is taught by the combination of Uneno, Taniai, and Naruse as discussed connection with claim 12.
Claim(s) 13 and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Uneno in view of Taniai in view of Langle et al. (US20200388042A1 -hereinafter Langle) in view of Greenwald et al. (US20200250455A1 -Greenwald).
Regarding Claim 13, the combination of Uneno and Taniai teaches all the limitations of claim 1 above; however, it does not explicitly teach comprising operating the at least one processor to: identify a plurality of images amongst the sequence of images showing a same moving workpiece of the one or more moving workpieces; select feature data associated with the moving workpiece comprising a position and a timing associated with the position of the moving workpiece in each image of the plurality of images; and apply the feature data associated with the moving workpiece to a regression model to determine the velocity of the moving workpiece.
Langle from the same or similar field of endeavor teaches comprising operating the at least one processor to:
identify a plurality of images amongst the sequence of images showing a same moving workpiece of the one or more moving workpieces; (see [0050]; Langle: “A plurality of images 23 a, 23 b, 23 c are recorded of the observation situation, which depict the movement of the object 21.”)
select feature data associated with the moving workpiece comprising a position and a timing associated with the position of the moving workpiece in each image of the plurality of images; and (see [0054]; Langle: “An object is observed at multiple points in time t. Every observation of an object is described by a feature vector ft.”)
…determine the velocity of the moving workpiece. (see [0019]; Langle: “apply the feature data associated with the moving workpiece to a regression model to determine the velocity of the moving workpiece.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Uneno and Taniai to include Langle’s features of identifying a plurality of images amongst the sequence of images showing a same moving workpiece of the one or more moving workpieces; selecting feature data associated with the moving workpiece comprising a position and a timing associated with the position of the moving workpiece in each image of the plurality of images, and determining the velocity of the moving workpiece. Doing so would detect with greatest accuracy for all observed camera positions and provide a cost-effective option to be able to determine at least one mechanical property of at least one object. (Langle, [0010] and [0096])
However, it does not explicitly teach: apply the feature data associated with the moving workpiece to a regression model to determine the velocity...
Greenwald from the same or similar field of endeavor teaches apply the feature data associated with the moving workpiece to a regression model to determine the velocity… (see [0068]; Greenwald: “processor 134 trains a machine learning model to determine a velocity factor for each of multiple attributes of a particular physical item. The machine learning model may use any of a variety of techniques such as decision trees, linear regression models, logistic regression models”. See [0079]: “Processor 134 can apply machine learning to determine the weights for each velocity factor.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Uneno, Taniai, and Langle to include Greenwald’s features of applying the feature data associated with the moving workpiece to a regression model to determine the velocity. Doing so would optimize a recommendation provided to source. (Greenwald, [0061])
Regarding Claim 24, the limitations in this claim is taught by the combination of Uneno, Taniai, Langle, and Greenwald as discussed connection with claim 13.
Claim(s) 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Uneno in view of Taniai in view of Langle in view of Greenwald in view of Ikeda et al. (US 20140301632 A1 -hereinafter Ikeda).
Regarding Claim 14, the combination of Uneno, Taniai, Langle, and Greenwald teaches all the limitations of claim 13 above; however, it does not explicitly teach comprising operating the at least one processor to reconstruct the motion of the moving workpiece across the plurality of images.
Ikeda from the same or similar field of endeavor teaches comprising operating the at least one processor to reconstruct the motion of the moving workpiece across the plurality of images. (see [0087]; Ikeda: “The "visual servo" is a method of continuously imaging a moving workpiece by a camera, and momentarily adjusting the direction and speed of the motion of the workpiece from the result of the image processing.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Uneno, Taniai, Langle, and Greenwald to include Ikeda’s features of operating the at least one processor to reconstruct the motion of the moving workpiece across the plurality of images. Doing so would realize positioning using image processing at higher speed and with higher accuracy. (Ikeda, [0009])
Regarding Claim 15, the combination of Uneno, Taniai, Langle, and Greenwald teaches all the limitations of claim 13 above; however, it does not explicitly teach comprising operating the at least one processor to detect and mask the moving workpiece within each image of the plurality of images.
Ikeda from the same or similar field of endeavor teaches comprising operating the at least one processor to detect and mask the moving workpiece within each image of the plurality of images. (see [0048]; Ikeda: “comprising operating the at least one processor to detect and mask the moving workpiece within each image of the plurality of images.”)
The same motivation to combine Uneno, Taniai, Langle, Greenwald, and Ikeda a set forth for Claim 14 equally applies to Claim 15.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yahashi (US12236576B2) discloses detecting a small surface defect with high accuracy.
Lavid Ben Lulu (US11669083B2) discloses identifying and repairing suboptimal operation of a machine.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VI N TRAN whose telephone number is (571)272-1108. The examiner can normally be reached Mon-Fri 9:00-5:00.
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/V.N.T./ Examiner, Art Unit 2117
/Christopher E. Everett/ Primary Examiner, Art Unit 2117