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 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) 1-2, 10-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Matsumura (US 2021/0004683) in view of Muller (US 2002/0017212).
Regarding to claims 1, 12, 16-17:
Matsumura discloses a computer implemented method for controlling a winding machine, the winding machine comprising at least a winder (FIG. 7, element 302) and a rewinder (FIG. 7, element 500), the method comprising:
determining an actual velocity of the winder during operation of the winding machine (FIG. 10: Web transport velocity);
utilizing the determined actual velocity as a winder-related feature and as an input for a trained machine learning algorithm (FIG. 10 shows the web transport velocity as an input of training data); and
executing the machine learning algorithm based on the winder-related feature and issuing an anomaly indicator as an output (FIG. 14 shows the learning model 1034 predicting the winding defect level).
Matsumura however does not teach processing the actual velocity to extract a winder-related feature by subtracting a command velocity of the winder from the actual velocity to determine an envelope signal and filtering the envelope signal to preserve an amplitude-related information as an input for the machine learning algorithm to issue an anomaly indicator.
Muller discloses a method for detecting faults during transport of a web in a web-fed machine (Abstract), comprising measuring an actual speed, determining the difference (subtraction) of the measured actual speed from a desired speed (command speed), and based on the determined difference to detect faults (FIG. 4, steps 46, 50, 52, and 54), wherein the amount (envelop) of such difference reads on the claimed amplitude-related information.
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify Matsumura’s method for detecting the anomality based on the difference amount between the measured actual speed and the desired speed, rather than only on the measured actual speed, as disclosed by Muller to gain the reliability of the detection. The modification, therefore, would produce of using the amount of the difference as an input to train and execute the machine learning model.
Regarding to claim 2: further comprising initiating an amendment of at least one control parameter of the winding machine in an event of an indicated anomaly (Muller: FIG. 4, steps 56-58).
Regarding to claims 10-11, 13-14: wherein the machine learning algorithm is pre-trained based on a supervised/unsupervised training method, wherein an unsupervised training method is utilized to identify clusters and to determine an anomaly degree for input data based on a corresponding cluster, wherein a supervised training method is utilizing to identify classes based on labeled training data sets and to determine an anomaly degree for input data based on a corresponding class (paragraph [0141]: The machine learning model correlates the input and the output in the supervised training method. FIG. 12 shows the machine learning model trained in the unsupervised method. In addition, it is conventional for training a machine learning model either in supervised or unsupervised mode. Wherein in the supervised mode, classes and labels are used as inputs and outputs correlated to each other. In the unsupervised mode, clustering is a conventional method for processing data in the training data to determine the data patterns or structure).
Regarding to claim 15: wherein the winding machine in a training phase comprises a web tension sensor; and wherein labeled data is generated depending on values of the web tension sensor (Matsumura: FIG. 7: Web tension sensor 314 and FIG. 3: Web tension is an input data of the training data).
Allowable Subject Matter
Claims 3-9 are 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.
Regarding to claims 3-4: The primary reasons for the indication of the allowability of the claims is the inclusions therein, in combination as currently claimed, of the limitation that wherein the winding machine further comprises a web accumulator, and a web-accumulator-related feature is extracted from the web accumulator actual position and is utilized as an additional input for the trained machine learning algorithm and the machine learning algorithm is executed based on the winder-related feature and the web-accumulator-related feature is neither disclosed nor taught by the cited prior art of record, alone or in combination.
Claims 5-9 are allowed because they depend on claim 3.
CONTACT INFORMATION
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAM S NGUYEN whose telephone number is (571)272-2151.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, DOUGLAS RODRIGUEZ, can be reached on 571-431-0716. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LAM S NGUYEN/ Primary Examiner, Art Unit 2853
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