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.
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) 1-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou (CN 106841209 A) in view of Yao (CN 109064459 A).
-Regarding claim 1, Zhou discloses a textile package production system (FIG. 1-3) comprising: an imager configured to generate an optical image for a textile package, the imager having at least one optical detector and an optical emitter, the imager having an inspection region (imaging unit 11, 13, 15; illumination unit 12, 14, 16; inspection region 3, FIG. 1); a transporter having a test subject carrier configured for relative movement as to the carrier and the inspection region (transmitting unit 2, FIG. 1); a sorter coupled to the transporter and configured to make a selection as to a first classification and a second classification (sorting unit 4, FIG. 1).
Zhou is silent to teaching that a controller having a processor and a memory, the controller coupled to the imager, the transporter, and the sorter and configured to implement an artificial engine classifier in which the sorter is controlled based on the optical image and based on instructions and training data in the memory. However, the claimed limitation is well known in the art as evidenced by Yao.
In the same field of endeavor, Yao teaches a controller having a processor and a memory, the controller coupled to the imager, the transporter, and the sorter and configured to implement an artificial engine classifier in which the sorter is controlled based on the optical image and based on instructions and training data in the memory (the convolutional neural networks Fabric Defect detector that will become trained at function are loaded into host computer, are placed in automatic assembly line, S5).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Zhou with the teaching of Yao in order to improve detection accuracy and automation with predictable results (KSR: applying a known deep-learning defect-classification technique to a similar optical-inspection/sorting device).
-Regarding claim 2, the combination further discloses the controller is configured to implement a neural network (Zhou, neural network, paragraph 147; Yao, convolutional neural network, abstract).
-Regarding claim 3, the combination further discloses the controller is configured to implement a regression calculation (Yao, see S4-S5).
-Regarding claim 4, the combination further discloses the imager is configured to generate a two- dimensional view (Zhou, camera, abstract).
-Regarding claim 5, the combination further discloses the controller is configured to generate a bounding box in the two-dimensional view (Yao, S7.2, it is generated with RPN network and suggests window, every picture generates about 300 suggestion window).
-Regarding claim 6, the combination further discloses the controller is configured to generate a prediction corresponding to the bounding box (Yao, S7.4, each ROI is made to generate fixed-size characteristic pattern by the pond ROI layer; S7.5, it is returned using detection class probability and detection frame to class probability and frame probability joint training).
-Regarding claim 7, the combination further discloses the at least one optical detector includes a camera (Zhou, camera, abstract).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PING Y HSIEH whose telephone number is (571)270-3011. The examiner can normally be reached Monday-Friday, 9am-4pm.
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/PING Y HSIEH/Primary Examiner, Art Unit 2664
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