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 .
Claims 1-15 are pending.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chou, et al. (“QR Code Detection Using Convolutional Neural Networks”, published in the 2015 International Conference on Advanced Robotics and Intelligent Systems (ARIS) on May 29-31, 2015, herein Chou).1 2 Regarding claims 1 and 15, Chou teaches a method and optoelectronic code reader of reading optical codes, said method and code reader comprising the steps recording an image (Section II: input images); locating code zones in the image (Section II-C)); and decoding the optical codes in the code zones (Section II-C), wherein the locating of code zones has a first segmentation process with machine learning by which first candidates for code zones are found (Section II-D), wherein the first candidates are evaluated to determine parameters for the locating of code zones (Section II-D) and/or3 the decoding of the optical codes. Regarding claim 2, Chou teaches the first segmentation process generates a first result map, with a result map being an image of lower resolution than the recorded image whose pixels comprise information on whether a code zone has been recognized at the location of the pixel (Section II-B). Regarding claim 3, Chou teaches the first segmentation process has a neural network (Section II-B). Regarding claim 4, Chou teaches the neural network is a convolution neural network (Section II-B). Regarding claim 5, Chou teaches the locating of code zones comprises a second segmentation process of classical image processing without machine learning by which second candidates for code zones are found (Section II-B). Regarding claim 6, Chou teaches a contrast threshold for the second segmentation process is determined from the evaluation of the first candidates (Section II-D). Regarding claim 7, Chou teaches the contrast threshold is locally determined (Section II-D). Regarding claim 8, Chou teaches the contrast threshold is locally determined per environment of a first candidate (Section II-D). Regarding claim 9, Chou teaches wherein a segmentation mode is determined from the evaluation of the first candidates (Section II-D). Regarding claim 10, Chou teaches one of the following segmentation modes is determined: use the first candidates as code zones (Section II-D); use the second candidates as code zones (Section II-D); only use those code zones that are both first candidates and second candidates (Section II-D); use code zones that are first candidates or second candidates (Section II-D). Regarding claim 11, Chou teaches a ratio of the number of first candidates to the number of second candidates is used as the criterion for determining the segmentation mode (Section II-D). Regarding claim 12, Chou teaches a filter that initially excludes located code zones before the decoding is determined from the evaluation of the first candidates (Section II-B). Regarding claim 13, Chou teaches the filter checks whether the code zone has a light background (Section II-B) and/or4 quiet zones of an optical code. Regarding claim 14, Chou teaches the locating of code zones comprises a second segmentation process of classical image processing without machine learning by which second candidates for code zones are found and wherein a ratio of the number of first candidates to the number of second candidates is used as the criterion for determining the filter (Section II-D).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW MIKELS whose telephone number is (571)270-5470. The examiner can normally be reached Monday to Thursday 7:00 AM ET - 4:30 PM ET, Friday 7:00 AM ET - 11:00 AM ET, the Examiner is on central time.5
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael G Lee can be reached at 571-272-2398. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW MIKELS/Primary Examiner, Art Unit 2876
1 See 892 form for the full citation. A copy of this reference is attached to this Office Action.
2 In addition to the cited portions, please see also the associated figures.
3 Note that under the broadest reasonable interpretation, MPEP § 2111, “and/or” is construed as requiring only the “or”, i.e. requiring these two limitations in the alternative.
4 See construction of the “and/or” in claim 1 above.
5 The Examiner can also be reached at matthew.mikels@uspto.gov.