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
Claim objection is withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 6 recites the limitation " the iToF camera" in line 2. There is insufficient antecedent basis for this limitation in the claim. This limitation is introduced in claim 4 however claim 6 depends on claim 5 and neither of claim 5 nor 1 introduces an “iToF camera”. This rejection is sustained.
Rejection of claim 19 is withdrawn.
Claim Rejections - 35 USC § 101
The 101 rejections are withdrawn,
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) below are is/are rejected under 35 U.S.C. 103 as being unpatentable over Su, Shuochen, et al. "Deep end-to-end time-of-flight imaging." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018 in view of Yin, Wei, et al. "Temporal phase unwrapping using deep learning." Scientific reports 9.1 (2019): 20175.
Referring to claims 1 and 21, Su shows an electronic device comprising circuitry (see introduction) configured to unwrap a depth map or phase image by means of an artificial intelligence algorithm to obtain an unwrapped depth map (see abstract).
However fails to show Su shows the artificial intelligence algorithm is configured to determine wrapping indexes from the depth map or phase image in order to obtain an unwrapped depth map (see page 6389 left column note the effect of inputs as well as page 6385 right column equation 4).
Yin shows a similar device that includes the artificial intelligence algorithm is configured to determine wrapping indexes from the depth map or phase image in order to obtain an unwrapped depth map (see the multi-frequency temporal phase unwrapping (MF-TUP) with equation 3 ranging from -π to π also see figure 1 of the deep learning network configured to generate the phase-to-height mapping where the deep neural network performs the DL-TUP). It would have been obvious to include the wrapping indexes from depth map or phase image using a deep learning neural network as shown by Yin because the error rate for the phase to height and 3D model is lower when performed in the DL-TUP as shown by figure 2b.
Referring to claim 3, Su shows the circuitry is configured to perform unwrapping based on the wrapping indexes and an unambiguous operating range of an indirect Time-of-Flight (iToF) camera to obtain the unwrapped depth map (see the abstract note the use of iToF cameras inherently have an unambiguous range).
Referring to claim 4, Su shows a depth map or phase image is obtained by an indirect Time-of-Flight (iToF) camera (see abstract).
Referring to claim 5, Su shows the artificial intelligence algorithm further uses side-information to obtain an unwrapped depth map (see page 6386 right column last paragraph to section 4.1).
Referring to claim 6, Su shows the side-information is an amplitude image obtained by the iToF camera (see page 6386 right column last paragraph to section 4.1).
Referring to claim 7, Su shows the side-information is obtained by one or more other sensing modalities (see section 5 page 6387).
Referring to claim 8, Su shows the side information is a color image (see page 6383 note the left column under introduction).
Referring to claim 9, Su shows the electronic device comprises an iToF camera (see abstract).
Referring to claim 10, Su shows the artificial intelligence is applied on a stream of depth maps and/or amplitude images (see figure 5).
Referring to claim 11, Su shows the circuitry is further configured to perform pre-processing on the depth map or phase image (see page 6385 note section 3 the depth acquisition).
Referring to claim 12, Su shows the circuitry is further configured to perform pre-processing on the side information (see abstract, note the normalization).
Referring to claim 13, Su shows the pre-processing comprising segmentation colorspace changes, denoising, normalization, filtering, and/or contrast enhancement (see abstract, note the normalization).
Referring to claim 15, Su shows the artificial intelligence algorithm is implemented as an artificial neural network (see abstract).
Referring to claim 16, Su shows the artificial neural network is a convolutional neural network (see abstract).
Referring to claim 20, Su shows the artificial intelligence algorithm is trained with reference data obtained by an iToF simulation (see page 6387 under section 5.1).
Referring to claim 24, Su shows a method of generating an unwrapped depth map comprising:
obtaining a depth map from an iToF camera (see page 6385 under depth acquisition);
obtaining an amplitude image from the iToF camera (see page 6386 left column);
performing denoising on the depth map and the amplitude image to obtain denoised depth map and denoised amplitude image (see page 6386 left column under section 4.1);
apply, by circuitry an artificial neural network on the denoised depth map and the denoised amplitude image to obtain wrapping indexes (see abstract);
performing unwrapping based on the wrapping indexes to obtain an unwrapped depth map (see the rejection of claim 1 Su in view of Yin).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Su, Shuochen, et al. "Deep end-to-end time-of-flight imaging." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018 in view of Yin, Wei, et al. "Temporal phase unwrapping using deep learning." Scientific reports 9.1 (2019): 20175 and Tanaka (20140340515). Referring to claim 14, Shuochen fails to show but Tanaka shows performing colorspace changes, image segmentation on a color image, or applying color or contrast equalization to an amplitude image (see paragraph 67). It would have been obvious to include the pre-processing as shown by Tanaka because Shuochen shows the use of a color iTof camera and one of ordinary skill in the art would realize that pre-processing of the color images is needed.
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Su, Shuochen, et al. "Deep end-to-end time-of-flight imaging." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018 in view of Yin, Wei, et al. "Temporal phase unwrapping using deep learning." Scientific reports 9.1 (2019): 20175 and Cossairt (20190301857).
Referring to claim 19, Shuochen shows a ground truth device (see page 6384 left column above related work). However fails to specifically show a ground truth device is a LIDAR.
Cossairt shows a similar device that includes ground truth device is a LIDAR scanner (see paragraph 93 and 111). It would have been obvious to include the ground truth device is a LIDAR scanner because this is extremely well known and adds no new or unexpected results.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUKE D RATCLIFFE whose telephone number is (571)272-3110. The examiner can normally be reached M-F 9:00AM-5:00PM EST.
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/LUKE D RATCLIFFE/Primary Examiner, Art Unit 3645
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