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 § 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lew et al (US 10594338).
As to claim 1, Lew discloses a neural network-based image processing method (FIGS. 3A-4B), comprising:
acquiring a feature tensor from an input image by using a first neural network including a plurality of neural network layers (FIG. 4A, tensor 320; see col. 3, lines 53-60);
acquiring a quantized feature tensor by quantizing the acquired feature tensor based on a quantization size (FIG. 4A, quantized tensor 435 and alphabet size data 430; col. 9, lines 51-65); and
generating a bitstream by performing entropy encoding on the quantized feature tensor (FIGS. 3A-4A, bitstream 340 generated by entropy coder 245),
wherein the quantization size is adaptively derived based on predefined encoding information (col. 4, lines 1-8 and 64-67; col. 5, lines 1-13).
As to claim 2, Lew further discloses wherein the quantization size is adaptively derived based on at least one of the acquired feature tensor (col. 4, lines 1-37; col. 9, lines 39-50; col. 12, lines 14-18), a target bit rate, or distribution information.
As to claims 3-4 and 14, the features of these claims are directed to “wherein the quantization size is adaptively derived based on distribution information” which is not required based on the BRI of claim 2 (see rejection of claim 2). The features of these claims are therefore not required.
As to claims 5-12, the features of these claims are directed to “wherein the quantization size is adaptively derived based a target bit rate” which is not required based on the BRI of claim 2 (see rejection of claim 2). The features of these claims are therefore not required.
As to claim 13, Lew further discloses wherein the bitstream is generated by performing an Asymmetric Numeral System (ANS)-based entropy encoding on the quantized feature tensor based on a predefined probability table (see col. 6, lines 65-67).
As to claim 15, Lew discloses a neural network-based image processing device (FIG. 2A), comprising:
a processor configured to control the image processing device (col. 13, lines 55-67); and
a memory connected to the processor, the memory being configured to store data (col. 13, lines 55-67),
wherein the processor is configured to:
acquire a feature tensor from an input image by using a first neural network including a plurality of neural network layers (FIG. 4A, tensor 320; see col. 3, lines 53-60),
acquire a quantized feature tensor by quantizing the acquired feature tensor based on a quantization size (FIG. 4A, quantized tensor 435 and alphabet size data 430; col. 9, lines 51-65),
generate a bitstream by performing entropy encoding on the quantized feature tensor (FIGS. 3A-4A, bitstream 340 generated by entropy coder 245),
wherein the quantization size is adaptively derived based on predefined encoding information (col. 4, lines 1-8 and 64-67; col. 5, lines 1-13).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BOUBACAR ABDOU TCHOUSSOU whose telephone number is (571)272-7625. The examiner can normally be reached M-F 8am-4pm.
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/BOUBACAR ABDOU TCHOUSSOU/Primary Examiner, Art Unit 2482
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