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
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over
Cricri et al (US 2024/0289590 and hereafter referred to as “Cricri”) in view of Amirpour et al (“DeepStream Video Streaming Enhancements using Compressed Deep Neural Networks,” cited in IDS filed 05/21/2025 and hereafter referred to as “Amirpour”)
Regarding Claim 1, Cricri discloses an electronic device comprising:
a communication circuit (Page 5, paragraph 0194);
memory, comprising one or more storage media, storing instructions (Page 10, paragraph 0241-0243); and
one or more processors communicatively coupled to the communication circuit and the memory, wherein the instructions, when executed by the one or more processors individually or collectively (Page 10, paragraph 0241-0243), cause the electronic device to:
determine information on a scalability applicable to an original image (Page 8, paragraph 0226),
generate a base layer (BL) image for the original image(Page 8, paragraph 0214),
a deep neural network (Page 13, paragraph 0285);
compress the BL image, and control the communication circuit to transmit a bitstream including the compressed BL image (Figure 5).
Cricri does not disclose explicitly generating a base layer image for the original image, based on at least one of a resolution, a color gamut, a gamma, a frame rate, a bit depth, or a bit rate of the original image, generate at least one artificial intelligence (AI) network, based on the original image, the information on the scalability for the original image, and the BL image, and compress the BL image, and control the communication circuit to transmit a bitstream including the compressed BL image and configuration information for the at least one AI network to an external electronic device.
Amirpour discloses determine information on a scalability applicable to an original image (Page 5, § III, quality of bitrate resolution pairs), generating a base layer image for the original image, based on at least one of a resolution, a color gamut, a gamma, a frame rate, a bit depth, or a bit rate of the original image (Page 5, § III, 3)- 4), Figure 2), generate at least one artificial intelligence (AI) network, based on the original image, the information on the scalability for the original image, and the BL image (Page 5, § III, 3)- 4)), and compress the BL image, and control the communication circuit to transmit a bitstream including the compressed BL image and configuration information for the at least one AI network to an external electronic device (Page 5, § III, 3)- 4), Page 2, § I). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Cricri to include the missing limitations as taught by Amirpour in order to deploy highly efficient upscaling method (Page 5, § III, 3) as disclosed by Amirpour.
Regarding Claim 7, Cricri discloses an electronic device comprising:
a communication circuit (Page 21, paragraph 0404-0406);
memory, comprising one or more storage media, storing instructions (Page 21, paragraph 0404-0406); and
one or more processors communicatively coupled to the communication circuit and the memory, wherein the instructions, when executed by the one or more processors individually or collectively, cause the electronic device to:
receive, from an external electronic device, a bitstream including a compressed base layer (BL) image and configuration information for at least one artificial intelligence (AI) network (Figure 5, Page 13, paragraph 0285),
determine at least one scalability based on the configuration information for the at least one AI network (Page 8, paragraph 0226),
configure at least one Al network for the at least one scalability, and
obtain an enhanced layer (EL) image using the BL image (Page 7, paragraph 0213-0214).
Cricri does not disclose explicitly configure at least one Al network for the at least one scalability, and obtain an enhanced layer (EL) image using the BL image and the at least one AI network.
Amirpour discloses de configure at least one Al network for the at least one scalability, and obtain an enhanced layer (EL) image using the BL image and the at least one AI network (Page 5, § III, quality of bitrate resolution pairs, Page 4, § II, B). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Cricri to include the missing limitations as taught by Amirpour in order to deploy highly efficient upscaling method (Page 5, § III, 3) as disclosed by Amirpour.
Regarding Claim 13, Cricri discloses a method performed by an electronic device, the method comprising:
determining information on a scalability applicable to an original image (Page 8, paragraph 0226),
generating a base layer (BL) image for the original image(Page 8, paragraph 0214),
a deep neural network (Page 13, paragraph 0285);
compressing the BL image, and control the communication circuit to transmit a bitstream including the compressed BL image (Figure 5)
Cricri does not disclose explicitly generating a base layer image for the original image, based on at least one of a resolution, a color gamut, a gamma, a frame rate, a bit depth, or a bit rate of the original image, generate at least one artificial intelligence (AI) network, based on the original image, the information on the scalability for the original image, and the BL image, and compress the BL image, and control the communication circuit to transmit a bitstream including the compressed BL image and configuration information for the at least one AI network to an external electronic device.
Amirpour discloses determining information on a scalability applicable to an original image (Page 5, § III, quality of bitrate resolution pairs), generating a base layer image for the original image, based on at least one of a resolution, a color gamut, a gamma, a frame rate, a bit depth, or a bit rate of the original image (Page 5, § III, 3)- 4), Figure 2), generating at least one artificial intelligence (AI) network, based on the original image, the information on the scalability for the original image, and the BL image (Page 5, § III, 3)-4)), and compressing, by the electronic device, the BL image, and transmitting, by the electronic device, a bitstream including the compressed BL image and configuration information for the at least one AI network (Page 5, § III, 3)- 4), Page 2, § I). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Cricri to include the missing limitations as taught by Amirpour in order to deploy highly efficient upscaling method (Page 5, § III, 3) as disclosed by Amirpour.
Regarding Claim 2, 8 and 14, Cricri and Amirpour disclose all the limitations of Claim 1, 7 and 13 respectively. Cricri discloses wherein the configuration information for the at least one AI network includes at least one of information on a scalability ID, information on an AI network configuration, or information on operators in the AI network configuration (paragraph 0351, 0393).
Regarding Claim 3, 9 and 15, Cricri and Amirpour disclose all the limitations of Claim 2, 8 and 14 respectively. Cricri discloses wherein the information on the AI network configuration includes at least one of a number of channels, a layer configuration, or a filter for each layer (paragraph 0351, 0393).
Regarding Claim 4, 10 and 16, Cricri and Amirpour disclose all the limitations of Claim 2, 8 and 14 respectively. Cricri discloses wherein the information on the operators in the AI network configuration includes at least one of filter information, an activation function, or an optimizer (paragraph 0351).
Regarding Claim 5, 11 and 17, Cricri and Amirpour disclose all the limitations of Claim 1, 7 and 13 respectively. Cricri discloses wherein the bitstream includes sequence parameter set (SPS) data, picture parameter set (PPS) data, and one or more supplemental enhancement information (SEI) data (paragraph 0228).
Regarding Claim 6, 12 and 18, Cricri and Amirpour disclose all the limitations of Claim 1, 7 and 13 respectively. Cricri discloses wherein the scalability is any one of spatial scalability, temporal scalability, signal-to-noise ratio (SNR) scalability, a hybrid codec, color gamut scalability, and high dynamic range (HDR) scalability (paragraph 0257, 0356, 0361)
Conclusion
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/FARZANA HOSSAIN/Primary Examiner, Art Unit 2482
August 8, 2026
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