DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice of Preliminary Amendment
2. The Examiner acknowledges the amended claims filed on 01/05/2023.
- Claims 1-23 have been cancelled.
- Claims 24-43 have been added.
Miscellaneous
3. The Examiner contacted Applicant’s Representative, Elizabeth Iglesias (Reg. No. 70,659), on March 23th, 2026 to confirm that the amended claims filed on 01/05/2023 are the claims pending for examination. Attorney Iglesias further confirmed that the latest claims filed on 04/26/2026 are merely a copy of the original claims, initially filed on 01/05/2023, and they are to remain cancelled as set forth in the previously filed preliminary amendment (01/05/2023). Thus, claims 24-43 are presented for examination.
Priority
4. Receipt is acknowledged of certified copies of documents required by 37 CFR 1.55.
Information Disclosure Statement
5. The information disclosure statements (IDS) submitted on 03/01/2024, 07/10/2023 and 04/10/2023 are in compliance with the provisions of 37 CFR 1.97 and were considered by the examiner.
Specification
6. The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 102
7. 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.
8. 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)(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.
9. Claims 24, 32, 33, 41, 42 and 43 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wang et al. (US-PGPUB 2022/0207680).
Regarding claim 24, Wang discloses a method (see figs. 3, 16), applied to an electronic device (Terminal 100; see fig. 1 and paragraph 0134), wherein the method comprises:
displaying a first interface, wherein the first interface is a preview interface or a recording interface (The first deep learning network is a target deep learning network selected from a deep learning network resource pool based on first indication information. The first indication information is indication information that is obtained by analyzing a characteristic of a preview image obtained by a camera and that is related to an application scenario; see paragraphs 0045, 0047, 0097, 0225, 0239);
obtaining a brightness parameter of the electronic device, wherein the brightness parameter indicates an ambient brightness of a photographing environment in which the electronic device is located (The current application scenario is determined by analyzing the characteristic of the preview image. If the characteristic of the current preview image matches the dark light scenario, the AI controller selects the dark light deep learning network as a target deep learning network; see paragraph 0225);
obtaining a first image stream, wherein the first image stream is an image stream of first color space (Obtain raw images. The raw image is an RYYB image or an image including four different color components, i.e. RGGB, XYZW; see fig. 3 and paragraphs 0020-0021, 0058, 0156-0157);
determining, based on the ambient brightness, to perform first target processing (Performing color conversion on the first target image after determining the dark light scenario OR performing color format conversion of the second output image after determining the dark light scenario; see paragraphs 0020, 0232 and figs. 3, 16) or** second target processing on the first image stream to obtain a fifth image stream,
wherein the first target processing comprises first image processing, the first image processing is used to convert the first image stream into second color space (Before the performing at least one of brightness enhancement or color enhancement on the first target image to obtain a second target image, the method further includes: performing color conversion on the first target image to obtain an RGB color image; see figs. 3, 9 and paragraph 0020, 0058, 0132, 0168, 0197-0198 OR perform color format conversion on the second output image to obtain a target output image; see fig. 16 and paragraph 0232), the second target processing comprises second image processing and third image processing, the second image processing comprises downsampling and image enhancement, and the third image processing is used to convert an image stream obtained after the second image processing into the second color space; and
displaying or storing the fifth image stream (Send the second target image to a display for display or store the second target image in a storage unit; see fig. 3 and paragraph 0185. Obtain a target output image that can be displayed on a display; see fig. 16 and paragraph 0232).
Regarding claim 32, Wang discloses everything claimed as applied above (see claim 24). In addition, Wang discloses an algorithm of the image enhancement comprises at least one of the following: a denoising algorithm, a super-resolution algorithm, a deblurring algorithm, a contrast adjustment algorithm, a brightness adjustment algorithm, or a high dynamic range image algorithm (The functions of the first deep learning network further include super-resolution (SR) reconstruction; see paragraphs 0008-0009. The brightness enhancement includes contrast increase; see paragraphs 0030, 0061, 0181).
Regarding claim 33, Wang discloses an electronic device (Terminal 100; see fig. 1 and paragraph 0134), comprising: one or more processors (Processor 130; see fig. 1 and paragraph 0136); and a non-transitory memory (Memory 140; see fig. 1 and paragraph 0137), wherein the non-transitory memory is coupled to the one or more processors, the non-transitory memory is configured to store computer program code (Computer program instructions; see paragraph 0137), the computer program code comprises computer instructions that are executable by the one or more processors, causing the electronic device to:
display a first interface, wherein the first interface is a preview interface or a recording interface (The first deep learning network is a target deep learning network selected from a deep learning network resource pool based on first indication information. The first indication information is indication information that is obtained by analyzing a characteristic of a preview image obtained by a camera and that is related to an application scenario; see paragraphs 0045, 0047, 0097, 0225, 0239);
obtain a brightness parameter of the electronic device, wherein the brightness parameter indicates ambient brightness of a photographing environment in which the electronic device is located (The current application scenario is determined by analyzing the characteristic of the preview image. If the characteristic of the current preview image matches the dark light scenario, the AI controller selects the dark light deep learning network as a target deep learning network; see paragraph 0225);
obtain a first image stream, wherein the first image stream is an image stream of first color space (Obtain raw images. The raw image is an RYYB image or an image including four different color components, i.e. RGGB, XYZW; see fig. 3 and paragraphs 0020-0021, 0058, 0156-0157);
determine, based on the ambient brightness, to perform first target processing (Performing color conversion on the first target image after determining the dark light scenario OR performing color format conversion of the second output image after determining the dark light scenario; see paragraphs 0020, 0232 and figs. 3, 16) or** second target processing on the first image stream to obtain a fifth image stream,
wherein the first target processing comprises first image processing, the first image processing is used to convert the first image stream into second color space (Before the performing at least one of brightness enhancement or color enhancement on the first target image to obtain a second target image, the method further includes: performing color conversion on the first target image to obtain an RGB color image; see figs. 3, 9 and paragraph 0020, 0058, 0132, 0168, 0197-0198 OR perform color format conversion on the second output image to obtain a target output image; see fig. 16 and paragraph 0232), the second target processing comprises second image processing and third image processing, the second image processing comprises downsampling and image enhancement, and the third image processing is used to convert an image stream obtained after the second image processing into the second color space; and
display or store the fifth image stream (Send the second target image to a display for display or store the second target image in a storage unit; see fig. 3 and paragraph 0185. Obtain a target output image that can be displayed on a display; see fig. 16 and paragraph 0232).
Regarding claim 41, Wang discloses everything claimed as applied above (see claim 33). In addition, Wang discloses an algorithm of the image enhancement comprises at least one of** the following: a denoising algorithm, a super-resolution algorithm, a deblurring algorithm, a contrast adjustment algorithm, a brightness adjustment algorithm, or a high dynamic range image algorithm (The functions of the first deep learning network further include super-resolution (SR) reconstruction; see paragraphs 0008-0009. The brightness enhancement includes contrast increase; see paragraphs 0030, 0061, 0181).
Regarding claim 42, Wang discloses everything claimed as applied above (see claim 33). In addition, Wang discloses a photographing mode of the electronic device is a night mode, wherein the night mode is a mode in a camera application (The current application scenario is determined by analyzing the characteristic of the preview image. If the characteristic of the current preview image matches the dark light scenario, the AI controller selects the dark light deep learning network as a target deep learning network; see paragraph 0225. The plurality of deep learning networks having different functions include: a deep learning network in a dark light scenario and a deep learning network in a night mode; see paragraphs 0223, 0090).
Regarding claim 43, Wang discloses a non-transitory computer-readable storage medium (Memory 140; see fig. 1 and paragraph 0137), wherein the non-transitory computer-readable storage medium stores a computer program (Computer program instructions; see paragraph 0137) that is executable by a processor (Processor 130; see fig. 1 and paragraphs 0136-0137), and when the computer program is executed by the processor, an electronic device (Terminal 100; see fig. 1 and paragraph 0134) is enabled to perform the following steps:
displaying a first interface, wherein the first interface is a preview interface or a recording interface (The first deep learning network is a target deep learning network selected from a deep learning network resource pool based on first indication information. The first indication information is indication information that is obtained by analyzing a characteristic of a preview image obtained by a camera and that is related to an application scenario; see paragraphs 0045, 0047, 0097, 0225, 0239);
obtaining a brightness parameter of the electronic device, wherein the brightness parameter indicates ambient brightness of a photographing environment in which the electronic device is located (The current application scenario is determined by analyzing the characteristic of the preview image. If the characteristic of the current preview image matches the dark light scenario, the AI controller selects the dark light deep learning network as a target deep learning network; see paragraph 0225);
obtaining a first image stream, wherein the first image stream is an image stream of first color space (Obtain raw images. The raw image is an RYYB image or an image including four different color components, i.e. RGGB, XYZW; see fig. 3 and paragraphs 0020-0021, 0058, 0156-0157);
determining, based on the ambient brightness, to perform first target processing (Performing color conversion on the first target image after determining the dark light scenario OR performing color format conversion of the second output image after determining the dark light scenario; see paragraphs 0020, 0232 and figs. 3, 16) or** second target processing on the first image stream to obtain a fifth image stream,
wherein the first target processing comprises first image processing, the first image processing is used to convert the first image stream into second color space (Before the performing at least one of brightness enhancement or color enhancement on the first target image to obtain a second target image, the method further includes: performing color conversion on the first target image to obtain an RGB color image; see figs. 3, 9 and paragraph 0020, 0058, 0132, 0168, 0197-0198 OR perform color format conversion on the second output image to obtain a target output image; see fig. 16 and paragraph 0232), the second target processing comprises second image processing and third image processing, the second image processing comprises downsampling and image enhancement, and the third image processing is used to convert an image stream obtained after the second image processing into the second color space; and
displaying or storing the fifth image stream (Send the second target image to a display for display or store the second target image in a storage unit; see fig. 3 and paragraph 0185. Obtain a target output image that can be displayed on a display; see fig. 16 and paragraph 0232).
**Note: The U.S. Patent and Trademark Office considers Applicant’s “or”, “one of”, and “at least one of” language to be anticipated by any reference containing one of the preceding or subsequent corresponding elements.
Allowable Subject Matter
10. Claims 25-31 and 34-40 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all the limitations of the base claim and any intervening claims.
Regarding claim 25, the specific limitation of “wherein determining, based on the ambient brightness, to perform first target processing or second target processing on the first image stream to obtain the fifth image stream comprises: performing the second target processing on the first image stream when the ambient brightness is less than or equal to a first brightness threshold, to obtain the fifth image stream; performing the first target processing and the second target processing on the first image stream when the ambient brightness is greater than a first brightness threshold and less than a second brightness threshold, to obtain the fifth image stream; or performing the first target processing on the first image stream when the ambient brightness is greater than or equal to a second brightness threshold, to obtain the fifth image stream” in the combination as claimed is neither anticipated nor made obvious over the prior art made of record.
Regarding claims 26-31, it is objected to for depending on claim 25.
Regarding claim 34, the specific limitation of “wherein determining, based on the ambient brightness, to perform first target processing or second target processing on the first image stream to obtain the fifth image stream comprises: performing the second target processing on the first image stream when the ambient brightness is less than or equal to a first brightness threshold, to obtain the fifth image stream; performing the first target processing and the second target processing on the first image stream when the ambient brightness is greater than a first brightness threshold and less than a second brightness threshold, to obtain the fifth image stream; or performing the first target processing on the first image stream when the ambient brightness is greater than or equal to a second brightness threshold, to obtain the fifth image stream” in the combination as claimed is neither anticipated nor made obvious over the prior art made of record.
Regarding claims 35-40, it is objected to for depending on claim 34.
Contact Information
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CYNTHIA CALDERON whose telephone number is (571)270-3580. The examiner can normally be reached M-F 9:00 AM-5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TWYLER HASKINS can be reached at (571)272-7406. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CYNTHIA CALDERON/Primary Examiner, Art Unit 2639 03/25/2026
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