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 .
Application Status
This office action is responsive to the Application No.:18/619,527 filed on 03/28/2024.
Claims 1-9 and 19-29 are pending and presented for examination. Claims 10-18 has been canceled.
This action has been made NON-FINAL.
Examiner Remarks
In the spirit of compact prosecution, Applicant is requested to contact the Examiner for an interview to discuss the inventive concepts of the instant application. Applicant may optionally amend the claims to further direct the claims toward a particular inventive concept described in the specification without an interview.
Additionally, the prior art rejection (if applicable) cites particular paragraphs, columns, and/or line numbers in the references for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/26/2024 is being considered by the examiner. A signed IDS is hereby attached.
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)(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) 1-5, 19, 21, 22, 24, 25 and 29 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Yu, US 20240112088.
Claim 1:
Yu discloses a device (See Abstract; Summary of Invention) comprising:
a decoder (See Yu Figure 1, Items 24; Paragraphs 0070-0074) implemented in hardware and configured to generate a decoded digital image (See Yu Figure 1, Items 24; Paragraphs 0070-0074) from an encoded digital image (See Yu Figure 1, Items 16; Paragraphs 0070-0074);
and a renderer implemented in hardware and configured to reconstruct a digital image (See Yu Paragraph 0090) from the decoded digital image (See Yu Figure 1, Items 24; Paragraphs 0070-0074) by rendering the decoded digital image (See Yu Figure 1; Paragraphs 00261-0033; 0070-0074) using a machine-learning model (See Yu Paragraphs 0008-0011; 0025-0028; 0144-0145).
Claim 2:
Yu discloses wherein the digital image is panoramic as capturing a plurality of viewpoints of an environment (See Yu Paragraph 01072) and the renderer is configured to adjust a respective said viewpoint with respect to the environment captured by the digital image (See Yu Paragraph 01073).
Claim 3:
Yu discloses a sensor implemented in hardware to detect movement and wherein the renderer is configured to adjust the respective said viewpoint (See Yu Paragraph 01074) based on the detected movement (See Yu Paragraphs 0147; 0151).
Claim 4:
Yu discloses wherein the machine-learning model (See Yu Paragraphs 0008-0011; 0025-0028; 0144-0145) is configured to reconstruct high dynamic range pixels of the digital image (See Yu Paragraph 0055; 0065; 0090; 0104) from standard dynamic range pixels (See Yu Paragraph 0055; 0065; 0104) included in the encoded digital image (See Yu Figure 1, Items 16; Paragraphs 0070-0074).
Claim 5:
Yu discloses wherein the machine-learning model (See Yu Paragraphs 0008-0011; 0025-0028; 0144-0145) is configured to reconstruct illumination with respect to one or more objects in an environment captured by the digital image (See Yu Paragraph 0090).
Claims 19 and 21:
Claims 19 and 21 are rejected on the same basis as claim 1.
Claim 22:
Yu discloses communicating, by the first device, client capability data to the second device, the client capability data describing machine-learning functionality supported by the first device, the encoded digital image based on the client capability data (See Yu Paragraphs 0008-0011; 0025-0028; 0144-0145).
Claim 24 and 25:
Claims 24 and 25 are rejected on the same basis as claims 4 and 5.
Claim 29:
Claim 29 is rejected on the same basis as claim 2.
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) 6-9, 20, 23, 26 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Yu, US 20240112088, in view of Cerny, US 20210241415.
Claim 6:
Yu failed to disclose cast a transient illumination effect back into the environment. However, Cerny discloses this feature in paragraph 0079. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have further modified Yu by the teachings of Cerny to enable improved graphic processing when rendering an image, more effectively (See Cerny Technical Field of Invention). In addition, both of the references teach features that are directed to analogous art and they are directed to the same field of endeavor, such as graphic processing. This close relation between both references highly suggests an expectation of success.
As modified:
The combination of Yu and Cerny discloses wherein the machine-learning model (See Yu Paragraphs 0008-0011; 0025-0028; 0144-0145) is configured to reconstruct the illumination using image-based lighting (IBL) or cast a transient illumination effect back into the environment (See Cerny Paragraph 0079).
Claim 7:
Yu failed to disclose a geometry buffer. However, Cerny discloses this feature in paragraph 0098. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have further modified Yu by incorporating geometry buffer, as taught by Cerny, to enable improved graphic processing when rendering an image, more effectively (See Cerny Technical Field of Invention). In addition, both of the references teach features that are directed to analogous art and they are directed to the same field of endeavor, such as graphic processing. This close relation between both references highly suggests an expectation of success.
As modified:
The combination of Yu and Cerny discloses wherein the machine-learning model (See Yu Paragraphs 0008-0011; 0025-0028; 0144-0145) is configured to reconstruct one or more geometry buffer assets from a geometry buffer configured to store geometric data of one or more objects in an environment captured by the digital image (See Cerny Paragraph 0098).
Claim 8:
The combination of Yu and Cerny wherein the one or more geometry buffer assets define albedo, normal vectors, depth, or secularity of the one or more objects in the environment (See Cerny Paragraph 0098).
Claim 9:
The combination of Yu and Cerny wherein the machine-learning model (See Yu Paragraphs 0008-0011; 0025-0028; 0144-0145) is configured to compute shading in the environment captured by the digital image using the one or more geometry buffer assets (See Cerny Paragraph 0098).
Claim 20:
Claim 20 is rejected on the same basis as claim 6.
Claim 23:
Claim 23 is rejected on the same basis as claim 9.
Claims 26 and 27:
Claims 26 and 27 are rejected on the same basis as claims 6 and 7.
Claim(s) 28 is rejected under 35 U.S.C. 103 as being unpatentable over Yu, US 20240112088, in view of Cerny, US 20210241415 and in further view of Kreis, US 20250111588.
Claim 28:
The combination of Yu and Cerny failed to disclose path tracing using generative artificial intelligence. However, Kreis discloses this feature in paragraph 0025. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have further modified Yu and Cerny by the teachings of Kreis to generate new image using generative AI, more effectively (See Kreis Abstract).
As modified:
The combination of Yu, Cerny and Kreis discloses wherein the encoded digital image is configured using path tracing and the machine-learning functionality is to smooth the path tracing using generative artificial intelligence (Kreis Paragraph 0025).
Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fortin, US 20100135379 discloses a method and a system for encoding and decoding a digital image frame.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHEREE N BROWN whose telephone number is (571)272-4229. The examiner can normally be reached M-F 5:30-2:00 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SAID BROOME can be reached at (571) 272-2931. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHEREE N BROWN/Primary Examiner, Art Unit 2612 February 2, 2026
1 Yu Paragraph 0026 recites “the machine-learned image processing model further comprises a decoder portion configured to generate reconstructed image patches based on the one or more quantized codes or to generate synthetic image patches based at least in part on the one or more predicted quantized codes.”
2 Yu recites in Paragraph 0107 “zooming in on some of the images.”
3 Yu recites in Paragraph 0107 “zooming in on some of the images.”
4 Yu recites in Paragraph 0107 “zooming in on some of the images.”