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 .
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on April 29, 2025 and August 25, 2025 complies with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
35 USC § 101 Statutory Analysis
The claims do not recite any of the judicial exceptions enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance. Further, the claims do not recite any method of organizing human activity, such as a fundamental economic concept or managing interactions between people. Finally, the claims do not recite a mathematical relationship, formula, or calculation. Thus, the claims are eligible because they do not recite a judicial exception.
Claim Rejections - 35 USC § 101
35 U.S.C. §101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 20 is rejected under 35 U.S.C. §101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the broadest reasonable interpretation of the claimed “one or more computer-readable storage media”, consistent with a conclusion reached by one skilled in the art based on both the specification disclosure and the state-of-the-art, is that the full scope covers transitory “signal” embodiments. The state-of-the-art at the time the invention was made included signals, carrier waves and other wireless communication modalities (e.g. RF, infrared, etc.) as media on which executable code was recorded and from which computers acquired such code. Thus, the full scope of the claim covers “signals” and their equivalents, which are non-statutory per se (In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007)).
The examiner suggests clarifying the claims to exclude such non-statutory signal embodiments, such as (but not limited to) by reciting a “one or more non-transitory computer-readable storage media”, or equivalents.
Claim Rejections - 35 USC § 102
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 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 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.
Claims 1-5, 7-16 and 18-20 are rejected under 35 U.S.C. §102(a)(1) as being anticipated by Ungureanu et al. (U.S. Patent Application Publication No. US 2023/0055204 A1) (hereafter referred to as “Ungureanu”).
With regard to claim 1, Ungureanu describes receiving a plurality of training examples for training an image processing model, each training example comprising an image and a corresponding ground-truth output for the image (refer for example to paragraphs [0022] and [0061]); generating, for each image, a respective grayscale image (see Figure 2A, element 200and refer for example to paragraph [0048]); generating, for each respective grayscale image, one or more recolored images using one or more colorization models (see Figure 2A, element 202 and refer for example to paragraph [0048]); and generating an augmented set of training data for training the image processing model that comprises a plurality of additional training examples, each additional training example comprising a respective recolored image generated from a respective image and the corresponding ground-truth output for the respective image (refer for example to paragraphs [0088], [0089] and [0096]).
As to claim 2, Ungureanu describes further comprising training the image processing model on the augmented set of training data (refer to paragraph [0089]).
In regard to claim 3, Ungureanu describes wherein the augmented set of training data further comprises the plurality of training examples (refer to paragraph [0089]).
With regard to claim 4, Ungureanu describes wherein each image comprises a synthetic image (refer for example to paragraph [0096]).
As to claim 5, Ungureanu describes wherein the synthetic image comprises a rendered image (refer for example to paragraph [0096]).
With regard to claim 7, Ungureanu describes wherein generating, for each respective grayscale image, one or more recolored images using one or more colorization models comprises generating each of the one or more recolored images by sampling from the one or more colorization models given the respective grayscale image (refer to paragraph [0028] through [0031], [0047] through [0053] and [0071]).
As to claim 8, Ungureanu describes wherein the one or more colorization models comprise a sequence of colorization models, and wherein generating, for each respective grayscale image, one or more recolored images using one or more colorization models comprises for each respective grayscale image generating an initial recolored image using a first colorization model in the sequence of colorization models given a first grayscale image derived from the respective grayscale image, and for each subsequent colorization model in the sequence of colorization models providing an input recolored image and a respective intermediate grayscale image derived from the respective grayscale image for the subsequent colorization model as input to the subsequent colorization model to generate a respective intermediate recolored image, wherein the input recolored image is generated as output by a preceding colorization model in the sequence, and wherein the one or more recolored images comprise the respective intermediate recolored image generated by a last colorization model of the sequence of colorization models (refer for example to paragraphs [0028] through [0031], [0047] through [0053], [0071] and [0133]).
In regard to claim 9, Ungureanu describes wherein each subsequent colorization model generates images of a corresponding resolution, and wherein the respective intermediate grayscale image for the subsequent colorization model has the corresponding resolution (refer for example to paragraph [0069]).
With regard to claim 10, Ungureanu describes wherein the one or more colorization models have been trained on training data comprising real images (refer for example to paragraphs [0070] and [0071]).
As to claim 11, Ungureanu describes wherein the image processing model performs an image processing task comprising any one or more of image segmentation, object detection, or object recognition (refer to paragraphs [0026], [0027], [0039], and [0056]).
In regard to claim 12, Ungureanu describes one or more computers and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations (refer for example to paragraphs [0128] through [0134]) comprising receiving a plurality of training examples for training an image processing model, each training example comprising an image and a corresponding ground-truth output for the image (refer for example to paragraphs [0022] and [0061]); generating, for each image, a respective grayscale image (see Figure 2A, element 200and refer for example to paragraph [0048]); generating, for each respective grayscale image, one or more recolored images using one or more colorization models (see Figure 2A, element 202 and refer for example to paragraph [0048]); and generating an augmented set of training data for training the image processing model that comprises a plurality of additional training examples, each additional training example comprising a respective recolored image generated from a respective image and the corresponding ground-truth output for the respective image (refer for example to paragraphs [0088], [0089] and [0096]).
With regard to claim 13, Ungureanu describes wherein the operations further comprise training the image processing model on the augmented set of training data.
As to claim 14, Ungureanu describes wherein the augmented set of training data further comprises the plurality of training examples (refer to paragraph [0089]).
In regard to claim 15, Ungureanu describes wherein each image comprises a synthetic image (refer for example to paragraph [0096]).
With regard to claim 16, Ungureanu describes wherein the synthetic image comprises a rendered image (refer for example to paragraph [0096]).
In regard to claim 18, Ungureanu describes wherein generating, for each respective grayscale image, one or more recolored images using one or more colorization models comprises generating each of the one or more recolored images by sampling from the one or more colorization models given the respective grayscale image (refer to paragraph [0028] through [0031], [0047] through [0053] and [0071]).
With regard to claim 19, Ungureanu describes wherein the one or more colorization models comprise a sequence of colorization models, and wherein generating, for each respective grayscale image, one or more recolored images using one or more colorization models comprises for each respective grayscale image generating an initial recolored image using a first colorization model in the sequence of colorization models given a first grayscale image derived from the respective grayscale image, and for each subsequent colorization model in the sequence of colorization models providing an input recolored image and a respective intermediate grayscale image derived from the respective grayscale image for the subsequent colorization model as input to the subsequent colorization model to generate a respective intermediate recolored image, wherein the input recolored image is generated as output by a preceding colorization model in the sequence, and wherein the one or more recolored images comprise the respective intermediate recolored image generated by a last colorization model of the sequence of colorization models (refer for example to paragraphs [0028] through [0031], [0047] through [0053], [0071] and [0133]).
As to claim 20, Ungureanu describes one or more computer-readable storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations (refer for example to paragraphs [0128] through [0134]) comprising receiving a plurality of training examples for training an image processing model, each training example comprising an image and a corresponding ground-truth output for the image (refer for example to paragraphs [0022] and [0061]); generating, for each image, a respective grayscale image (see Figure 2A, element 200and refer for example to paragraph [0048]); generating, for each respective grayscale image, one or more recolored images using one or more colorization models (see Figure 2A, element 202 and refer for example to paragraph [0048]); and generating an augmented set of training data for training the image processing model that comprises a plurality of additional training examples, each additional training example comprising a respective recolored image generated from a respective image and the corresponding ground-truth output for the respective image (refer for example to paragraphs [0088], [0089] and [0096]).
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 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(a) 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 6 and 17 are rejected under 35 U.S.C. §103(a) as being unpatentable over Ungureanu et al. (U.S. Patent Application Publication No. US 2023/0055204 A1) in view of Zhang et al. (U.S. Patent Application Publication No. US 2025/0046055 A1) (hereafter referred to as “Zhang”).
The arguments advanced in section 8 above, as to the applicability of Ungureanu, are incorporated herein.
In regard to claim 6, although Ungureanu does not expressly describe the wherein each of the one or more colorization models comprises an image-to-image diffusion model, such a technique is well known and widely utilized in the prior art.
Zhang discloses a (see Figure 4 and refer for example to paragraphs [0037], [0038], [0039] and [0048]).
Given the teachings of the two references and the same environment of operation, namely that of systems for colorization of images using neural networks which provide for training the neural networks using colorization models, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Ungureanu system to provide for colorization models which comprise an image-to-image diffusion model in the manner described by Zhang according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested by Zhang (refer for example to paragraph [0002]), which fails to patentably distinguish over the prior art absent some novel and unexpected result.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Liao and Huang all disclose systems similar to applicant’s claimed invention.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jose L. Couso whose telephone number is (571) 272-7388. The examiner can normally be reached on Monday through Friday from 5:30am to 1:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella, can be reached on 571-272-7778. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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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.
/JOSE L COUSO/Primary Examiner, Art Unit 2667
January 21, 2026