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 (IDS) submitted on 11/24/2025 and 08/04/2026 were filed after the mailing date of the application. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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. Claim 20 describes a computer program product.
Further, Applicant's specification, at paragraph [91] fails to explicitly limit the scope of computer program product to non-transitory product. Thus, in giving the term its plain meaning (see MPEP 2111.01), the claimed computer program product is considered to include data signals per se. Data signals per se are not statutory as they fail to fall into one of the four statutory categories of invention.
As an additional note, a non-transitory computer program product having executable programming instructions stored thereon is considered statutory as non-transitory computer program product excludes data signals (data signals are transitory computer readable media).
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 (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 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 1-4, 6-13, 16, 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Couleaud et al. (US. Patent App. Pub. No. 2025/0078347, “Couleaud”, hereinafter).
As per claim 1, Couleaud teaches a computer-implemented method, comprising:
receiving an input via a device, wherein the input comprises a description of an entity (Fig. 1-2, input of a text prompt 102, 202, ¶ [39]);
processing the input using a first machine learning (ML) model to generate an image depicting the entity and a set of attributes of the image, wherein the first ML model having been trained to generate images based on inputs describing entities (¶ [6], generating, using the first trained machine learning model and based on the text input, a single-layer image comprising a plurality of objects with a plurality of attributes associated with the plurality of objects);
determining that the image has a particular attribute from among the set of attributes (also ¶ [6], “determining a plurality of attributes associated with the plurality of objects”); and
in response to determining that the image has the particular attribute:
processing the image to generate one or more alternate images each having a different version of the particular attribute (¶ [6], “generating, using the first trained machine learning model and based on the plurality of textual descriptions and the plurality of attributes, a plurality of images respectively corresponding to the plurality of textual descriptions”. See further Fig. 2, numerals ¶ [61], different alternative for the composite images 240, 242, 244, or 246); and
providing the one or more alternate images for display on the device (Fig. 6, 7).
As per claim 2, Couleaud also teaches wherein set of attributes of the image comprises a plurality of segments of the image (Fig. 1 and 2, ¶ [42]), a type associated to each segment of the plurality of segments (¶ [42], different types or classes of the objects) and a color associated to each segment of the plurality of segments (¶ [43]).
As per claim 3, Couleaud does also teach wherein each respective segment in the plurality of segments is depicted using a respective mask for the respective segment (¶ [43]).
As per claim 4, as addressed above, Couleaud does further teach wherein the particular attribute comprises a segment among the plurality of segments of the image of a particular type (¶ [42], each segment corresponding to an object, which, in turn, corresponds to a particular type).
As per claim 6, Couleaud does also teach wherein the first ML model is a generative model (¶ [8], generative artificial intelligence).
As per claim 7, as addressed in claim 1, Couleaud teaches wherein the input comprises at least one of a textual description of the entity, a third image depicting an entity or a voice recording describing the entity.
As per claim 8, Couleaud does further teach wherein processing the image comprises processing the image using the first ML model to generate the one or more alternate images (Fig. 1 and 4, machine learning 108 and 408 generating different variations of composite images shown in Fig. 3, ¶ [62]).
As per claim 9, as also addressed, Couleaud teaches wherein processing the image comprises processing the image using one or more image processing techniques to generate the one or more alternate images (see claim 1. It is noted that limitation image processing techniques is not clearly defined, thus given the broadest reasonable interpretation).
As per claim 10, Couleaud does further teach wherein processing the image comprises processing the image using a second ML model to generate the one or more alternate images (¶ 119], “At 1016, the control circuitry may generate, using a second trained machine learning model (e.g., machine learning model 126 of FIG. 1…)”).
As per claim 11, as addressed in claim 10, Couleaud teaches wherein the second ML model is an image processing model (¶ [119], for generating image).
Claim 12, which is similar in scope to claims 1, 10, and 11 as addressed above, is thus rejected under the same rationale.
Claim 13, which is similar in scope to claim 7 as addressed above, is thus rejected under the same rationale.
Claim 16, which is similar in scope to claim 11 as addressed above, is thus rejected under the same rationale.
Claim 18, which is similar in scope to claim 1 as addressed above, is thus rejected under the same rationale.
Claim 19, which is similar in scope to claim 12 as addressed above, is thus rejected under the same rationale.
Claim 20, which is similar in scope to claim 1 as addressed above, is thus rejected under the same rationale.
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.
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.
Claims 5, 14-15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Couleaud et al. (US. Patent App. Pub. No. 2025/0078347) in view of Mitra et al. (US. Patent App. Pub. No. 20220028139, “Mitra”).
As per claim 5, Couleaud does not expressly teach wherein the particular type comprises a skin of the entity.
However, in a very similar method of generating image with a set of attributes (see Abstract), Mitra teaches this feature, i.e., wherein the particular type comprises a skin of the entity (see Fig. 1 and 5, ¶ [34], ¶ [156]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method as taught by Mitra into the method as taught by Couleaud as addressed above, the advantage of which is to provide users with significant control over a range of attributes such as object properties, camera position, and illumination (¶ [20]).
As per claim 14, as addressed in claim 5, the combined teachings of Couleaud and Mitra include wherein determining the particular attribute comprises determining that the image depicts a portion of entity that shows skin. Thus, claim 14 would have been obvious over the combined references for the reason above.
As per claim 15, the combined Couleaud-Mitra does also teach wherein the set of altered attributes comprise a different color for the portion of entity that shows skin (Mitra, ¶ [32-34], i.e., modifying skin tone). Thus, claim 15 would have been obvious over the combined references for the reason above.
As per claim 17, the combined Couleaud-Mitra substantially teaches wherein the image processing model is a transformer-based convolutional neural network model (Mitra, Fig. 7, ¶ [76]). Thus, claim 17 would have been obvious over the combined references for the reason above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hau H. Nguyen whose telephone number is: 571-272-7787. The examiner can normally be reached on MON-FRI from 8:30-5:30.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tammy Goddard, can be reached on (571) 272-7773.
The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HAU H NGUYEN/Primary Examiner, Art Unit 2611
