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 .
Notice to Applicants
Limitations appearing inside of {} are intended to indicate the limitations not taught by said prior art(s)/combinations.
This office action is a response to the amended claims filed on 04/14/2026.
Claims 1-20 are pending in the application
Response to Amendments
Amended claims, filed on 04/14/2026 in response to the Non-Final office action filed on 01/21/2026, have been entered. Claims 1, 5, 11, 16, 18 19 have been amended. Claims 1-4, 7-10, 12-15, 17, and 20 are original. No new matter has been introduced. The rejections under 35 USC §112(b) are withdrawn. In the Non-Final office action, examiner acknowledges omission of rejection of claim 19 under 35 USC 112(b), similar to analogous claim 5, and recommends amending claim 19 to overcome rejection in further prosecution. For the purpose of examination, claim 19 is considered analogous to claim 5. The rejections under 35 USC §101 are maintained. The rejections under 35 USC §§102 and 103 are withdrawn, and a new grounds of rejection is made in view of Diesendruck et al., US 20240193911 A1, in view of Shayani et al., US 20230326159 A1.
Response to Arguments/Remarks
The rejections of claims 1-20 under 35 USC §112(b) (See Remarks, filed on 04/14/2026, page 7) are withdrawn in light of the amended claims. Examiner acknowledges that claim 19 should have similarly been rejected as analogous claim 5, and recommends amending claim 19 similarly as claim 5.
Applicant arguments regarding the rejections of claims 1-20 under 35 USC §101 (See Remarks, filed on 04/14/2026, pages 7-11) have been fully considered and are not persuasive. Applicant asserts that the amended claims integrate any such judicial exception into a practical application Step 2A Prong 2, such that the limitation reflects a specific technological improvement to the machine learning model (Remarks 04/14/2026, pages 7-8).
However, the amended claimed limitation “training, by the processing device, a style machine-learning model using the stylized training data in which the style is consistent but the subject matter varies, the style-machine learning model trained to disentangle the style from the subject matter to enable identification of the style independent of the subject matter” is known in the art, evidenced by at least Shayani et al., (US Patent Application Publication No. US 20230326159 A1), (¶[0032]) “training engine 122, execution engine 124, and style-generation engine 126 train and execute the machine learning model in a way that allows attributes pertaining to style to be disentangled from attributes pertaining to content … thereby allowing “style” to be defined”, and (¶[0116]). Applicant provided evidence regarding how the recited limitation is a technological improvement to the machine learning model training itself which includes “improving computational functional operation and operational functionality” by “separating identification of style from identification of subject matter” (Application Specification ¶[0026]), by varying content with consistent style, or varying style with a piece of semantic content (Application Specification ¶[0034]) (see Remarks 04/14/2026, page 8). Remarks (04/14/2026), pages 9-10, cites Ex Parte Desjardin decision which adds to §MPEP 2106.05(a) regarding guidance on subject eligibility of “improvements to how the machine learning model itself operates”, and additionally cites Director Squire’s December 4, 2025 Guidance on Subject Matter Eligibility which support such improvements as “patent-eligible technological advancements under the Alice framework”. Applicant submits the claimed improvement to the technology: “the claimed features change how the machine learning model processes and learns style information by using training data specifically structured to disentangle style from subject matter” (see Remarks 04/14/2026, page 10). However, the structure of the learning model itself, as indicated in MPEP §2106.05, is not recited. Further, the recited training data structure, is found in prior art, specifically in at least Shayani, as noted above. For at least these reasons the amended claims fails to integrate the judicial exception into a practical application Step 2A Prong 2, therefore examiner respectfully disagrees. Accordingly the rejections are maintained.
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1-7, 9-20 are rejected under 35 U.S.C. 103 as being unpatentable over “Diesendruck” (Diesendruck et al., US 20240193911 A1) in view of “Shayani” (Shayani et al., US 20230326159 A1).
Regarding claim 1, Diesendruck teaches a method comprising:
obtaining, by a processing device, a style example of style exhibited by digital content and a plurality of subject matter examples of different subject matter exhibited by the digital content (Diesendruck, ¶[0051]; style matching system 106 receives the small set of images with a request to generate a large dataset of style-matching images having styles and content that match the initial or original small sample set of input images);
synthesizing, by the processing device, stylized training data by transferring the style from the style example to the plurality of subject matter examples of the digital content having the different subject matter using a machine-learning model (Diesendruck, ¶[0055]; act 206 of synthesizing new images from the expanded input image set; the style matching system 106 utilizes a generative machine-learning model and/or a deep learning model to generate new synthesized images that appear similar in style and content to input images); and
training, by the processing device, a style machine-learning model using the stylized training data {in which the style is consistent but the subject matter varies, the style machine-learning model trained to disentangle the style from the subject matter to enable identification of the style independent of the subject matter} in a subsequent item of digital content (Diesendruck, ¶[0098]; FIG. 5 also includes the act 210 of training an image-based machine learning model using the style-matching dataset; ¶[0099]; FIG. 6 illustrates a series of acts for utilizing the style matching system 106; ¶[0100]; the act 610 may involve comparing an initial set of input images to multiple sets (e.g., large sets) of stored image datasets to determine the style distribution).
Diesendruck teaches "the determined style distribution" (¶[0015]), and the "catalog of stored images sets having different styles" is used to generate more images, i.e., "expands the set of input images by selecting images from … the determined style distribution" (¶[0016]). Diesendruck does not explicitly disclose in which the style is consistent but the subject matter varies, the style machine-learning model trained to disentangle the style from the subject matter to enable identification of the style independent of the subject matter.
However, Shayani, a similar field of endeavor of neural style transfer, teaches in which the style is consistent but the subject matter varies, the style machine-learning model trained to disentangle the style from the subject matter to enable identification of the style independent of the subject matter (Shayani, ¶[0116]; attributes pertaining to style in 3D shapes can be disentangled from attributes pertaining to content in 3D shapes via an arbitrary set of augmentations to the 3D shapes. In this regard, the augmentations can be selected to target certain features or attributes that constitute the “style” of a set of 3D shapes, thereby enabling precise control over the extraction and transfer of large-scale).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a disentangling the style and subject matter and transferring a consistent style to a plurality of subject matter as taught by Shayani to the invention of Diesendruck. The motivation to do so would be so that augmentations can be selected to target or destroy certain features or attributes that constitute the “style” while preserving features or attributes that constitute the “content”.
Regarding claim 2, the combination of Diesendruck and Shayani teaches the method as described in claim 1. Diesendruck further teaches wherein the digital content is a digital image and the style is a visual style depicted by the digital image (Diesendruck, ¶[0021]; input image dataset; ¶[0026]; “image style” (or “style” for short) refers to the look and feel of an image. Examples of styles include geometry, visual theme, topic, color palette, arrangement, feature sets, creation medium, characteristics, scale, resolution, perspective, capture type, spacing, object types, and/or other image styles that distinguish one set of images from another image set).
Regarding claim 3, the combination of Diesendruck and Shayani teaches method as described in claim 2. Diesendruck further teaches wherein the different subject matter corresponds to different objects that are depicted in the plurality of subject matter examples, one to another (Diesendruck, ¶[0027]; term “image content” (or “content” for short) refers to the semantic meaning of what is depicted in an image. In many cases, the content of an image refers to the subject matter and/or objects in an image).
Regarding claim 4, the combination of Diesendruck and Shayani teaches the method as described in claim 1. Diesendruck further teaches wherein the different subject matter corresponds, respectively, to different semantic content (Diesendruck, ¶[0027]; the term “image content” (or “content” for short) refers to the semantic meaning of what is depicted in an image. In many cases, the content of an image refers to the subject matter and/or objects in an image. Content can include foreground as well as background portions of an image).
Regarding claim 5, Diesendruck teaches the method as described in claim 1. Diesendruck does not explicitly disclose wherein the training includes use of a first said item of stylized training data having the style and exhibiting first said subject matter as positive training data and a second said item having a second style and exhibiting the first said subject matter as negative training data.
However, Shayani teaches wherein the training includes use of a first said item of stylized training data having the style and exhibiting first said subject matter as positive training data and a second said item having a second said style and exhibiting the first said subject matter as negative training data (Shayani, ¶[0081]; Style-generation engine 126 could use a contrastive learning technique to train the machine learning model using “positive” and “negative” pairs of style codes and descriptions. A “positive” pair could include a style code and a description of the style-based attributes associated with the style code, and a “negative” pair could include a style code and a description of style-based attributes that are not associated with the style code.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include positive and negative training data as taught by Shayani to the invention of Diesendruck. The motivation to do so would be to because this loss would also cause machine learning model 228 to minimize the similarity of a style code and an embedding of a description when the style code and description belong to a negative pair.
Regarding claim 6, Diesendruck teaches the method as described in claim 1. Shayani further teaches wherein the synthesizing the stylized training data is performed using a neural style transfer machine-learning model configured using an encoder-decoder architecture (Shayani, [0037] As shown in FIG. 2, machine learning model 204 includes an encoder 212, a style network 214, and a decoder 216).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include encoder-decoder architecture as taught by Shayani to the invention of Diesendruck. The motivation to do so would be to obtain style features and convert the style features to reconstruct the style.
Regarding claim 7, the combination of Diesendruck and Shayani teaches the method as described in claim 1. Diesendruck further teaches further comprising: identifying the style in the subsequent item of digital content using the trained style machine-learning model (Diesendruck, ¶[0098]; FIG. 5 also includes the act 210 of training an image-based machine learning model using the style-matching dataset; ¶[0099]; FIG. 6 illustrates a series of acts for utilizing the style matching system 106; ¶[0100]; the act 610 may involve comparing an initial set of input images to multiple sets (e.g., large sets) of stored image datasets to determine the style distribution); and
outputting a result of the identifying for display in a user interface (Diesendruck, ¶[0121]; converting data 707 stored in the memory 703 into text, graphics, and/or moving images (as appropriate) shown on the display device 715).
Regarding claim 9, the combination of Diesendruck and Shayani teaches the method as described in claim 1. Shayani further teaches wherein the training is performed using contrastive losses to drive a learning signal (Shayani, ¶[0081]; Style-generation engine 126 could use a contrastive learning technique to train the machine learning model using “positive” and “negative” pairs of style codes and descriptions).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include use of contrastive loss to drive a learning signal as taught by Shayani to the invention of Diesendruck. The motivation to do so would be to because this loss would also cause machine learning model 228 to minimize the similarity of a style code and an embedding of a description when the style code and description belong to a negative pair.
Regarding claim 10, the combination of Diesendruck and Shayani teaches the method as described in claim 1. Diesendruck further teaches wherein the synthesizing and the training are performed for a plurality of said styles (Diesendruck, ¶[0023]; the style matching system utilizes the original input image set to conditionally sample synthesized images from within the style-mixed (i.e., plurality of said styles) embedding space (i.e., synthesizing), which results in a larger dataset that is often indistinguishable from the original input image set (i.e. for training)).
Regarding claim 11, Diesendruck teaches a training data generation system comprising: a style selection module implemented by a processing device (Diesendruck, See FIG 1 exhibits computing device 108) to obtain a style example of style exhibited by digital content Diesendruck, ¶[0051]; style matching system 106 receives the small set of images with a request to generate a large dataset of style-matching images having styles and content that match the initial or original small sample set of input images;
a content selection module implemented by the processing device to obtain a plurality of subject matter examples of different subject matter exhibited by digital content (Diesendruck, ¶[0053]; the style matching system 106 determines how similar each image style set is to the small set of input images. Then, based on the style similarity values, the style matching system 106 generates a style distribution and selects a proportional number of sample images from each image style set (i.e., different images) to generate an extended input image set); and
a style transfer system implemented by the processing device to synthesize stylized training data configured to train a machine-learning model to identify the style, the stylized training data synthesized by transferring the style from the style example to the plurality of subject matter examples of the digital content having the different subject matter using a machine-learning model (Diesendruck, ¶[0055]; act 206 of synthesizing new images from the expanded input image set; the style matching system 106 utilizes a generative machine-learning model and/or a deep learning model to generate new synthesized images that appear similar in style and content to input images), {the stylized training data having consistent style but varying subject matter such that the machine-learning model is trainable to disentangle the style from the subject matter to enable identification of the style independent of the subject matter}.
Diesendruck does not explicitly disclose the stylized training data having consistent style but varying subject matter such that the machine-learning model is trainable to disentangle the style from the subject matter to enable identification of the style independent of the subject matter.
However, Shayani further teaches the stylized training data having consistent style but varying subject matter such that the machine-learning model is trainable to disentangle the style from the subject matter to enable identification of the style independent of the subject matter (Shayani, ¶[0116]; attributes pertaining to style in 3D shapes can be disentangled from attributes pertaining to content in 3D shapes via an arbitrary set of augmentations to the 3D shapes. In this regard, the augmentations can be selected to target certain features or attributes that constitute the “style” of a set of 3D shapes, thereby enabling precise control over the extraction and transfer of large-scale).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a disentangling the style and subject matter and transferring a consistent style to a plurality of subject matter as taught by Shayani to the invention of Diesendruck. The motivation to do so would be so that augmentations can be selected to target or destroy certain features or attributes that constitute the “style” while preserving features or attributes that constitute the “content”.
Claim 12 is similarly analyzed as analogous claim 2.
Claim 13 is similarly analyzed as analogous claim 6.
Regarding claim 14, the combination of Diesendruck and Shayani teaches the training data generation system as described in claim 11. Diesendruck further teaches wherein the style transfer system is configured to use a plurality of said styles (Diesendruck, ¶[0053]; the style matching system 106 generates a style distribution and selects a proportional number of sample images from each image style set (i.e., different images) to generate an extended input image set; ¶[0082]; style-mixed images from a catalog of image styles)
and the machine-learning model is configured to identify the plurality of said styles based on the stylized training data (Diesendruck, ¶[0015]; the style matching system identifies a catalog of stored image sets having different styles (e.g., a style catalog of image sets); ¶[0100]; the style matching system compares the initial input images to the stored image sets to determine a style distribution of distances between the initial input images and the sets of stored images).
Regarding claim 15, the combination of Diesendruck and Shayani teaches the training data generation system as described in claim 11. Diesendruck further teaches further comprising a machine-learning training module configured to train the machine- learning model using the stylized training data to identify the style in a subsequent item of digital content (Diesendruck, ¶[0098]; FIG. 5 also includes the act 210 of training an image-based machine learning model using the style-matching dataset; ¶[0099]; FIG. 6 illustrates a series of acts for utilizing the style matching system 106; ¶[0100]; the act 610 may involve comparing an initial set of input images to multiple sets (e.g., large sets) of stored image datasets to determine the style distribution).
Regarding claim 16, Diesendruck teaches one-or-more computer-readable storage media storing instructions that, responsive to execution by a processing device, causes the processing device to perform operations (Diesendruck, ¶[0113]; computer-executable instructions or data structures can be transferred automatically from transmission media to non-transitory computer-readable storage media ) comprising:
receiving an item of digital content (Diesendruck, ¶[0119]; Executing the instructions 705 may involve the use of the data 707 that is stored in the memory 703);
identifying, by a machine-learning model, a style exhibited by the item of digital content, the machine-learning model trained using stylized training data generated by transferring the style from a style example of digital content to a plurality of subject matter examples of digital content having different subject matter, one to another (Diesendruck, ¶[0098]; FIG. 5 also includes the act 210 of training an image-based machine learning model using the style-matching dataset; ¶[0099]; FIG. 6 illustrates a series of acts for utilizing the style matching system 106; ¶[0100]; the act 610 may involve comparing an initial set of input images to multiple sets (e.g., large sets) of stored image datasets to determine the style distribution; ¶[0053] the style matching system 106 generates a style distribution and selects a proportional number of sample images from each image style set (i.e., different images) to generate an extended input image set), the stylized training data having consistent style but varying subject matter such that the machine-learning model is trained to disentangle the style from the subject matter to enable identification of the style independent of the subject matter; and
outputting a result of the identifying for display in a user interface (Diesendruck, ¶[0121]; converting data 707 stored in the memory 703 into text, graphics, and/or moving images (as appropriate) shown on the display device 715).
Diesendruck does not explicitly disclose the stylized training data having consistent style but varying subject matter such that the machine-learning model is trainable to disentangle the style from the subject matter to enable identification of the style independent of the subject matter.
However, Shayani further teaches the stylized training data having consistent style but varying subject matter such that the machine-learning model is trainable to disentangle the style from the subject matter to enable identification of the style independent of the subject matter (Shayani, ¶[0116]; attributes pertaining to style in 3D shapes can be disentangled from attributes pertaining to content in 3D shapes via an arbitrary set of augmentations to the 3D shapes. In this regard, the augmentations can be selected to target certain features or attributes that constitute the “style” of a set of 3D shapes, thereby enabling precise control over the extraction and transfer of large-scale).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a disentangling the style and subject matter and transferring a consistent style to a plurality of subject matter as taught by Shayani to the invention of Diesendruck. The motivation to do so would be so that augmentations can be selected to target or destroy certain features or attributes that constitute the “style” while preserving features or attributes that constitute the “content”.
Claim 17 is similarly analyzed as analogous claims 2 and 3.
Claim 18 is similarly analyzed as analogous claim 6.
Claim 19 is similarly analyzed as analogous claim 5.
Regarding claim 20, the combination of Diesendruck and Shayani teaches the one-or-more computer-readable storage media as described in claim 16. Diesendruck further teaches wherein the identifying of the style includes identifying the style from a plurality of said styles using the machine-learning model (Diesendruck, ¶[0098], FIG. 5 includes the act 210 of training an image-based machine learning model using the style-matching dataset; the style matching system 106 utilizes the style-matching image set and/or their corresponding customized pre-trained embeddings to improve the functions and operations of other image-based machine-learning models; ¶[0100]; the series of acts 600 includes an act 610 of comparing input images to sets of stored images to determine a style distribution; the act 610 may involve comparing an initial set of input images to multiple sets (e.g., large sets) of stored image datasets to determine the style distribution between the initial set of input images and the multiple large sets of stored images; where a plurality of said styles is referred to in ¶[0082]; style-mixed images from a catalog of image styles).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Diesendruck in view of Shayani, and further in view of “Johnson” (Johnson, Justin , et al., "Perceptual Losses for Real-Time Style Transfer and Super-Resolution.", Cornell University arXiv, arXiv.org [retrieved 2023-06-28]. Retrieved from the Internet <https://arxiv.org/pdf/1603.08155.pdf>., 03/27/2016), as cited in the IDS (02/27/2024).
Regarding claim 8, the combination of Diesendruck and Shayani teaches the method of claim 1. Diesendruck further teaches wherein the synthesizing and the training are performed {in real time} (Diesendruck, ¶[0055]; act 206 of synthesizing new images from the expanded input image set; and ¶[0098]; FIG. 5 also includes the act 210 of training an image-based machine learning model using the style-matching dataset). The combination does not explicitly teach in real time.
However, Johnson, a similar field of endeavor of style transfer, teaches in real time (Johnson, [p 2, §1, ¶3]; the transformation networks run in real-time).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include real-time image processing as taught by Johnson to the combined invention of Diesendruck and Shayani. The motivation to do so would be to generate high-quality images by designing and optimizing perceptual loss functions.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHANDHANA PEDAPATI whose telephone number is 571-272-5325. The examiner can normally be reached M-F 8:30am-6pm (ET).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chan Park can be reached at 571-272-7409. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHANDHANA PEDAPATI/Examiner, Art Unit 2669 /CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669
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