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 .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Response to Amendment
The Request for continued Examination, filed on 05/14/2026, has been entered and acknowledged by the Examiner. In the Amendment, applicant amended claims 1, 12 and 18.
As to Arguments and Remarks filed in the Amendment, please see Examiner’s responses shown after Rejections - 35 U.S.C § 103
Please note claims 1-20 are pending.
Terminal Disclaimer
The Terminal Disclaimer filed on 05/20/2026 has been acknowledged and has been approved.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below 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 or disclosed by the examiner.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 non-obviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Astrakhantsev et al. (US PGPUB 2022/0277056, hereinafter Astrakhantsev), in view of Collomosse et al. (US PGPUB 2022/0092108, hereinafter Collomosse) and further in view of Saharia et al. (US PGPUB 2023/0103638, hereinafter Saharia).
As per as claim 1, Astrakhantsev discloses:
A method comprising:
receiving, by a processing device, a search query to locate digital images included in a digital image repository (Astrakhantsev, e.g., fig. 1, associating with texts description, [0003-0004], “...user enters a query...” and [0025-0027],”...users to enter queries...searches one or more datastores...”);
causing, by the processing device, a generative machine-learning model to generate a generated digital image based on the search query, the generated digital image including first visual features that correspond to the search query (Astrakhantsev, e.g., [0028-0029], “... generate textual search results 24 with a plurality of search results for the query...include a plurality of images, a plurality of videos, and a plurality of text...”, [0036-0037], “... clicks for each image in the plurality of images 40 or takes features of the most dominant image of the plurality of images 40 if aggregation for a particular feature ...” and see [0041-0042], “... machine learning model to determine whether the query includes a visual intent. The machine learning model may be trained using fully supervised data (labeled by humans) or weakly supervised data of search engine logs to extract images searches where users are interested in the source pages, not only in the images, based on whether the user clicks on pages more often than the image from the search engine logs...”);
performing, by the processing device and using a generated digital image, an image-based search to locate the digital images included in the digital image repository by comparing second visual features of the digital images to the first visual features of the generated digital image (Astrakhantsev, e.g., [0022], [0029-0031], “...determine to trigger the cascading waterfall layout of images, videos, and/or gifs based on the query provided by the user...use a trained machine learning model to determine whether to trigger the presentation of the cascading waterfall layout. The machine learning model may be trained based on fully supervised data...extract images searches where users are interested in the source pages” (extracting and comparing images features/metadata) and further see [0037-0038] for comparing second features and first features of images/video/photo);
generating, by the processing device, latent representations of the digital images (Astrakhantsev, e.g., [0023-0024], “...images, videos, and/or gifs to include in the cascading waterfall by using a ranking based on a weighted... order of images presented in the cascading waterfall may be also specific (e.g., for DIY projects the order of images presented may be a logical order of steps to complete the project)...”) and further see [0040-0042], “...generate a cascading waterfall layout to present the plurality of images...”); and
presenting, by the processing device, a search result of the digital images in a user interface based on the performing of the image-based search, the search result arranging the digital images in an order based on clusters of the latent representations (Astrakhantsev, e.g., [0036-0038], “...rankings may be used to determine an order for presenting the plurality of webpages and/or a plurality of images associated with the plurality of webpages....classifier may apply a higher ranking...” and further see [0048-0049] and [0104], “...presenting the structured data next to the cascading waterfall layout on the same search result webpage... rearranging an ordering of the images within at least one column of the plurality of columns 50, rearranging a position of the images within the plurality of column...”).
To make records clearer regarding to the language of “causing, by the processing device, a generative machine-learning model to generate a generated digital image based on the search query, the generated digital image including first visual features that correspond to the search query” and “comparing second visual features of the digital images/item/video to the first visual features” (although as stated above Astrakhantsev functional disclose “generated digital image of first visual features that correspond to the search query” ((Astrakhantsev, e.g., [0028-0029], 36-0037]) and “the features of comparing features between items/image/photo/video” (Astrakhantsev, e.g., [0022], [0029-0031], [0037-0038]).
However Collomosse, in an analogous art, discloses “causing, by the processing device, a generative machine-learning model to generate a generated digital image based on the search query, the generated digital image including first visual features that correspond to the search query” (Collomosse, e.g., [0019-0023], “...generates a style embedding for a query digital image by utilizing a weakly supervised discriminative neural network to extract features from the query digital image. Indeed, in some cases, the style search system generates a style embedding such as a feature vector including features that represent observable and/or unobservable characteristics of the query digital image that define or represent its style...” and [0039-0040], [0055-0056], “...the style search system 102 generates a style embedding that includes one or more features indicating a style of a digital image. For instance, the style search system 102 generates a style embedding by combining two or more style codes. In some embodiments, a style embedding refers to a collection or a set of observable features or unobservable (e.g., deep) features that indicate characteristics of a style for a digital image. For example, a style embedding includes a feature vector of one or more constituent style codes combined together. Along these lines, in some embodiments, a style code refers to one or more encoded features of a digital image that captures or represents one or more aspects of style for the digital image..”, and [0088], [0101], “... parameter learning manager 908 analyzes sample digital images to learn various internal weights and network parameters (e.g., instance normalization parameters) associated with the particular style extraction neural networks ...” and [0107] (language format)) and “comparing second visual features of the digital images/item/video to first visual features” (Collomosse, e.g., [0024], [0037-0038], “... generates a style embedding for a query digital image to compare with style embeddings associated with digital images within a repository of digital images. To generate the style embedding... identify digital images with similar style to a query digital image...”, [0041-0042], [0098], “.... determining one or more digital images from a repository of digital images and having a style similar to the query digital image by comparing the style embedding and style embeddings associated with digital images within the repository of digital images...” and [0055-0056], “…a feature map 324 includes a set of values corresponding to latent and/or patent attributes and characteristics of an input analyzed by a neural network (e.g., a digital image). In particular, the deep image comparator model 106 generates a first set of deep features (e.g., a first set of feature maps) for the aligned query image 310 utilizing the neural network feature extractor 322. The deep image comparator model 106 generates a second set of deep features (e.g., a second set of feature maps) for the source image 308 utilizing the neural network feature extractor …”). Thus, it would have been obvious to one of ordinary skill in the art BEFORE the effective filling date of the claimed invention to combine the teaching of Collomosse and Astrakhantsev for machine learning models to learn representations suitable for searching digital artwork and defining a suitable ontology to label styles as well as the subjective nature of labelling digital images in order to identify styles beyond those that are already labeled within a dataset (Collomosse, e.g., [0001-0003]).
To further clarify the features of “the generative machine-learning model including a diffusion model trained to generate digital images via a reverse diffusion process based on the search query”.
However Saharia, in an analogous art, discloses “the generative machine-learning model including a diffusion model trained to generate digital images via a reverse diffusion process based on the search query” (Saharia, e.g., [0005-0014], “…a noisy image and a denoised version of the noisy image; training, based on the training data, a multi-task diffusion model to perform a plurality of image-to-image translation tasks... determining a reverse diffusion process by inverting the forward diffusion process to predict the denoised version of the noisy image; and providing, by the computing device, the trained multi-task diffusion model…” and [0090-0091], [0129], “…search, computation, graphical, audio, video, World Wide Web/Internet utilization, and/or other functions…”). Thus, it would have been obvious to one of ordinary skill in the art BEFORE the effective filling date of the claimed invention to combine the teaching of Saharia, Collomosse and Astrakhantsev to perform a plurality of image-to-image translation tasks, wherein the training includes iteratively generating a forward diffusion process by predicting, at each iteration in a sequence of iterations and based on a current noisy estimate of the denoised version of the noisy image, noise data for a next noisy estimate of the denoised version of the noisy image; updating, at each iteration, the current noisy estimate to the next noisy estimate by combining the current noisy estimate with the predicted noise data (Saharia, e.g., [abstract], [0004]).
As per as claim 2, the combination of Saharia, Collomosse and Astrakhantsev disclose:
The method of claim 1 further comprises:
generating one or more latent representations of the generated digital image (Astrakhantsev, e.g., [0040-0041], “...generate a cascading waterfall layout to present the plurality of images 40 for the identified plurality of webpages...”); and
assigning each latent representation of the one or more latent representations to a cluster among the clusters (Astrakhantsev, e.g., [0032-0033], “... group the plurality of images from the search results according to ideas, projects, and/or travel destinations associated with the images...” and (Collomosse, e.g., [0005], “...digital content groupings from digital portfolios to learn parameters of a style extraction neural network without relying on explicit labelling of styles in digital images...” and [0046-0050], [0061-0063], “... selects a project grouping G c T at random and further selects a digital image a at random from the project grouping G as the anchor. The style search system 102 selects the remaining digital images within the grouping G as positive examples G.sup.+=G\{a}. Additionally, the style search system 102 selects an equal number of negative examples from other project groupings G.sup.−⊂T\G.sup.+. By thus selecting digital images to form triplets, the style search system 102 generates a mini-batch B of 32 triplets (a, p, n) where p∈G.sup.+ and n∈G.sup.− (where a represents an anchor digital image...”.).
As per as claim 3, the combination of Saharia, Collomosse and Astrakhantsev disclose:
The method of claim 2, wherein:
the generated digital image includes multiple generated digital images (Astrakhantsev, e.g., [0028-0029], [0040], “... generate search results that identify a plurality of webpages from the datastores.. a plurality of images, a plurality of videos, and/or a plurality of text...”).; and
a single latent representation of each generated digital image is assigned to each cluster, a first number of clusters being equal to a second number of the multiple generated digital images (According to figure 5, [0058] and [0062] of instant application discloses “... grouping result digital images into clusters... group result digital images included in the first, second, third, fourth, and fifth sets of result digital images into clusters... a number of the clusters is equal to a number of the digital images...”). Unless applicant provide the detailing of the features of how to assigned to each cluster, a first number of clusters being equal to a second number of the multiple generated digital image, otherwise for the broadest interpretation, the examiner asserts the digital images equal to number of clusters (categories/groups/directories/folder/subfolder of images/photo/video) (Astrakhantsev, e.g., [0028], [0032-0033], “... group the plurality of images from the search results according to ideas, projects, and/or travel destinations associated with the images...” and further see (Collomosse, e.g., [0005], “...digital content groupings from digital portfolios to learn parameters of a style extraction neural network without relying on explicit labelling of styles in digital images...” and [0046-0050], [0061-0063], “... selects a project grouping G c T at random and further selects a digital image a at random from the project grouping G as the anchor. The style search system 102 selects the remaining digital images within the grouping G as positive examples G.sup.+=G\{a}. Additionally, the style search system 102 selects an equal number of negative examples from other project groupings G.sup.−⊂T\G.sup.+. By thus selecting digital images to form triplets, the style search system 102 generates a mini-batch B of 32 triplets (a, p, n) where p∈G.sup.+ and n∈G.sup.− (where a represents an anchor digital image...”.).
As per as claim 4, the combination of Saharia, Collomosse and Astrakhantsev disclose:
The method of claim 2, wherein the digital images are grouped into the clusters based on perceptual similarities computed for the digital images (Collomosse, e.g., [0005], “...digital content groupings from digital portfolios to learn parameters of a style extraction neural network without relying on explicit labelling of styles in digital images...” and [0046-0050], [0061-0063], “... selects a project grouping G c T at random and further selects a digital image a at random from the project grouping G as the anchor. The style search system 102 selects the remaining digital images within the grouping G as positive examples G.sup.+=G\{a}. Additionally, the style search system 102 selects an equal number of negative examples from other project groupings G.sup.−⊂T\G.sup.+. By thus selecting digital images to form triplets, the style search system 102 generates a mini-batch B of 32 triplets (a, p, n) where p∈G.sup.+ and n∈G.sup.− (where a represents an anchor digital image...”.) and (Astrakhantsev, e.g., [0023], [0032-0033], grouping similar images).
As per as claim 5, the combination of Saharia, Collomosse and Astrakhantsev disclose:
The method of claim 4, wherein the perceptual similarities are computed using a learned perceptual image patch similarity loss (Astrakhantsev, e.g., [0036-0038], “...rankings may be used to determine an order for presenting the plurality of webpages and/or a plurality of images associated with the plurality of webpages....classifier may apply a higher ranking...” and further see [0048-0049] and [0104], “...presenting the structured data next to the cascading waterfall layout on the same search result webpage... rearranging an ordering of the images within at least one column of the plurality of columns 50, rearranging a position of the images within the plurality of column...”) and further see (Collomosse, e.g., [0059-0063], and [0068-0070], “...reconstruction loss 412, where T represents the 1 million sample digital images from Behance.net used for training. In some embodiments, the style search system 102 utilizes a reconstruction loss function...”).
As per as claim 6, the combination of Saharia, Collomosse and Astrakhantsev disclose: The method of claim 2, wherein the order is based on a number of the digital images included in the clusters (Astrakhantsev, e.g., [0036-0038], “...rankings may be used to determine an order for presenting the plurality of webpages and/or a plurality of images associated with the plurality of webpages....classifier may apply a higher ranking...” and further see [0048-0049] and [0104], “...presenting the structured data next to the cascading waterfall layout on the same search result webpage... rearranging an ordering of the images within at least one column of the plurality of columns 50, rearranging a position of the images within the plurality of column...”) and (Collomosse, e.g., [0005], “...digital content groupings from digital portfolios to learn parameters of a style extraction neural network without relying on explicit labelling of styles in digital images...” and [0046-0050], [0061-0063], “... selects a project grouping G c T at random and further selects a digital image a at random from the project grouping G as the anchor. The style search system 102 selects the remaining digital images within the grouping G as positive examples G.sup.+=G\{a}. Additionally, the style search system 102 selects an equal number of negative examples from other project groupings G.sup.−⊂T\G.sup.+. By thus selecting digital images to form triplets, the style search system 102 generates a mini-batch B of 32 triplets (a, p, n) where p∈G.sup.+ and n∈G.sup.− (where a represents an anchor digital image...”.).
As per as claim 7, the combination of Saharia, Collomosse and Astrakhantsev disclose: The method of claim 2, wherein the order of arranging the digital images in the search result is also based on a diversity input controlling an interleaving of the digital images from different clusters (Astrakhantsev, e.g., [0032-0033], “...group the plurality of images from the search results according to ideas, projects, and/or travel destinations associated with the images...” (group different types/class/subclass) and further see (Collomosse, e.g., [0005], “...digital content groupings from digital portfolios to learn parameters of a style extraction neural network without relying on explicit labelling of styles in digital images...” and [0046-0050], [0061-0063], “... selects a project grouping G c T at random and further selects a digital image a at random from the project grouping G as the anchor. The style search system 102 selects the remaining digital images within the grouping G as positive examples G.sup.+=G\{a}. Additionally, the style search system 102 selects an equal number of negative examples from other project groupings G.sup.−⊂T\G.sup.+. By thus selecting digital images to form triplets, the style search system 102 generates a mini-batch B of 32 triplets (a, p, n) where p∈G.sup.+ and n∈G.sup.− (where a represents an anchor digital image...”.).
As per as claim 8 the combination of Saharia, Collomosse and Astrakhantsev disclose: The method of claim 7, wherein:
a first diversity input value causes the digital images from a largest cluster to be displayed first followed by the digital images from a second-largest cluster (Astrakhantsev, e.g., [0022-0023], “...user clicks on pages more often than the images from the search engine logs. The user clicking on the source pages may indicate that the query has visual exploration intent (exploration because of clicking on the webpages may indicate interest in additional context and/or related information) and visual intent because the query was issued to an image search...using a ranking based on a weighted....”) and see [0032-0033] for grouping/clustering) and further (Collomosse, e.g., [0005], “...digital content groupings from digital portfolios to learn parameters of a style extraction neural network without relying on explicit labelling of styles in digital images...” and [0046-0050], [0061-0063], “... selects a project grouping G c T at random and further selects a digital image a at random from the project grouping G as the anchor. The style search system 102 selects the remaining digital images within the grouping G as positive examples G.sup.+=G\{a}. Additionally, the style search system 102 selects an equal number of negative examples from other project groupings G.sup.−⊂T\G.sup.+. By thus selecting digital images to form triplets, the style search system 102 generates a mini-batch B of 32 triplets (a, p, n) where p∈G.sup.+ and n∈G.sup.− (where a represents an anchor digital image...”.); and
a second diversity input value causes a first digital image from the largest cluster to be displayed first followed by a second digital image from the second- largest cluster (Astrakhantsev, e.g., [0022-0023], “...user clicks on pages more often than the images from the search engine logs. The user clicking on the source pages may indicate that the query has visual exploration intent (exploration because of clicking on the webpages may indicate interest in additional context and/or related information) and visual intent because the query was issued to an image search...using a ranking based on a weighted....”) and further see [0032-0033] for grouping/clustering) and (Collomosse, e.g., [0005], [0046-0050] and [0061-0063]).
As per as claim 9, the combination of Saharia, Collomosse and Astrakhantsev disclose: The method of claim 1, wherein:
the search query comprises a text search query in a natural language format (Astrakhantsev, e.g., fig. 1, associating with texts description, [0003-0004], “...user enters a query...” and [0025-0027],”...users to enter queries...searches one or more datastores...”); and
the generated digital image is generated by the machine-learning model based on the text search query, the machine-learning model being trained on training data to generate generated digital images with visual features that correspond to semantic intents of training text inputs in the natural language format (Astrakhantsev, e.g., [0022-0023], [0041], “...user clicking on the source pages may indicate that the query has visual exploration intent (exploration because of clicking on the webpages may indicate interest in additional context and/or related information) and visual intent because the query was issued to an image search, not a web search. If the machine learning model determines that the query includes a visual intent, the presentation of the cascading waterfall layout of images may be triggered....”) and further (Collomosse, e.g., [0019-0023], “...generates a style embedding for a query digital image by utilizing a weakly supervised discriminative neural network to extract features from the query digital image. Indeed, in some cases, the style search system generates a style embedding such as a feature vector including features that represent observable and/or unobservable characteristics of the query digital image that define or represent its style...” and [0039-0040], “...the style search system 102 generates a style embedding that includes one or more features indicating a style of a digital image. For instance, the style search system 102 generates a style embedding by combining two or more style codes. In some embodiments, a style embedding refers to a collection or a set of observable features or unobservable (e.g., deep) features that indicate characteristics of a style for a digital image. For example, a style embedding includes a feature vector of one or more constituent style codes combined together. Along these lines, in some embodiments, a style code refers to one or more encoded features of a digital image that captures or represents one or more aspects of style for the digital image..” and [0088], [0101], “... parameter learning manager 908 analyzes sample digital images to learn various internal weights and network parameters (e.g., instance normalization parameters) associated with the particular style extraction neural networks ...”).
As per as claim 10, the combination of Saharia, Collomosse and Astrakhantsev disclose: The method of claim 9, wherein the machine-learning model generates the generated digital image based on a set of prompts generated by an additional machine-learning model, the additional machine-learning model including a natural language model and being trained to generate prompts that cause the machine-learning model to generate digital images that depict the visual features that correspond to the semantic intents of text search queries in the natural language format (Astrakhantsev, e.g., [0022-0023], “...user clicking on the source pages may indicate that the query has visual exploration intent (exploration because of clicking on the webpages may indicate interest in additional context and/or related information) and visual intent because the query was issued to an image search, not a web search. If the machine learning model determines that the query includes a visual intent, the presentation of the cascading waterfall layout of images may be triggered....”). (the examiner asserts when user click on the links/source = prompts) and further see [0029-0030] and [0041], “...The machine learning model may be trained using fully supervised data (labeled by humans) or weakly supervised data of search engine logs to extract images searches where users are interested in the source pages, not only in the images, based on whether the user clicks on pages more often than the image from the search engine logs... presentation component may use the trained machine learning model to determine that the query includes a visual aspect and/or is a visual exploration search and may trigger the presentation of the cascading waterfall layout”) and (Collomosse, e.g., [0019-0023], “...generates a style embedding for a query digital image by utilizing a weakly supervised discriminative neural network to extract features from the query digital image. Indeed, in some cases, the style search system generates a style embedding such as a feature vector including features that represent observable and/or unobservable characteristics of the query digital image that define or represent its style...” and [0039-0040], “...the style search system 102 generates a style embedding that includes one or more features indicating a style of a digital image. For instance, the style search system 102 generates a style embedding by combining two or more style codes. In some embodiments, a style embedding refers to a collection or a set of observable features or unobservable (e.g., deep) features that indicate characteristics of a style for a digital image. For example, a style embedding includes a feature vector of one or more constituent style codes combined together. Along these lines, in some embodiments, a style code refers to one or more encoded features of a digital image that captures or represents one or more aspects of style for the digital image..” and [0088], [0101], “... parameter learning manager 908 analyzes sample digital images to learn various internal weights and network parameters (e.g., instance normalization parameters) associated with the particular style extraction neural networks ...”).
As per as claim 11, the combination of Saharia, Collomosse and Astrakhantsev disclose: The method of claim 1, wherein:
the search query comprises an input image (Astrakhantsev, e.g., fig. 9, associating with texts description, user may enter in a general query...include a cascading waterfall layout with a plurality of images...”); and
the generated digital image is generated by the machine-learning model based on the input image, the machine-learning model being trained on training data to generate generated digital images with visual features that correspond to the input image (Astrakhantsev, e.g., [0022-0023], “...user clicking on the source pages may indicate that the query has visual exploration intent (exploration because of clicking on the webpages may indicate interest in additional context and/or related information) and visual intent because the query was issued to an image search, not a web search. If the machine learning model determines that the query includes a visual intent, the presentation of the cascading waterfall layout of images may be triggered....”) and further see [0029-0030] and [0041], “...The machine learning model may be trained using fully supervised data (labeled by humans) or weakly supervised data of search engine logs to extract images searches where users are interested in the source pages, not only in the images, based on whether the user clicks on pages more often than the image from the search engine logs... presentation component may use the trained machine learning model to determine that the query includes a visual aspect and/or is a visual exploration search and may trigger the presentation of the cascading waterfall layout”) and (Collomosse, e.g., [0019-0023], “...generates a style embedding for a query digital image by utilizing a weakly supervised discriminative neural network to extract features from the query digital image. Indeed, in some cases, the style search system generates a style embedding such as a feature vector including features that represent observable and/or unobservable characteristics of the query digital image that define or represent its style...” and [0039-0040], “...the style search system 102 generates a style embedding that includes one or more features indicating a style of a digital image. For instance, the style search system 102 generates a style embedding by combining two or more style codes. In some embodiments, a style embedding refers to a collection or a set of observable features or unobservable (e.g., deep) features that indicate characteristics of a style for a digital image. For example, a style embedding includes a feature vector of one or more constituent style codes combined together. Along these lines, in some embodiments, a style code refers to one or more encoded features of a digital image that captures or represents one or more aspects of style for the digital image..” and [0088], [0101], “... parameter learning manager 908 analyzes sample digital images to learn various internal weights and network parameters (e.g., instance normalization parameters) associated with the particular style extraction neural networks ...”).
Claims 12-16 are essentially the same as claims 1-11 except that they set forth the claimed invention as computer readable medium rather a method, respectively and correspondingly, therefore is rejected under the same reasons set forth in rejections of claims 1-7.
Claims 17-20 are essentially the same as claims 1-11 except that they set forth the claimed invention as a computer readable storage medium rather a method, respectively and correspondingly, therefore is rejected under the same reasons set forth in rejections of claims 1-11.
Response to Arguments
The Examiner respectfully reminds applicant of the broadest reasonable interpretation standard (See MPEP 2111), "During examination, the claims must be interpreted as broadly as their terms reasonably allow." In re American Academy of Science Tech Center, 367 F.3d 1359, 1369, 70 USPQ2d 1827, 1834 (Fed. Cir. 2004) (The USPTO uses a different standard for construing claims than that used by district courts; during examination the USPTO must give claims their broadest reasonable interpretation.) In Phillips v. AWH Corp., 415 F.3d 1303, 75 USPQ2d 1321 (Fed. Cir. 2005), the court further elaborated on the “broadest reasonable interpretation" standard and recognized that “The Patent and Trademark Office (“PTO") determines the scope of claims in patent applications not solely on the basis of the claim language, but upon giving claims their broadest reasonable construction." Thus, when interpreting claims, the courts have held that Examiners should (1) interpret claim terms as broadly as their terms reasonably allows and (2) interpret claim phrases as broadly as their construction reasonably allows.
Applicant’s arguments filed 05/14/2026 with respect to claims 1-20 have been considered but are moot in view of the new ground(s) of rejection necessitated by applicant's amendment to the claims. Applicant's newly amended features are taught implicitly, expressly, or impliedly by the prior art of record (See the new ground(s) of rejection set forth herein above).
The Examiner respectfully submits that, with respect to the totally newly amended subject matter, the Examiner respectfully cited proper paragraphs from cited reference to reject the claim in responsive to the newly amended, please refer to the corresponding section of the office action.
Additional Art Considered
The prior art made of record and not relied upon is considered pertinent to the Applicants’ disclosure.
The following patents and papers are cited to further show the state of the art at the time of Applicants’ invention with respect to provide an images search which is entering search query and return the list of results digital images allow the user to selects a particular result digital image from the list and replaces the example digital image in the digital template with the particular result digital image.
a. Fouts et al. (US PGPUB 2023/0149092, hereinafter Fouts) “ Systems and Methods For Compensating For Obstructions In Medical Images” discloses “ determining an attribute associated with anatomy of interest of a patient includes receiving first image data capturing the anatomy of interest of the patient and at least one obstruction obscuring at least a portion of the anatomy of interest of the patient; generating, using at least one machine learning model, second image data in which at least a portion of the obstruction is replaced; and determining at least one attribute associated with the anatomy of interest based on the second image data”.
Fouts also detection and the obstruction replacement are performed using different machine learning models [0007].
Fouts further teaches diffusion-based model decompose the image generation process into a sequential application of denoising autoencoders. The diffusion-based model is trained by first applying noise iteratively to a set of training images and then recovering the data by reversing the noising process using denoising autoencoders. The trained diffusion-based model can perform in-painting tasks by sequentially applying the denoising autoencoders to the obstruction identified to generate a new representation from the noise pattern that is coherent with the rest of the image [0150].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See form 892.
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/TUAN A PHAM/Primary Examiner, Art Unit 2163
