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 .
Response to Amendment
This Action is responsive to the Applicant’s Amendment/Remarks filed on 01/28/2026. In the Amendment, applicant amended claims 1-2, 6, 10-13, 15 and 18-19. No Terminal Disclaimer has filed, therefore Examiner hereby respectfully maintains claims 1-20 rejection on the ground of nonstatotury double patenting as being unpatentable over claim 1-20 of U.S. Patent No. 12,197496.
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.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,197496. Although the conflicting are not patentably distinct from each other because since the claims of the Patent No. 12,197496 contains every element of the claims of the instant application, and as such, anticipate the claims of the instant application 19/018306. (See table below).
Instant Application claim 1
Patent No. 12,197,496 claim 1
A method comprising: receiving, by a processing device, a search query to locate digital images included in a digital image repository;
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;
performing, by the processing device and using the generated digital image, an image-based search to locate the digital images in the digital image repository by comparing second visual features of the digital images to the first visual features of the generated digital image; generating, by the processing device, latent representations of the digital 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.
A method comprising: receiving, by a processing device, a text search query to locate at least one digital image included in a digital image repository, the text search query being in a natural language format; generating, by the processing device, a set of prompts using a first machine learning model by processing the text search query, the first machine learning model including a natural language model and being trained to generate prompts that cause a second machine learning model to generate digital images that depict visual features that correspond to semantic intents of text search queries in the natural language format, the first machine-learning model including a bidirectional encoder representations from transformers model: generating, by the processing device, a generated digital image with first visual features using the second machine learning model based on the set of prompts, the second machine learning model 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; performing, by the processing device and using the generated digital image as an input, an image-based search to locate the at least one digital image included in the digital image repository by comparing second visual features of the at least one digital image to the first visual features of the generated digital image; presenting, by the processing device, a search result of the at least one digital image in a user interface based on the performing of the image-based search.
Claims 1-7 of Patent No. 12,197496 satisfies all the elements of claims 2-11 of the instant application, and as such, anticipates the claims of instant application.
Instant Application claim 12
Patent No. 12,197496 claim 8
A system comprising: a memory component; and a processing device coupled to the memory component, the processing device to perform operations comprising: receive a text search query in a natural language format to locate digital images included in a digital image repository;
cause a generative machine-learning model to generate a generated digital image based on the text search query, the generated digital image including first visual features that correspond to the text search query, the generative machine-learning model being trained on training data to generate generated digital image with visual features that correspond to sematic intents of training text inputs in the natural language format;
perform, using the generated digital image, an image-based search to locate the digital images in the digital image repository by comparing second visual features of the digital images to the first visual features of the generated digital image; generate latent representations of the digital images; and present 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.
A system comprising: a memory component; and a processing device coupled to the memory component, the processing device to perform operations comprising: receiving a natural language search query for at least one digital image included in a digital image repository; generating a set of prompts for a first machine learning model by processing the natural language search query using a second machine learning model, the second machine learning model including a natural language model and trained to generate prompts that cause the first machine learning model to generate digital images that depict visual features that correspond to semantic intents of natural language search queries, the second machine-learning model including a bidirectional encoder representations from transformers model; generating a generated digital image by processing the set of prompts using the first machine learning model using generative artificial intelligence (AI) that includes first visual features, the first machine learning model trained to generate generated digital images with visual features that correspond to the semantic intents of prompts; and presenting a search result of an image-based search performed in the digital image repository using the generated digital image as an input, the search result including at least one digital image with second visual features similar to the first visual features.
Claims 13-17 of Patent No. 12,197496 satisfies all the elements of claims 8-13 of the instant application, and as such, anticipates the claims of instant application.
Instant Application claim 18
Patent No. 12,197496 claim 14
A non-transitory computer-readable storage medium storing executable instructions, which when executed by a processing device, cause the processing device to perform operations comprising: receiving a search query to locate digital images included in a digital image repository;
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;
performing, using the 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; generating latent representations of the digital images; and presenting 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.
A non-transitory computer-readable storage medium storing executable instructions, which when executed by a processing device, cause the processing device to perform operations comprising: receiving a text search query for digital images included in a digital image repository, the text search query being in a natural language format; generating a set of prompts using a first machine learning model by processing the text search query, the first machine learning model including a natural language model and being trained to generate prompts that cause a second machine learning model to generate digital images that depict visual features that correspond to semantic intents of text search queries in the natural language format, the first machine-learning model including a bidirectional encoder representations from transformers model; generating a set of digital images that includes first visual features using the second machine learning model based on the set of prompts, the second machine learning model trained on training data to generate generated digital images with visual features that correspond to semantic intents of text inputs in the natural language format; receiving a search result based on performing an image-based search for digital images included in the digital image repository by comparing second visual features of the digital images to the first visual features of the set of digital images; and presenting the search result for display in a user interface.
Claims 14-20 of Patent No. 12,197496 satisfies all the elements of claims 18-20 of the instant application, and as such, anticipates the claims of instant application.
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 nonobviousness.
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).
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], “...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...”). 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]).
As per as claim 2, the combination of 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 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 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 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 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 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 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 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 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 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 01/28/2026 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).
Issue I: Regarding to the claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,197496.
Response I: No Terminal Disclaimer has filed, therefore the examiner respectfully maintains the nonstatutory double patenting rejection to claims 1-20 as being unpatentable over claims 1-20 of U.S. Patent No. 12,197496.
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. Saraee et al. (US PGPUB 2022/0335256, hereinafter Saraee) “ Systems, Methods, And Storage Media For Training A Model For Image Evaluation” discloses “extract a first plurality of features from a plurality of first training images and a second plurality of features from a second training image; generating a model comprising a first image performance score for each of the plurality of first training images and a feature weight for each feature, the feature weight for each feature of the first plurality of features calculated based on an impact of a variation in the feature on first image performance scores of the plurality of first training images; training the model by adjusting the impact of a variation of each of a first set of features that correspond to the second plurality of features”.
Saraee also teaches determining whether the candidate image may be similar to the set of training images includes comparing the modified candidate image feature tensor with the first generative model [0008].
Saraee further teaches extracting a second set of features from each training image to generate a second feature tensor for each training image. The method may include reducing a dimensionality of each first feature tensor to generate a first modified feature tensor for each training image [0018-0019].
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TUAN A PHAM whose telephone number is (571)270-3173. The examiner can normally be reached M-F 7:45 AM - 6:30 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached on 571-272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/TUAN A PHAM/Primary Examiner, Art Unit 2163