# US 2025/0148005 A1 · Searching for images using generated images > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0148005 A1 - Document ID: US20250148005A1 - Status: Published application - Kind: A1 - Application number: 19/018,306 - Filed: 2025-01-13 - Published: 2025-05-08 - Earliest claimed: 2023-07-29 - Assignees: Adobe Inc. - Applicants: Adobe Inc. - Inventors: Saikat Chakrabarty; Shikhar Garg - Web page: https://patentgrep.com/patent/US20250148005A1 ## CPC - G06F 16/538: Presentation of query results - G06F 16/532: Query formulation, e.g. graphical querying - G06F 16/55: Clustering; Classification - G06F 40/40: Processing or translation of natural language (natural language analysis G06F40/20; semantic analysis G06F40/30) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract In implementations of systems for searching for images using generated images, a computing device implements a search system to receive a natural language search query for digital images included in a digital image repository. The search system generates a set of digital images using a machine learning model based on the natural language search query. The machine learning model is trained on training data to generate digital images based on natural language inputs. The search system performs an image-based search for digital images included in the digital image repository using the set of digital images. An indication of the search result is generated for display in a user interface based on performing the image-based search. ## Claims ### Claim 1 A method comprising: receiving, by a processing device, a search query to locate digital images included in a digital image repository; 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 first visual features of the digital images to second visual features of the generated digital image, the generated digital image being generated by a machine-learning model based on the search query; 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. ### Claim 2 (depends on 1) The method of claim 1 further comprises: generating one or more latent representations of the generated digital image; and assigning each latent representation of the one or more latent representations to a cluster. ### Claim 3 (depends on 2) The method of claim 2, wherein: the generated digital image includes multiple generated digital images; 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. ### Claim 4 (depends on 2) The method of claim 2, wherein the digital images are grouped into the clusters based on perceptual similarities computed for the digital images. ### Claim 5 (depends on 4) The method of claim 4, wherein the perceptual similarities are computed using a learned perceptual image patch similarity loss. ### Claim 6 (depends on 2) The method of claim 2, wherein the order is based on a cluster of the clusters that includes a greatest number of the digital images. ### Claim 7 (depends on 2) 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. ### Claim 8 (depends on 7) 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; 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. ### Claim 9 (depends on 1) The method of claim 1, wherein: the search query comprises a text search query in a natural language format; 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. ### Claim 10 (depends on 9) 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. ### Claim 11 (depends on 1) The method of claim 1, wherein: the search query comprises an input image; 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. ### Claim 12 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; perform, using a generated digital image, an image-based search to locate the digital images included in the digital image repository by comparing first visual features of the digital images to second visual features of the generated digital image, the generated digital image being generated by a 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 forma; 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. ### Claim 13 (depends on 12) The system of claim 12, wherein the latent representation of the generated digital image includes multiple latent representations and each latent representation of the multiple latent representations are assigned to a cluster. ### Claim 14 (depends on 13) The system of claim 13, wherein: the generated digital image includes multiple generated digital images; 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; and the digital images are grouped into the clusters based on perceptual similarities computed for the digital images. ### Claim 15 (depends on 13) The system of claim 13, wherein the order is based on a cluster of the clusters that includes a greatest number of the digital images. ### Claim 16 (depends on 13) The system of claim 13, 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. ### Claim 17 (depends on 16) The system of claim 16, 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; 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. ### Claim 18 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; performing, using a generated digital image, an image-based search to locate the digital images included in the digital image repository by comparing first visual features of the digital images to second visual features of the generated digital image, the generated digital image being generated by a machine-learning model based on the search query; 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. ### Claim 19 (depends on 16) The non-transitory computer-readable storage medium of claim 16, wherein: the latent representation of the generated digital image includes multiple latent representations; each latent representation of the multiple latent representations are assigned to a cluster; and 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. ### Claim 20 (depends on 19) The non-transitory computer-readable storage medium of claim 19, 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; 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.