# US 2026/0087633 A1 · Automatic image cropping using generative artificial intelligence > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0087633 A1 - Document ID: US20260087633A1 - Status: Published application - Kind: A1 - Application number: 18/893,571 - Filed: 2024-09-23 - Published: 2026-03-26 - Earliest claimed: 2024-09-23 - Assignees: Adobe Inc. - Applicants: Adobe Inc. - Inventors: Ashish CHOPRA; Vangala Naveen REDDY - Web page: https://patentgrep.com/patent/US20260087633A1 ## CPC - G06T 7/10: Segmentation; Edge detection (motion-based segmentation G06T7/215) - G06F 3/0482: Interaction with lists of selectable items, e.g. menus - G06F 3/04845: for image manipulation, e.g. dragging, rotation, expansion or change of colour - G06T 3/40: Scaling of whole images or parts thereof, e.g. expanding or contracting - G06V 10/25: Determination of region of interest [ROI] or a volume of interest [VOI] - G06V 10/82: using neural networks - G06T 2207/20132: Image cropping - G06V 2201/07: Target detection - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Some aspects relate to technologies providing a framework for automatically cropping images. In accordance with some aspects, a list of objects that are present in at least on image of a set of images is generated and that list is combined with a list of desired objects obtained from a content brief. In some aspects, the items in this combined list is ranked and this ranked list is used to detect and rank regions within images of the set of images that correspond to the combined and ranked list. In some aspects, these detected and ranked regions are used to inform the image cropping. ## Claims ### Claim 1 One or more computer storage media storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform operations, the operations comprising: obtaining a source image; obtaining a set of guidelines for cropping the source image; generating a list of objects present in the source image, using an object recognition model; generating a list of desired objects based, at least in part, on the set of guidelines for cropping the source image; combining the list of objects and the list of desired objects to generate a list of object keywords; identifying regions of the source image that contain at least one object of the list of objects based, at least in part, on the list of object keywords; and generating a cropped image from the source image, of a desired image size specified in the set of guidelines, wherein the cropped image at least includes a selected identified region of the identified regions. ### Claim 2 (depends on 1) The one or more computer storage media of claim 1, wherein: the source image is one of a plurality of images; and generating the list of objects comprises generating a list of objects that are present in at least one image of the plurality of images, using the object recognition model. ### Claim 3 (depends on 1) The one or more computer storage media of claim 1, wherein the operations further comprise: assigning a ranking to each of the object keywords of the list of object keywords; and wherein the selected identified region is selected based, at least in part, on the rankings of the object keywords of objects in the selected identified region. ### Claim 4 (depends on 1) The one or more computer storage media of claim 1, wherein: the set of guidelines for cropping the source image comprises a content brief that indicates a type of desired content and one or more desired image sizes; and the list of desired objects is generated using a large-language model (LLM) based, at least in part, on the content brief. ### Claim 5 (depends on 1) The one or more computer storage media of claim 1, wherein the operations further comprise: augmenting the list of object keywords by removing one or more object keywords from the list of object keywords. ### Claim 6 (depends on 1) The one or more computer storage media of claim 1, wherein the operations further comprise: augmenting the list of object keywords by adding one or more object keywords to the list of object keywords. ### Claim 7 (depends on 1) The one or more computer storage media of claim 1, wherein the source image is a frame of a video comprising a plurality of frames. ### Claim 8 (depends on 1) The one or more computer storage media of claim 1, wherein combining the list of objects and the list of desired objects to generate a list of object keywords uses a large language model (LLM). ### Claim 9 (depends on 1) The one or more computer storage media of claim 1, wherein identifying regions of the source image that contain at least one object of the list of objects comprises: segmenting the source image using a segment anything model (SAM) to generate a list of identified regions; assigning a ranking to the identified regions of the list of identified regions; combining the list of identified regions and the list of object keywords to generate a list of objects with regions; sorting the list of objects with regions based, at least in part, on the ranking of the identified regions; and identifying the regions of the source image that contain at least one object of the list of objects based, at least in part, on the sorted list of objects with regions;. ### Claim 10 A computer-implemented method comprising: generating, by an object detection component, a list of objects present in a digital asset selected from a set of digital assets; generating, by an object inference component, a list of desired objects based, at least in part, on a set of guidelines for cropping digital assets; combining, by the object inference component, the list of objects and the list of desired objects to generate a list of object keywords; assigning, by the object inference component, a ranking to each object keyword in the list of object keywords to generate a ranked list of object keywords; augmenting, by an object augmentation component, the ranked list of object keywords by removing object keywords from the ranked list of object keywords based, at least in part, on the ranking of the object keywords; identifying, by an object region detection and re-ranking component, regions of the selected digital asset that contain at least one object of the list of objects based, at least in part, on the ranked list of object keywords; and generating, by an image cropping component, a cropped version of the selected digital asset that at least includes a selected identified region of the identified regions. ### Claim 11 (depends on 10) The computer-implemented method of claim 10, wherein the set of digital assets comprises one or more images. ### Claim 12 (depends on 10) The computer-implemented method of claim 10, wherein the set of digital assets comprises one or more videos. ### Claim 13 (depends on 10) The computer-implemented method of claim 10, wherein the cropped version of the selected digital asset is cropped based, at least in part, on an image size indicated by the set of guidelines for cropping digital assets. ### Claim 14 (depends on 10) The computer-implemented method of claim 10, wherein the cropped version of the selected digital asset is cropped based, at least in part, on an aspect ratio indicated by the set of guidelines for cropping digital assets. ### Claim 15 (depends on 10) The computer-implemented method of claim 10, wherein: the list of objects comprises a list of objects that are present in at least one digital asset of the set of digital assets; and each object of the list of objects has an assigned ranking based, at least in part, on a number of occurrences of the object in the set of digital assets. ### Claim 16 A computer system comprising: one or more processors; and one or more computer storage media storing computer-useable instructions that, when used by the one or more processors, causes the computer system to perform operations comprising: generating, by an object detection component, a list of objects present in a at least one image of an image corpus; generating, by an object inference component, a list of desired objects based, at least in part, on a set of guidelines obtained from a content brief; combining, by the object inference component, the list of objects and the list of desired objects to generate a list of object keywords; assigning, by the object inference component, a ranking to each object keyword in the list of object keywords to generate a ranked list of object keywords; identifying, by an object region detection and re-ranking component, regions of a selected image of the image corpus that contain at least one object of the list of objects based, at least in part, on the ranked list of object keywords; and generating, by an image cropping component, a cropped version of the selected image that at least includes a selected identified region of the identified regions. ### Claim 17 (depends on 15) The computer system of claim 15, wherein the operations further comprise: augmenting, by an object augmentation component, the ranked list of object keywords by removing one or more object keywords from the list of object keywords based, at least in part, on the assigned ranking. ### Claim 18 (depends on 15) The computer system of claim 15, wherein: desired objects are assigned ranking that is a positive number; and restricted objects are assigned a ranking that is a negative number. ### Claim 19 (depends on 15) The computer system of claim 15, wherein generating the list of object keywords uses a large language model (LLM). ### Claim 20 (depends on 15) The computer system of claim 15, wherein the regions of the selected image are identified using a grounded segment anything model that generates a list of identified regions and assigns a ranking to the identified regions of the list of identified regions.