公开(公告)号：US20200320165A1

公开(公告)日：2020-10-08

申请号：US16376906

申请日：2019-04-05

Applicant: Adobe Inc.

Inventor： Balaji Vasan Srinivasan ,  Surya S. Dwivedi ,  Rohan Kumar ,  Pranav Ravindra Maneriker ,  Paridhi Maheshwari ,  Nitish Bansal

IPC: G06F17/24 ,  G06F17/21 ,  G06T7/11 ,  G06T11/60

Abstract: A method includes extracting a set of segments located in a reference single page graphic image. A first segment overlaps with a second segment of the set of segments. The method includes identifying a plurality of bounding areas within the reference single page graphic image. Each segment of the set of segments is associated with a bounding area of the plurality of bounding areas. The plurality of bounding areas includes a first bounding area and a second bounding area, the first bounding area overlapping with the second bounding area. The method includes generating an editable template including a set of editable fields. The set of editable fields is determined based upon the plurality of bounding areas in the reference single page graphic image. A position of an editable field in the editable template is based upon a position in the reference single page graphic image of a corresponding bounding area.

公开(公告)号：US20240202876A1

公开(公告)日：2024-06-20

申请号：US18067989

申请日：2022-12-19

Applicant: Adobe Inc.

Inventor： Tripti Shukla ,  Kuldeep Kulkarni ,  Paridhi Maheshwari

IPC: G06T5/50 ,  G06V10/82 ,  G06V20/70

CPC classification number: G06T5/50 ,  G06V10/82 ,  G06V20/70 ,  G06T2207/20221

Abstract: Techniques are described for object insertion via scene graph. In implementations, given an input image and a region of the image where a new object is to be inserted, the input image is converted to an intermediate scene graph space. In the intermediate scene graph space, graph convolutional networks are leveraged to expand the scene graph by predicting the identity and relationships of a new object to be inserted, taking into account existing objects in the input image. The expanded scene graph and the input image are then processed by an image generator to insert a predicted visual object into the input image to produce an output image.

公开(公告)号：US20240119646A1

公开(公告)日：2024-04-11

申请号：US18541377

申请日：2023-12-15

Applicant: Adobe Inc.

Inventor： Paridhi Maheshwari ,  Vishwa Vinay ,  Shraiysh Vaishay ,  Praneetha Vaddamanu ,  Nihal Jain ,  Dhananjay Bhausaheb Raut

IPC: G06T11/00 ,  G06F40/30 ,  G06V10/40

CPC classification number: G06T11/001 ,  G06F40/30 ,  G06V10/40 ,  G06T2207/10024 ,  G06T2207/20081

Abstract: Digital image text editing techniques as implemented by an image processing system are described that support increased user interaction in the creation and editing of digital images through understanding a content creator's intent as expressed using text. In one example, a text user input is received by a text input module. The text user input describes a visual object and a visual attribute, in which the visual object specifies a visual context of the visual attribute. A feature representation generated by a text-to-feature system using a machine-learning module based on the text user input. The feature representation is passed to an image editing system to edit a digital object in a digital image, e.g., by applying a texture to an outline of the digital object within the digital image.

公开(公告)号：US11682031B2

公开(公告)日：2023-06-20

申请号：US17376405

申请日：2021-07-15

Applicant: ADOBE INC.

Inventor： Paridhi Maheshwari ,  Tanay Anand ,  Atanu Sinha

IPC: G06Q30/0202 ,  G06Q30/0226 ,  G06Q30/0201 ,  G06F18/2415

CPC classification number: G06Q30/0202 ,  G06F18/2415 ,  G06Q30/0201 ,  G06Q30/0226

Abstract: A method for predicting user purchase by a user of a first site includes: selecting a distribution representing a probability distribution (PD) of inter-purchase-times (IPTs) across the first site and a second other site for each user, assigning each purchase of each user to one of the first site and the second site according to a Stochastic model, combining the selected PD with the Stochastic model to generate a PD of IPTs for only the first online site, estimating parameters of the probability distribution of IPTs for the first site by applying a Statistical modeling approach to features of each user, applying a sequence of observed IPTs of a given user for the first site and the parameters of the given user to the selected distribution to generate a probability, and determining whether the next purchase occurs on the second site based on the probability.

公开(公告)号：US20230015978A1

公开(公告)日：2023-01-19

申请号：US17376405

申请日：2021-07-15

Applicant: ADOBE INC.

Inventor： Paridhi Maheshwari ,  Tanay Anand ,  Atanu Sinha

IPC: G06Q30/02 ,  G06K9/62

Abstract: A method for predicting user purchase by a user of a first site includes: selecting a distribution representing a probability distribution (PD) of inter-purchase-times (IPTs) across the first site and a second other site for each user, assigning each purchase of each user to one of the first site and the second site according to a Stochastic model, combining the selected PD with the Stochastic model to generate a PD of IPTs for only the first online site, estimating parameters of the probability distribution of IPTs for the first site by applying a Statistical modeling approach to features of each user, applying a sequence of observed IPTs of a given user for the first site and the parameters of the given user to the selected distribution to generate a probability, and determining whether the next purchase occurs on the second site based on the probability.

公开(公告)号：US11537787B2

公开(公告)日：2022-12-27

申请号：US17188302

申请日：2021-03-01

Applicant: Adobe Inc.

Inventor： Sumit Shekhar ,  Vedant Raval ,  Tripti Shukla ,  Simarpreet singh Saluja ,  Paridhi Maheshwari ,  Divyam Gupta

IPC: G06F17/00 ,  G06F40/186 ,  G06F40/106 ,  G06K9/62 ,  G06N3/04

Abstract: Certain embodiments involve a template-based redesign of documents based on the contents of documents. For instance, a computing system selects a template for modifying an input document. To do so, the computing system uses a generative adversarial network to generate an interpolated layout image from an input layout image, which represents the input document, and a template layout image, which represents the selected template. The computing system matches the input element to an interpolated element from the interpolated layout image. The computing system generates an output document by, for example, modifying a layout of the input document to match the interpolated layout image, such as by fitting the input element into a shape of the interpolated element.

公开(公告)号：US11354513B2

公开(公告)日：2022-06-07

申请号：US16784000

申请日：2020-02-06

Applicant: Adobe Inc.

Inventor： Natwar Modani ,  Srinivas Saurab Sirpurkar ,  Paridhi Maheshwari ,  Harsh Deshpande ,  Diviya Singh

IPC: G06F40/279 ,  G06F40/131 ,  G06F40/30 ,  G06F9/30 ,  G06F40/216 ,  G06V30/416

Abstract: A technique for intelligently identifying concept labels for a text fragment where the identified concept labels are representative of and semantically relevant to the information contained by the text fragment is provided. The technique includes determining, using a knowledge base storing information for a reference set of concept labels, a first subset of concept labels that are relevant to the information contained by the text fragment. The technique includes ordering the first subset of concept labels according to their relevance scores and performing dependency analysis on the ordered list of concept labels. Based on the dependency analysis, the technique includes identifying concept labels for a text fragment that are more independent (e.g., more distinct and non-overlapping) of each other, representative of and semantically relevant to the information represented by the text fragment.

公开(公告)号：US20210248323A1

公开(公告)日：2021-08-12

申请号：US16784145

申请日：2020-02-06

Applicant: Adobe Inc.

Inventor： Paridhi Maheshwari ,  Harsh Deshpande ,  Diviya Singh ,  Natwar Modani ,  Srinivas Saurab Sirpurkar

IPC: G06F40/30 ,  G06K9/00 ,  G06F40/216

Abstract: Techniques are described for intelligently identifying concept labels for a set of multiple documents where the identified concept labels are representative of and semantically relevant to the information contained by the set of documents. The technique includes extracting semantic units (e.g., paragraphs) from the set of documents and determining concept labels applicable to the semantic units based on relevance scores computed for the concept labels. The technique includes determining an initial set of concept labels for the set of documents based on the applicable concept labels. The technique further includes obtaining a reference hierarchy associated with the reference set of concept labels and determining a final set of concept labels for the set of documents using a reference hierarchy, the initial set of concept labels, and the relevance scores. The technique includes outputting information identifying the final set of concept labels for the set of documents.

公开(公告)号：US10915577B2

公开(公告)日：2021-02-09

申请号：US15928288

申请日：2018-03-22

Applicant: ADOBE INC.

Inventor： Balaji Vasan Srinivasan ,  Rajat Chaturvedi ,  Tanya Goyal ,  Paridhi Maheshwari ,  Anish Valliyath Monsy ,  Abhilasha Sancheti

IPC: G06F16/907 ,  G06F16/901 ,  G06F16/81

Abstract: A framework is provided for constructing enterprise-specific knowledge bases from enterprise-specific data that includes structured and unstructured data. Relationships between entities that match known relationships are identified for each of a plurality of tuples included in the structured data. Where possible, relationships between entities that match known relationships also are identified for tuples included in the unstructured data. If matching relationships between entities that cannot be identified for tuples in the unstructured data, extracted relationships are sequentially clustered to similar relationships and a relationship is assigned to the clustered tuples. An enterprise-specific knowledge graph is constructed from the structured-data-tuples and their identified relationships, the unstructured-data-tuples where the relationships could be mapped to a known relationship and their identified relationships, and the unstructured-data-tuples that could not be mapped to a known relationship and their assigned relationships. The knowledge graph is enriched with any information determined to be missing therefrom.