公开(公告)号：US20230080407A1

公开(公告)日：2023-03-16

申请号：US17475145

申请日：2021-09-14

Applicant: Adobe Inc.

Inventor： Jayant Kumar ,  Manasi Deshmukh ,  Ming Liu ,  Ashok Gupta ,  Karthik Suresh ,  Chirag Arora ,  Jing Zheng ,  Ravindra Sadaphule ,  Vipul Dalal ,  Andrei Stefan

IPC: G06F9/451 ,  G06T11/60 ,  G06N5/02 ,  G06F40/20

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that generate a digital knowledge graph based on a plurality of tutorial content items to generate recommendations of digital resource items. Specifically, the disclosed system extracts a plurality of tasks, subject categories related to the tasks, and context signals related to an environment for the tasks from a plurality of tutorial content items for one or more digital content editing applications. The disclosed system generates a digital knowledge graph including nodes corresponding to the tasks and subject categories connected via edges based on relationships extracted from the tutorial content items. In some embodiments, the disclosed system also includes nodes corresponding to digital resource items in the digital knowledge graph or in a subgraph. The disclosed system utilizes the digital knowledge graph with context data to provide a recommendation of digital resource items for display at a client device.

公开(公告)号：US11775734B2

公开(公告)日：2023-10-03

申请号：US17534937

申请日：2021-11-24

Applicant: Adobe Inc.

Inventor： Sanat Sharma ,  Jing Zheng ,  Jayant Kumar

IPC: G06F40/109 ,  G06N3/02 ,  G06N5/02

CPC classification number: G06F40/109 ,  G06N3/02 ,  G06N5/02

Abstract: Embodiments are disclosed for receiving a modal input including at least one of a text input or an image input. The method may include extracting an intent label from the modal input. The method may further include generating, by an intent embedding generator, an intent embedding from the intent label. The method may further include comparing the intent embedding to a plurality of candidate font embeddings to obtain one or more candidate fonts based on a similarity of the intent embedding to the plurality of candidate font embeddings in an embedding space. The method may further include identifying a recommended font based on the similarity of the intent embedding to a selected candidate font embedding of the plurality of candidate font embeddings.

公开(公告)号：US11645095B2

公开(公告)日：2023-05-09

申请号：US17475145

申请日：2021-09-14

Applicant: Adobe Inc.

Inventor： Jayant Kumar ,  Manasi Deshmukh ,  Ming Liu ,  Ashok Gupta ,  Karthik Suresh ,  Chirag Arora ,  Jing Zheng ,  Ravindra Sadaphule ,  Vipul Dalal ,  Andrei Stefan

IPC: G06F9/451 ,  G06F40/20 ,  G06N5/022 ,  G06T11/60

CPC classification number: G06F9/453 ,  G06F40/20 ,  G06N5/022 ,  G06T11/60

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that generate a digital knowledge graph based on a plurality of tutorial content items to generate recommendations of digital resource items. Specifically, the disclosed system extracts a plurality of tasks, subject categories related to the tasks, and context signals related to an environment for the tasks from a plurality of tutorial content items for one or more digital content editing applications. The disclosed system generates a digital knowledge graph including nodes corresponding to the tasks and subject categories connected via edges based on relationships extracted from the tutorial content items. In some embodiments, the disclosed system also includes nodes corresponding to digital resource items in the digital knowledge graph or in a subgraph. The disclosed system utilizes the digital knowledge graph with context data to provide a recommendation of digital resource items for display at a client device.