公开(公告)号：US20230419015A1

公开(公告)日：2023-12-28

申请号：US17849271

申请日：2022-06-24

Applicant: Adobe Inc.

Inventor： Peter Evan O'Donovan ,  Siddartha Reddy Turpu ,  Razvan Cotlarciuc ,  Oliver Markus Michael Brdiczka ,  Nipun Jindal ,  Costin-Stefan Ion

IPC: G06F40/109 ,  G06F40/211 ,  G06F40/186

CPC classification number: G06F40/109 ,  G06F40/211 ,  G06F40/186

Abstract: Font recommendation techniques are described that provide recommendations of fonts based on a variety of factors, automatically and without user intervention in real time. This is performable in a variety of ways by addressing a wide range of considerations as part of machine learning, examples of which include context, popularity, similarity, customization, and topic compatibility.

公开(公告)号：US11947896B2

公开(公告)日：2024-04-02

申请号：US17848795

申请日：2022-06-24

Applicant: Adobe Inc.

Inventor： Peter Evan O'Donovan ,  Siddartha Reddy Turpu ,  Razvan Cotlarciuc ,  Oliver Markus Michael Brdiczka ,  Nipun Jindal ,  Costin-Stefan Ion

IPC: G06F40/109 ,  G06F16/583 ,  G06F40/126 ,  G06V30/413

CPC classification number: G06F40/109 ,  G06F16/5846 ,  G06F40/126 ,  G06V30/413

Abstract: Font recommendation techniques are described that provide recommendations of fonts based on a variety of factors, automatically and without user intervention in real time. This is performable in a variety of ways by addressing a wide range of considerations as part of machine learning, examples of which include context, popularity, similarity, customization, and topic compatibility.

公开(公告)号：US20230419014A1

公开(公告)日：2023-12-28

申请号：US17848795

申请日：2022-06-24

Applicant: Adobe Inc.

Inventor： Peter Evan O'Donovan ,  Siddartha Reddy Turpu ,  Razvan Cotlarciuc ,  Oliver Markus Michael Brdiczka ,  Nipun Jindal ,  Costin-Stefan Ion

IPC: G06F40/109 ,  G06F40/126 ,  G06V30/413 ,  G06F16/583

CPC classification number: G06F40/109 ,  G06F16/5846 ,  G06V30/413 ,  G06F40/126

Abstract: Font recommendation techniques are described that provide recommendations of fonts based on a variety of factors, automatically and without user intervention in real time. This is performable in a variety of ways by addressing a wide range of considerations as part of machine learning, examples of which include context, popularity, similarity, customization, and topic compatibility.