公开(公告)号：US20250036936A1

公开(公告)日：2025-01-30

申请号：US18358502

申请日：2023-07-25

Applicant: ADOBE INC.

Inventor： Ryan A. Rossi ,  Ryan Aponte ,  Shunan Guo ,  Jane Elizabeth Hoffswell ,  Nedim Lipka ,  Chang Xiao ,  Yeuk-yin Chan ,  Eunyee Koh

IPC: G06N3/08

Abstract: A method, apparatus, and non-transitory computer readable medium for hypergraph processing are described. Embodiments of the present disclosure obtain, by a hypergraph component, a hypergraph that includes a plurality of nodes and a hyperedge, wherein the hyperedge connects the plurality of nodes; perform, by a hypergraph neural network, a node hypergraph convolution based on the hypergraph to obtain an updated node embedding for a node of the plurality of nodes; and generate, by the hypergraph component, an augmented hypergraph based on the updated node embedding.

公开(公告)号：US20250036858A1

公开(公告)日：2025-01-30

申请号：US18225906

申请日：2023-07-25

Applicant: Adobe Inc.

Inventor： Ryan Rossi ,  Ryan Aponte ,  Shunan Guo ,  Nedim Lipka ,  Jane Hoffswell ,  Chang Xiao ,  Eunyee Koh ,  Yeuk-yin Chan

IPC: G06F40/154 ,  G06F40/117 ,  G06F40/143

Abstract: Techniques discussed herein generally relate to applying machine-learning techniques to design documents to determine relationships among the different style elements within the document. In one example, hypergraph model is trained on a corpus of hypertext markup language (HTML) documents. The trained model is utilized to identifying one or more candidate style elements for a candidate fragment and/or a candidate fragment. Each of the candidates are scored, and at least a portion of the scored candidates are presented as design options for generating a new document.