公开(公告)号：US11157680B1

公开(公告)日：2021-10-26

申请号：US17183055

申请日：2021-02-23

Applicant: Adobe Inc.

Inventor： Tak Yeon Lee ,  Sana Malik Lee ,  Ryan A. Rossi ,  Qisheng Li ,  Fan Du ,  Eunyee Koh

IPC: G06F40/14 ,  G06F40/197 ,  G06F40/186 ,  G06F3/0482

Abstract: In implementations of systems for suggesting content components, a computing device implements a design system to receive input data describing a feature of a content component to be included in a hypertext markup language (HTML) document. The design system represents that feature of the content component as a document object model (DOM) element and determines a hash value for the DOM element using locality-sensitive hashing. Manhattan distances are computed between the has value and has values described by a segment of content component data. The hash values were determined using the locality-sensitive hashing for DOM elements extracted from a corpus of HTML documents. The design system generates indications, for display in a user interface, of candidate content components for inclusion in the HTML document based on the Manhattan distances.

公开(公告)号：US20200342305A1

公开(公告)日：2020-10-29

申请号：US16394227

申请日：2019-04-25

Applicant: Adobe Inc.

Inventor： Fan Du ,  Eunyee Koh ,  Sungchul Kim ,  Shunan Guo ,  Sana Malik Lee

IPC: G06N3/08 ,  G06N5/02 ,  G06N20/10 ,  G06N7/00

Abstract: Disclosed systems and methods predict and visualize outcomes based on past events. For example, an analysis application encodes a sequence of events into a feature vector that includes, for each event, a numerical representation of a respective category and a respective timestamp. The application applies a time-aware recurrent neural network to the feature vector, resulting in one or more of (i) a set of future events in which each event is associated with a probability and a predicted duration and (ii) a sequence embedding that contains information about predicted outcomes and temporal patterns observed in the sequence of events. The application applies a support vector model classifier to the sequence embedding. The support vector model classifier computes a likelihood of a categorical outcome for each of the events in the probability distribution. The application modifies interactive content according to the categorical outcomes and probability distribution.

公开(公告)号：US20200342006A1

公开(公告)日：2020-10-29

申请号：US16397839

申请日：2019-04-29

Applicant: Adobe Inc.

Inventor： Ryan A. Rossi ,  Eunyee Koh ,  Anup Bandigadi Rao ,  Aldo Gael Carranza

IPC: G06F16/28 ,  G06F16/22

Abstract: In implementations of higher-order graph clustering and embedding, a computing device receives a heterogeneous graph representing a network. The heterogeneous graph includes nodes that each represent a network entity and edges that each represent an association between two of the nodes in the heterogeneous graph. To preserve node-type and edge-type information, a typed graphlet is implemented to capture a connectivity pattern and the types of the nodes and edges. The computing device determines a frequency of the typed graphlet in the graph and derives a weighted typed graphlet matrix to sort graph nodes. Sorted nodes are subsequently analyzed to identify node clusters having a minimum typed graphlet conductance score. The computing device is further implemented to determine a higher-order network embedding for each of the nodes in the graph using the typed graphlet matrix, which can then be concatenated into a matrix representation of the network.

公开(公告)号：US20200285944A1

公开(公告)日：2020-09-10

申请号：US16297024

申请日：2019-03-08

Applicant: Adobe Inc.

Inventor： John Boaz Tsang Lee ,  Ryan Rossi ,  Sungchul Kim ,  Eunyee Koh ,  Anup Rao

IPC: G06N3/04 ,  G06F16/901 ,  G06N3/08 ,  G06F17/16 ,  G06K9/46 ,  G06K9/62

Abstract: Various embodiments describe techniques for making inferences from graph-structured data using graph convolutional networks (GCNs). The GCNs use various pre-defined motifs to filter and select adjacent nodes for graph convolution at individual nodes, rather than merely using edge-defined immediate-neighbor adjacency for information integration at each node. In certain embodiments, the graph convolutional networks use attention mechanisms to select a motif from multiple motifs and select a step size for each respective node in a graph, in order to capture information from the most relevant neighborhood of the respective node.

公开(公告)号：US20200233864A1

公开(公告)日：2020-07-23

申请号：US16252169

申请日：2019-01-18

Applicant: ADOBE INC.

Inventor： Di Jin ,  Ryan A. Rossi ,  Eunyee Koh ,  Sungchul Kim ,  Anup Rao

IPC: G06F16/2458 ,  G06F16/901 ,  G06F16/28 ,  G06F16/26 ,  G06F16/215

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for latent summarization of a graph. Structural features can be captured from feature vectors associated with each node of the graph by applying base functions on the feature vectors and iteratively applying relational operators to successive feature matrices to derive deeper inductive relational functions that capture higher-order structural information in different subgraphs of increasing size (node separations). Heterogeneity can be summarized by performing capturing features in appropriate subgraphs (e.g., node-centric neighborhoods associated with each node type, edge direction, and/or edge type). Binning and/or dimensionality reduction can be applied to the resulting feature matrices. The resulting set of relational functions and multi-level feature matrices can form a latent summary that can be used to perform a variety of graph-based tasks, including node classification, node clustering, link prediction, entity resolution, anomaly and event detection, and inductive learning tasks.

公开(公告)号：US20200177466A1

公开(公告)日：2020-06-04

申请号：US16204616

申请日：2018-11-29

Applicant: Adobe Inc.

Inventor： Ryan A. Rossi ,  Eunyee Koh ,  Sungchul Kim ,  Anup Bandigadi Rao

IPC: H04L12/24 ,  G06F16/901 ,  G06N20/00

Abstract: In implementations of higher-order network embedding, a computing device maintains interconnected data in the form of a graph that represents a network, the graph including nodes that each represent entities in the network and node associations that each represent edges between the nodes in the graph. The computing device includes a network embedding module that is implemented to determine a frequency of k-vertex motifs for each of the edges in the graph, and derive motif-based matrices from the frequency of each of the k-vertex motifs in the graph. The network embedding module is also implemented to determine a higher-order network embedding for each of the nodes in the graph from each of the motif-based matrices. The network embedding module can then concatenate the higher-order network embeddings into a matrix representation.

公开(公告)号：US20190287025A1

公开(公告)日：2019-09-19

申请号：US15920934

申请日：2018-03-14

Applicant: Adobe Inc.

Inventor： Christian Perez ,  Eunyee Koh ,  Ashley Rosie Weiling Chen ,  Ankita Pannu

IPC: G06N99/00 ,  G06N5/04

Abstract: Techniques are disclosed for accurately identifying distinct physical user devices in a cross-device context. An example embodiment applies a multi-phase approach to generate labeled training datasets from a corpus of unlabeled device records. Such labeled training datasets can be used for training machine learning systems to predict the occurrence of device records that have been wrongly (or correctly, as the case may be) attributed to different physical user devices. Such identification of improper attribution can be particularly helpful in web-based analytics. The labeled training datasets include labeled pairs of device records generated using multiple strategies for inferring whether the two device records of a pair of device records represent the same physical user device (or different physical user devices). The labeled pairs of device records can then be used to train classifiers to predict with confidence whether two device records represent or do not represent the same physical user device.

公开(公告)号：US20190056861A1

公开(公告)日：2019-02-21

申请号：US16169877

申请日：2018-10-24

Applicant: Adobe Inc.

Inventor： Byungmoon Kim ,  Jihyun Lee ,  Eunyee Koh

IPC: G06F3/0488 ,  G06F3/023 ,  G02B27/01 ,  G06F3/01

CPC classification number: G06F3/04883 ,  G02B27/017 ,  G02B2027/014 ,  G02B2027/0178 ,  G02B2027/0187 ,  G06F3/011 ,  G06F3/0233 ,  G06F3/0236 ,  G06F3/04886

Abstract: Systems and methods for detecting a user interaction by identifying a touch gesture on a touch interface on a virtual reality headset. The touch gestures are received on a front surface that is on the opposite side of the headset's inner display screen so that correspondence between the touch location and displayed content is intuitive to the user. The techniques of the invention display a cursor and enable the user to move the cursor by one type of input and make selections with the cursor using a second type of input. In this way, the user is able to intuitively control a displayed cursor by moving a finger around (e.g., dragging) on the opposite side of the display in the cursor's approximate location. The user then uses another type of touch input to make a selection at the cursor's current location.