公开(公告)号：US12174907B2

公开(公告)日：2024-12-24

申请号：US18061697

申请日：2022-12-05

Applicant: Adobe Inc.

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

IPC: G06F17/10 ,  G06F16/901 ,  G06F17/16 ,  G06F18/21 ,  G06F18/24 ,  G06N3/047 ,  G06N3/08 ,  G06V10/426 ,  G06V10/82

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：US20240311581A1

公开(公告)日：2024-09-19

申请号：US18185547

申请日：2023-03-17

Applicant: ADOBE INC.

Inventor： Arpit Narechania ,  Fan Du ,  Atanu Sinha ,  Nedim Lipka ,  Alexa F. Siu ,  Jane Elizabeth Hoffswell ,  Eunyee Koh ,  Vasanthi Holtcamp

IPC: G06F40/40 ,  G06F40/279 ,  G06F40/30 ,  G06V30/19 ,  G06V30/412

CPC classification number: G06F40/40 ,  G06F40/279 ,  G06F40/30 ,  G06V30/19147 ,  G06V30/412

Abstract: Aspects of the method, apparatus, non-transitory computer readable medium, and system include obtaining a document and an information element. The aspects further include identifying, from the document, an anchor element that has an anchor type and a relationship type, wherein the anchor type describes a structure of a set of anchor elements, and the relationship type describes a relationship between the anchor element and the information element. The aspects further include extracting information corresponding to the information element based on the anchor element, the anchor type, and the relationship type, and displaying the extracted information to a user.

公开(公告)号：US20230418881A1

公开(公告)日：2023-12-28

申请号：US17809371

申请日：2022-06-28

Applicant: ADOBE INC.

Inventor： Shunan Guo ,  Yeuk-Yin Chan ,  Eunyee Koh ,  Caroline Jiwon Kim ,  Cole Edward Connelly ,  Andrew Douglas Thomson

IPC: G06F16/93 ,  G06F40/103 ,  G06F40/14 ,  G06F40/166

CPC classification number: G06F16/93 ,  G06F40/103 ,  G06F40/14 ,  G06F40/166

Abstract: Systems and methods for document generation are provided. One aspect of the systems and methods includes identifying, by a style extractor, a document fragment comprising a first style element of a first style category; computing, by a style generator, a reward function based on a correlation value between the first style element and a second style element of a second style category different from the first style category, wherein the correlation value is based on correlations between style elements in a plurality of historical document fragments; selecting, by the style generator, the second style element based on the reward function; and generating, by a document generator, a modified document fragment that includes the first style element of the first style category and the second style element of the second style category.

公开(公告)号：US20230350968A1

公开(公告)日：2023-11-02

申请号：US17661641

申请日：2022-05-02

Applicant: Adobe Inc.

Inventor： Irgelkha Mejia ,  Michele Saad ,  Eunyee Koh ,  Andrew Thomson ,  Lauren Dest ,  Dustin Ground ,  Anna Hammond ,  Arjun Athreya ,  Catherine Chiodo

IPC: G06F16/957 ,  G06F16/958 ,  G06F16/9536

CPC classification number: G06F16/9577 ,  G06F16/958 ,  G06F16/9536

Abstract: Methods, systems, and non-transitory computer readable media are disclosed for utilizing machine learning models to extract digital signals from low-results web queries and generate item demand deficiency predictions for digital item lists corresponding to websites. In one or more embodiments, the disclosed systems identify a low-results query submitted by client devices navigating a website. The disclosed systems generate features for the low-results query and the digital item list to generate a deficiency prediction relative to demand indicated by the low-results query. In some embodiments, the disclosed system utilizes a deficiency prediction model to process the extracted signals and generate a deficiency confidence score corresponding to the low-results query. Based on the deficiency confidence score, the disclosed system can generate and provide demand notifications via one or more graphical user interfaces.

公开(公告)号：US20230289696A1

公开(公告)日：2023-09-14

申请号：US17693778

申请日：2022-03-14

Applicant: ADOBE INC.

Inventor： Arpit Ajay Narechania ,  Fan Du ,  Atanu R. Sinha ,  Ryan A. Rossi ,  Jane Elizabeth Hoffswell ,  Shunan Guo ,  Eunyee Koh ,  John Anderson ,  Sonali Surange ,  Saurabh Mahapatra ,  Vasanthi Holtcamp

IPC: G06Q10/06

CPC classification number: G06Q10/06393 ,  G06F3/0482

Abstract: Embodiments provide systems, methods, and computer storage media for management, assessment, navigation, and/or discovery of data based on data quality, consumption, and/or utility metrics. Data may be assessed using attribute-level and/or record-level metrics that quantify data: “quality”—the condition of data (e.g., presence of incorrect or incomplete values), its “consumption”—the tracked usage of data in downstream applications (e.g., utilization of attributes in dashboard widgets or customer segmentation rules), and/or its “utility”—a quantifiable impact resulting from the consumption of data (e.g., revenue or number of visits resulting from marketing campaigns that use particular datasets, storage costs of data). This data assessment may be performed at different stages of a data intake, preparation, and/or modeling lifecycle. For example, an interactive tree view may visually represent a nested attribute schema and attribute quality or consumption metrics to facilitate discovery of bad data before ingesting into a data lake.

公开(公告)号：US11544535B2

公开(公告)日：2023-01-03

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 ,  G06K9/62 ,  G06F17/16 ,  G06V10/426

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：US11341204B2

公开(公告)日：2022-05-24

申请号：US16419676

申请日：2019-05-22

Applicant: Adobe Inc.

Inventor： Tak Yeon Lee ,  Jonggi Hong ,  Eunyee Koh

IPC: G06F16/955 ,  G06N20/00 ,  G06F9/451 ,  G06F16/93

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for determining and resolving misalignments between digital messages containing links and corresponding external digital content. For example, in one or more embodiments, the disclosed systems extract a plurality of alignment classification features from a digital link in a digital message and corresponding external digital content. Based on the alignment classification features and using a machine learning classification model, the disclosed system can generate alignment probability scores for a plurality of misalignment classes. The disclosed system can report identified misalignments of corresponding misalignment classes in a misalignment identification user interface. Furthermore, the disclosed system can receive publisher input via the misalignment identification user interface to further personalize the machine learning classification model.

公开(公告)号：US11170048B2

公开(公告)日：2021-11-09

申请号：US16451956

申请日：2019-06-25

Applicant: Adobe Inc.

Inventor： Ryan Rossi ,  Aldo Gael Carranza ,  David Arbour ,  Anup Rao ,  Sungchul Kim ,  Eunyee Koh

IPC: G06F16/00 ,  G06F16/901 ,  G06F17/18

Abstract: A system is disclosed for identifying and counting typed graphlets in a heterogeneous network. A methodology implementing techniques for the disclosed system according to an embodiment includes identifying typed k-node graphlets occurring between any two selected nodes of a heterogeneous network, wherein the nodes are connected by one or more edges. The identification is based on combinatorial relationships between (k−1)-node typed graphlets occurring between the two selected nodes of the heterogeneous network. Identification of 3-node typed graphlets is based on computation of typed triangles, typed 3-node stars, and typed 3-paths associated with each edge connecting the selected nodes. The method further includes maintaining a count of the identified k-node typed graphlets and storing those graphlets with non-zero counts. The identified graphlets are employed for applications including visitor stitching, user profiling, outlier detection, and link prediction.

公开(公告)号：US11163803B2

公开(公告)日：2021-11-02

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：US11144939B2

公开(公告)日：2021-10-12

申请号：US14959890

申请日：2015-12-04

Applicant: Adobe Inc.

Inventor： Eunyee Koh ,  Nedim Lipka

IPC: G06Q30/02 ,  H04L29/08 ,  H04L12/24

Abstract: An analytics server receives data characterizing consumer interactions that are observed by a cross-section of data providers, which may include, for example, website administrators, campaign managers, application developers, and the like. Such observational data includes device and login identifiers for a particular interaction, and optionally, timestamp information indicating when the interaction occurred. A statistical device graph model is generated based on this observational data. The statistical device graph model allows inferences to be drawn with respect to whether a given device is a private device, a shared device, or a public device. This, in turn, allows private devices which are “owned” by a single consumer to be identified. Depending on the type of observational data collected by the data providers, a wide range of additional insights can be drawn from the statistical device graph model, including for example, device usage patterns and confidence levels.