-
公开(公告)号：US20230130778A1
公开(公告)日：2023-04-27
申请号：US18069561
申请日：2022-12-21
Applicant: Adobe Inc.
Inventor： Shenyu Xu , Eunyee Koh , Fan Du , Tak Yeon Lee , Sana Malik Lee , Ryan Rossi
IPC: G06F16/738 , G06F16/901 , G06F16/783 , G06F16/34 , G06F16/9032 , G06F16/44
Abstract: This disclosure describes one or more embodiments of systems, non-transitory computer-readable media, and methods that intelligently and automatically analyze input data and generate visual data stories depicting graphical visualizations from data insights determined from the input data. For example, the disclosed systems automatically extract data insights utilizing an in-depth statistical analysis of dataset groups from data-attribute categories within the input data. Based on the data insights, the disclosed systems can automatically generate exportable visual data stories to visualize the data insights, provide textual or audio-based natural language summaries of the data insights, and animate such data insights in videos. In some embodiments, the disclosed systems generate a visual-data-story graph comprising nodes representing visual data stories and edges representing similarities between the visual data stories. Based on the visual-data-story graph, the disclosed systems can select a relevant visual data story to display on a graphical user interface.
-
公开(公告)号：US11630854B2
公开(公告)日：2023-04-18
申请号：US17660328
申请日：2022-04-22
Applicant: Adobe Inc.
Inventor： Fan Du , Yeuk-Yin Chan , Eunyee Koh , Ryan Rossi , Margarita Savova , Charles Menguy , Anup Rao
IPC: G06F16/00 , G06F16/28 , G06F16/22 , G06F16/14 , G06F16/84 , G06F16/2458 , G06F16/909
Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for utilizing hash partitions to determine local densities and distances among users (or among other represented data points) for clustering sparse data into segments. For instance, the disclosed systems can generate hash signatures for users in a sparse dataset and can map users to hash partitions based on the hash signatures. The disclosed systems can further determine local densities and separation distances for particular users (or other represented data points) within the hash partitions. Upon determining local densities and separation distances for datapoints from the dataset, the disclosed systems can select a segment (or cluster of data points) grouped according to a hierarchy of a clustering algorithm, such as a density-peaks-clustering algorithm.
-
公开(公告)号：US20230030341A1
公开(公告)日：2023-02-02
申请号：US17383114
申请日：2021-07-22
Applicant: Adobe Inc.
Inventor： Eunyee Koh , Tak Yeon Lee , Andrew Thomson , Vasanthi Holtcamp , Ryan Rossi , Fan Du , Caroline Kim , Tong Yu , Shunan Guo , Nedim Lipka , Shriram Venkatesh Shet Revankar , Nikhil Belsare
IPC: G06N3/08 , H04L12/26 , G06F40/186 , G06N3/04
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a dynamic user interface and machine learning tools to generate data-driven digital content and multivariate testing recommendations for distributing digital content across computer networks. In particular, in one or more embodiments, the disclosed systems utilize machine learning models to generate digital recommendations at multiple development stages of digital communications that are targeted on particular performance metrics. For example, the disclosed systems utilize historical information and recipient profile data to generate recommendations for digital communication templates, fragment variants of content fragments, and content variants of digital content items. Ultimately, the disclosed systems generate multivariate testing recommendations incorporating selected fragment variants to intelligently narrow multivariate testing candidates and generate more meaningful and statistically significant multivariate testing results.
-
公开(公告)号：US20220244815A1
公开(公告)日：2022-08-04
申请号：US17161770
申请日：2021-01-29
Applicant: Adobe Inc.
Inventor： Camille Harris , Zening Qu , Sana Lee , Ryan Rossi , Fan Du , Eunyee Koh , Tak Yeon Lee , Sungchul Kim , Handong Zhao , Sumit Shekhar
IPC: G06F3/0482 , G06F3/0484 , G06F17/15
Abstract: In some embodiments, a data visualization system detects insights from a dataset and computes insight scores for respective insights. The data visualization system further computes insight type scores, from the insight scores, for insight types in the detected insights. The data visualization system determines a selected insight type for the dataset having a higher insight type score than unselected insight types and determines, for the selected insight type, a set of selected insights that have higher insight scores than unselected insights. The data visualization system determines insight visualizations for the set of selected insights and generates, for inclusion in a user interface of the data visualization system, selectable interface elements configured for invoking an editing tool for updating the determined insight visualizations from the dataset. The selectable interface elements are arranged in the user interface according to the insight scores of the set of selected insights.
-
公开(公告)号：US20220237228A1
公开(公告)日：2022-07-28
申请号：US17161406
申请日：2021-01-28
Applicant: Adobe Inc.
Inventor： Shenyu Xu , Eunyee Koh , Fan Du , Tak Yeon Lee , Sana Malik Lee , Ryan Rossi
IPC: G06F16/738 , G06F16/901 , G06F16/9032 , G06F16/34 , G06F16/783
Abstract: This disclosure describes one or more embodiments of systems, non-transitory computer-readable media, and methods that intelligently and automatically analyze input data and generate visual data stories depicting graphical visualizations from data insights determined from the input data. For example, the disclosed systems automatically extract data insights utilizing an in-depth statistical analysis of dataset groups from data-attribute categories within the input data. Based on the data insights, the disclosed systems can automatically generate exportable visual data stories to visualize the data insights, provide textual or audio-based natural language summaries of the data insights, and animate such data insights in videos. In some embodiments, the disclosed systems generate a visual-data-story graph comprising nodes representing visual data stories and edges representing similarities between the visual data stories. Based on the visual-data-story graph, the disclosed systems can select a relevant visual data story to display on a graphical user interface.
-
Patent Agency Ranking
公开(公告)号：US20210326361A1
公开(公告)日：2021-10-21
申请号：US16852110
申请日：2020-04-17
Applicant: Adobe Inc.
Inventor： Fan Du , Yeuk-Yin Chan , Eunyee Koh , Ryan Rossi , Margarita Savova , Charles Menguy , Anup Rao
Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for utilizing hash partitions to determine local densities and distances among users (or among other represented data points) for clustering sparse data into segments. For instance, the disclosed systems can generate hash signatures for users in a sparse dataset and can map users to hash partitions based on the hash signatures. The disclosed systems can further determine local densities and separation distances for particular users (or other represented data points) within the hash partitions. Upon determining local densities and separation distances for datapoints from the dataset, the disclosed systems can select a segment (or cluster of data points) grouped according to a hierarchy of a clustering algorithm, such as a density-peaks-clustering algorithm.
-
公开(公告)号：US11109085B2
公开(公告)日：2021-08-31
申请号：US16367628
申请日：2019-03-28
Applicant: Adobe Inc.
Inventor： Anup Rao , Yasin Abbasi Yadkori , Tung Mai , Ryan Rossi , Ritwik Sinha , Matvey Kapilevich , Alexandru Ionut Hodorogea
IPC: G06F7/00 , G06F16/00 , H04N21/258 , H04N21/482 , H04N21/2668
Abstract: The present disclosure relates to training a recommendation model to generate trait recommendations using one permutation hashing and populated-value-slot-based densification. In particular, the disclosed systems can train the recommendation model by computing sketch vectors corresponding to traits using one permutation hashing. The disclosed systems can then fill in unpopulated value slots of the sketch vectors using populated-value-slot-based densification. The disclosed systems can combine the resulting densified sketches to generate the trained recommendation model. For example, in some embodiments, the disclosed systems can combine the sketches by generating a plurality of locality sensitive hashing tables based on the sketches. In some embodiments, the disclosed systems generate a count sketch matrix based on the sketches and generate trait embeddings based on the count sketch matrix using spectral embedding. Based on the trait embeddings, the disclosed systems can utilize the recommendation model to flexibly and accurately determine the similarity between traits.
-
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：US20240202940A1
公开(公告)日：2024-06-20
申请号：US18084606
申请日：2022-12-20
Applicant: Adobe Inc.
Inventor： Chang Xiao , Ryan Rossi , Enyu Cai
CPC classification number: G06T7/248 , G06T7/215 , G06T7/74 , G06T15/205 , G06T2207/10016 , G06T2207/20081 , G06T2207/20084 , G06T2207/30244 , G06T2210/56
Abstract: Certain aspects and features of this disclosure relate to providing a hybrid approach for camera pose estimation using a deep learning-based image matcher and a match refinement procedure. The image matcher takes an image pair as an input and estimates coarse point-to-point feature matches between the two images. The coarse point-to-point feature matches can be filtered based on a stability threshold to produce high-stability point-to-point matches. A perspective-n-point (PnP) camera pose for each frame of video, including one or more added digital visual elements can be computed using the high-stability matches and video frames can be rendered, each using its computed camera pose.
-
公开(公告)号：US20240119251A1
公开(公告)日：2024-04-11
申请号：US17936099
申请日：2022-09-28
Applicant: Adobe Inc.
Inventor： Ryan Rossi
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing machine-learning to automatically select a machine-learning model for graph learning tasks. The disclosed system extracts, utilizing a graph feature machine-learning model, meta-graph features representing structural characteristics of a graph representation comprising a plurality of nodes and a plurality of edges indicating relationships between the plurality of nodes. The disclosed system also generates, utilizing the graph feature machine-learning model, a plurality of estimated graph learning performance metrics for a plurality of machine-learning models according to the meta-graph features. The disclosed system selects a machine-learning model to process data associated with the graph representation according to the plurality of estimated graph learning performance metrics.
-
-
-
-
-
-
-
-
-
Search Results
49
records
(took 0.021 seconds)
