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公开(公告)号：US11768869B2
公开(公告)日：2023-09-26
申请号：US17170520
申请日：2021-02-08
Applicant: ADOBE INC.
Inventor： Nedim Lipka , Seyedsaed Rezayidemne , Vishwa Vinay , Ryan Rossi , Franck Dernoncourt , Tracy Holloway King
CPC classification number: G06F16/532 , G06F16/55 , G06F16/56 , G06F40/20 , G06N5/02
Abstract: The present disclosure describes systems and methods for information retrieval. Embodiments of the disclosure provide a retrieval network that leverages external knowledge to provide reformulated search query suggestions, enabling more efficient network searching and information retrieval. For example, a search query from a user (e.g., a query mention of a knowledge graph entity that is included in a search query from a user) may be added to a knowledge graph as a surrogate entity via entity linking. Embedding techniques are then invoked on the updated knowledge graph (e.g., the knowledge graph that includes additional edges between surrogate entities and other entities of the original knowledge graph), and entities neighboring the surrogate entity are retrieved based on the embedding (e.g., based on a computed distance between the surrogate entity and candidate entities in the embedding space). Search results can then be ranked and displayed based on relevance to the neighboring entity.
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32.发明公开公开(公告)号：US20230297625A1
公开(公告)日：2023-09-21
申请号：US17654933
申请日：2022-03-15
Applicant: Adobe Inc.
Inventor： Fayokemi Ojo , Ryan Rossi , Jane Hoffswell , Shunan Guo , Fan Du , Sungchul Kim , Chang Xiao , Eunyee Koh
IPC: G06F16/904 , G06N3/02
CPC classification number: G06F16/904 , G06N3/02
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a graph neural network to generate data recommendations. The disclosed systems generate a digital graph representation comprising user nodes corresponding to users, data attribute nodes corresponding to data attributes, and edges reflecting historical interactions between the users and the data attributes; Moreover, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. In addition, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. Furthermore, the disclosed systems determine a data recommendation for a target user utilizing the data attribute embeddings and a target user embedding corresponding to the target user from the user embeddings.
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公开(公告)号：US11483408B2
公开(公告)日：2022-10-25
申请号：US16507204
申请日：2019-07-10
Applicant: Adobe Inc.
Inventor： Ryan Rossi
IPC: H04L67/50 , H04L43/045 , H04L41/142 , H04L43/067 , G06F16/2458 , G06F16/901 , H04L41/14 , H04L67/306 , H04L41/082 , G06F16/28
Abstract: In some embodiments, a network analysis system receives network data in the form of a temporal graph that includes nodes and edges. Each node represents an entity involved in a network. An edge connects two nodes to indicate an association between the two nodes. Each edge also has a temporal value indicating a time point when the association between the two nodes was created. The network analysis system generates a sequence of nodes by traversing the nodes in the temporal graph along edges with non-decreasing temporal values or with non-increasing temporal values. The network analysis system further replaces the identifiers of the nodes in the sequence to generate a sequence of feature values. Based on the sequence of feature values, the network analysis system determines network embeddings for the nodes in the temporal graph. Using the network embeddings, the network analysis system identifies two or more of the nodes in the temporal graph that belong to the same entity.
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公开(公告)号：US20220309334A1
公开(公告)日：2022-09-29
申请号：US17210157
申请日：2021-03-23
Applicant: Adobe Inc.
Inventor： Ryan Rossi , Tung Mai , Nedim Lipka , Jiong Zhu , Anup Rao , Viswanathan Swaminathan
IPC: G06N3/08 , G06F16/901 , G06N5/02 , G06K9/62
Abstract: Techniques are provided for training graph neural networks with heterophily datasets and generating predictions for such datasets with heterophily. A computing device receives a dataset including a graph data structure and processes the dataset using a graph neural network. The graph neural network defines prior belief vectors respectively corresponding to nodes of the graph data structure, executes a compatibility-guided propagation from the set of prior belief vectors and using a compatibility matrix. The graph neural network predicts predicting a class label for a node of the graph data structure based on the compatibility-guided propagations and a characteristic of at least one node within a neighborhood of the node. The computing device outputs the graph data structure where it is usable by a software tool for modifying an operation of a computing environment.
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公开(公告)号：US20220300836A1
公开(公告)日：2022-09-22
申请号：US17207959
申请日：2021-03-22
Applicant: Adobe Inc.
Inventor： Ryan Rossi , Xin Qian , Tak Yeon Lee , Sungchul Kim , Sana Lee , Fan Du , Eunyee Koh
Abstract: A visualization recommendation system generates recommendation scores for multiple visualizations that combine data attributes of a dataset with visualization configurations. The visualization recommendation system maps meta-features of the dataset to a meta-feature space and configuration attributes of the visualization configurations to a configuration space. The visualization recommendation system generates meta-feature vectors that describe the mapped meta-features, and generates configuration attribute sets that describe the attributes of the visualization configurations. The visualization recommendation system applies multiple scoring models to the meta-feature vectors and configuration attribute sets, including a wide scoring model and a deep scoring model. In some cases, the visualization recommendation system trains the multiple scoring models using the meta-feature vectors and configuration attribute sets.
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Patent Agency Ranking
公开(公告)号：US20220229721A1
公开(公告)日：2022-07-21
申请号：US17150890
申请日：2021-01-15
Applicant: Adobe Inc.
Inventor： Ryan Rossi
Abstract: Embodiments described herein involve selecting outlier-detection programs that are specific to meta-features of datasets. For instance, a computing system constructs a performance vector from a U vector and a reference V matrix. Vector elements of the performance vector identify estimated performance values of various outlier-detection programs with respect to an input dataset. The U vector is generated using meta-features of the input dataset. The reference V matrix is generated from a training process in which performance values of the various outlier-detection programs with respect to training input datasets are used to obtain the reference V matrix via a UV decomposition. The computing system selects an outlier-detection program having a greater estimated performance value in the performance vector as compared to other outlier-detection programs' respective estimated performance values.
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公开(公告)号：US11328002B2
公开(公告)日：2022-05-10
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
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.
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公开(公告)号：US12182493B2
公开(公告)日：2024-12-31
申请号：US18484674
申请日：2023-10-11
Applicant: Adobe Inc.
Inventor： Md Main Uddin Rony , Fan Du , Iftikhar Ahamath Burhanuddin , Ryan Rossi , Niyati Himanshu Chhaya , Eunyee Koh
IPC: G06F17/00 , G06F40/106 , G06F40/40
Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating generation and presentation of insights. In one implementation, a set of data is used to generate a data visualization. A candidate insight associated with the data visualization is generated, the candidate insight being generated in text form based on a text template and comprising a descriptive insight, a predictive insight, an investigative, or a prescriptive insight. A set of natural language insights is generated, via a machine learning model. The natural language insights represent the candidate insight in a text style that is different from the text template. A natural language insight having the text style corresponding with a desired text style is selected for presenting the candidate insight and, thereafter, the selected natural language insight and data visualization are providing for display via a graphical user interface.
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公开(公告)号：US12175366B2
公开(公告)日：2024-12-24
申请号：US17210157
申请日：2021-03-23
Applicant: Adobe Inc.
Inventor： Ryan Rossi , Tung Mai , Nedim Lipka , Jiong Zhu , Anup Rao , Viswanathan Swaminathan
IPC: G06N3/08 , G06F16/901 , G06F18/2132 , G06F18/2413 , G06N5/02
Abstract: Techniques are provided for training graph neural networks with heterophily datasets and generating predictions for such datasets with heterophily. A computing device receives a dataset including a graph data structure and processes the dataset using a graph neural network. The graph neural network defines prior belief vectors respectively corresponding to nodes of the graph data structure, executes a compatibility-guided propagation from the set of prior belief vectors and using a compatibility matrix. The graph neural network predicts predicting a class label for a node of the graph data structure based on the compatibility-guided propagations and a characteristic of at least one node within a neighborhood of the node. The computing device outputs the graph data structure where it is usable by a software tool for modifying an operation of a computing environment.
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40.发明公开公开(公告)号：US20240311623A1
公开(公告)日：2024-09-19
申请号：US18183387
申请日：2023-03-14
Applicant: Adobe Inc.
Inventor： Ryan Rossi , Eunyee Koh , Jane Hoffswell , Nedim Lipka , Shunan Guo , Sudhanshu Chanpuriya , Sungchul Kim , Tong Yu
IPC: G06N3/049
CPC classification number: G06N3/049
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for building time-decayed line graphs from temporal graph networks for efficiently and accurately generating time-aware recommendations. For example, the time-decayed line graph system creates a line graph of the temporal graph network by deriving interaction nodes from temporal edges (e.g., timed interactions) and connecting interactions that share an endpoint node. Then, the time-decayed line graph system determines the edge weights in the line graph based on differences in time between interactions, with interactions that occur closer together in time being connected with higher weights. Notably, by using this method, the derived time-decayed line graph directly represents topological proximity and temporal proximity. Upon generating the time-decayed line graphs, the system performs downstream predictive modeling such as predicted edge classifications and/or temporal link predictions.
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