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公开(公告)号：US11995547B2
公开(公告)日：2024-05-28
申请号：US17823390
申请日：2022-08-30
Applicant: Adobe Inc.
Inventor： Fan Du , Sungchul Kim , Shunan Guo , Sana Lee , Eunyee Koh
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.
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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.
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公开(公告)号：US11782576B2
公开(公告)日：2023-10-10
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 , G06F17/15 , G06F3/04845
CPC classification number: G06F3/0482 , G06F3/04845 , 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.
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4.发明申请公开(公告)号：US20220076048A1
公开(公告)日：2022-03-10
申请号：US17015495
申请日：2020-09-09
Applicant: Adobe Inc.
Inventor： Ryan Rossi , Vasanthi Holtcamp , Tak Yeon Lee , Sana Lee , Nathan Ross , John Anderson , Fan Du , Eunyee Koh
IPC: G06K9/62 , G06F3/0482 , G06F17/18
Abstract: This disclosure involves generating, from a user data set, a ranked list of recommended secondary variables in a user interface field similar to primary variable selected in another user interface field. A system receives a data set having variables and corresponding sets of values. The data visualization system determines a feature vector for each variable based on statistics of a corresponding values set. The system generates a variable similarity graph having nodes representing variables and links representing degrees of similarity between feature vectors of variables. The system receives a selection of a first variable via a first field of the user interface, detects a selection of a second field, and identifies a relationship between the first field and the second field. The system generates a contextual menu of recommended secondary variables for use with the selected first variable based on similarity value of the links in the variable similarity graph.
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公开(公告)号：US11593648B2
公开(公告)日：2023-02-28
申请号：US16844006
申请日：2020-04-09
Applicant: Adobe Inc.
Inventor： Sana Lee , Po Ming Law , Moumita Sinha , Fan Du
Abstract: This disclosure involves detecting biases in predictive models and the root cause of those biases. For example, a processing device receives test data and training data from a client device. The processing device identifies feature groups from the training data and the test data generates performance metrics and baseline metrics for a feature group. The processing device detects biases through a comparison of the performance metrics and the baseline metrics the feature group. The processing device then isolates a portion of the training data that corresponds to the detected bias. The processing device generates a model correction usable to remove the bias from the predictive model.
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Patent Agency Ranking
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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公开(公告)号：US20220147540A1
公开(公告)日：2022-05-12
申请号：US17091941
申请日：2020-11-06
Applicant: ADOBE INC.
Inventor： RYAN Rossi , Vasanthi Holtcamp , Tak Yeon Lee , Sungchul Kim , Sana Lee , Nathan Ross , John Anderson , Fan Du , Eunyee Koh , Xin Qian
IPC: G06F16/26 , G06F11/30 , G06F11/34 , G06F3/0482
Abstract: Systems and methods for personalized visualization recommendation are described. Embodiments of the described systems and methods are configured to identify a first matrix representing user interactions with a plurality of data attributes corresponding to a plurality of datasets, a second matrix representing user interactions with a plurality of visualizations, and a third matrix representing a plurality of meta-features for each of the data attributes; compute low-dimensional embeddings representing user characteristics, the data attributes, visualization configurations, and the meta-features using joint factorization of the first matrix, the second matrix and the third matrix; generate a model for predicting visualization preference weights based on the low-dimensional embeddings; predict the visualization preference weights for a user corresponding to a plurality of candidate visualizations of dataset using the model; and generate a personalized visualization of the dataset for the user based on the predicted visualization preference weights.
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公开(公告)号：US11720590B2
公开(公告)日：2023-08-08
申请号：US17091941
申请日：2020-11-06
Applicant: ADOBE INC.
Inventor： Ryan Rossi , Vasanthi Holtcamp , Tak Yeon Lee , Sungchul Kim , Sana Lee , Nathan Ross , John Anderson , Fan Du , Eunyee Koh , Xin Qian
IPC: G06F16/9535 , G06F16/26 , G06F3/0482 , G06F11/34 , G06F11/30 , G06F16/9038
CPC classification number: G06F16/26 , G06F3/0482 , G06F11/302 , G06F11/3438 , G06F16/9038
Abstract: Systems and methods for personalized visualization recommendation are described. Embodiments of the described systems and methods are configured to identify a first matrix representing user interactions with a plurality of data attributes corresponding to a plurality of datasets, a second matrix representing user interactions with a plurality of visualizations, and a third matrix representing a plurality of meta-features for each of the data attributes; compute low-dimensional embeddings representing user characteristics, the data attributes, visualization configurations, and the meta-features using joint factorization of the first matrix, the second matrix and the third matrix; generate a model for predicting visualization preference weights based on the low-dimensional embeddings; predict the visualization preference weights for a user corresponding to a plurality of candidate visualizations of dataset using the model; and generate a personalized visualization of the dataset for the user based on the predicted visualization preference weights.
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9.发明授权公开(公告)号：US11288541B1
公开(公告)日：2022-03-29
申请号：US17015495
申请日：2020-09-09
Applicant: Adobe Inc.
Inventor： Ryan Rossi , Vasanthi Holtcamp , Tak Yeon Lee , Sana Lee , Nathan Ross , John Anderson , Fan Du , Eunyee Koh
IPC: G06K9/62 , G06F17/18 , G06F3/0482
Abstract: This disclosure involves generating, from a user data set, a ranked list of recommended secondary variables in a user interface field similar to primary variable selected in another user interface field. A system receives a data set having variables and corresponding sets of values. The data visualization system determines a feature vector for each variable based on statistics of a corresponding values set. The system generates a variable similarity graph having nodes representing variables and links representing degrees of similarity between feature vectors of variables. The system receives a selection of a first variable via a first field of the user interface, detects a selection of a second field, and identifies a relationship between the first field and the second field. The system generates a contextual menu of recommended secondary variables for use with the selected first variable based on similarity value of the links in the variable similarity graph.
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公开(公告)号：US20210319333A1
公开(公告)日：2021-10-14
申请号：US16844006
申请日：2020-04-09
Applicant: Adobe Inc.
Inventor： Sana Lee , Po Ming Law , Moumita Sinha , Fan Du
Abstract: This disclosure involves detecting biases in predictive models and the root cause of those biases. For example, a processing device receives test data and training data from a client device. The processing device identifies feature groups from the training data and the test data generates performance metrics and baseline metrics for a feature group. The processing device detects biases through a comparison of the performance metrics and the baseline metrics the feature group. The processing device then isolates a portion of the training data that corresponds to the detected bias. The processing device generates a model correction usable to remove the bias from the predictive model.
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