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公开(公告)号：US12265557B2
公开(公告)日：2025-04-01
申请号：US18459081
申请日：2023-08-31
Applicant: Adobe Inc.
Inventor： William Brandon George , Wei Zhang , Tyler Rasmussen , Tung Mai , Tong Yu , Sungchul Kim , Shunan Guo , Samuel Nephi Grigg , Said Kobeissi , Ryan Rossi , Ritwik Sinha , Eunyee Koh , Prithvi Bhutani , Jordan Henson Walker , Abhisek Trivedi
IPC: G06F40/00 , G06F16/242 , G06F16/28 , G06F40/205 , G06F40/40
Abstract: Graphic visualizations, such as charts or graphs conveying data attribute values, can be generated based on natural language queries, i.e., natural language requests. To do so, a natural language request is parsed into n-grams, and from the n-grams, word embeddings are determined using a natural language model. Data attributes for the graphic visualization are discovered in the vector space from the word embeddings. The type of graphic visualization can be determined based on a request intent, which is determined using a trained intent classifier. The graphic visualization is generated to include the data attribute values of the discovered data attributes, and in accordance with the graphic visualization type.
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12.发明公开公开(公告)号：US20240320421A1
公开(公告)日：2024-09-26
申请号：US18338033
申请日：2023-06-20
Applicant: Adobe Inc.
Inventor： Victor Soares Bursztyn , Wei Zhang , Prithvi Bhutani , Eunyee Koh , Abhisek Trivedi
IPC: G06F40/186
CPC classification number: G06F40/186
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating naturally phrased insights about data charts using light language models distilled from large language models. To synthesize training data for the light language model, in some embodiments, the disclosed systems leverage insight templates for prompting a large language model for generating naturally phrased insights. In some embodiments, the disclosed systems anonymize and augment the synthesized training data to improve the accuracy and robustness of model predictions. For example, the disclosed systems anonymize training data by injecting noise into data charts before prompting the large language model for generating naturally phrased insights from insight templates. In some embodiments, the disclosed systems further augment the (anonymized) training data by splitting or partitioning data charts into folds that act as individual data charts.
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13.发明授权公开(公告)号：US11798018B2
公开(公告)日：2023-10-24
申请号：US15062937
申请日：2016-03-07
Applicant: ADOBE INC.
Inventor： Wei Zhang , Shiladitya Bose , Said Kobeissi , Scott Allen Tomko , Jeremy W King
IPC: G06N20/00 , G06Q30/0204 , G06Q10/0639 , G06Q50/00 , G06F16/9535 , G06F16/35 , G06N3/08 , G06N3/044 , G06N3/045
CPC classification number: G06Q30/0205 , G06F16/35 , G06F16/9535 , G06Q10/06395 , G06Q50/01 , G06N3/044 , G06N3/045 , G06N3/08
Abstract: Systems and methods provide for feature selection that combines semantic classification and generative filtering with forward selection. Features from an original feature set are divided into feature subsets corresponding to ranked semantic classes. Additionally, low quality features are removed from consideration. Features are selected for a reduced feature set by iteratively processing the feature subsets using forward selection in an order corresponding to the ranking of the semantic classes. The reduced feature set is used to generate a predictive model.
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公开(公告)号：US20240169258A1
公开(公告)日：2024-05-23
申请号：US18057883
申请日：2022-11-22
Applicant: Adobe Inc.
Inventor： Wei Zhang , David Thomas Arbour
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: In implementations of systems for time-series anomaly detection, a computing device implements an anomaly system to receive, via a network, time-series data describing continuously observed values separated by a period of time. The anomaly system computes updated estimated parameters of a predictive model for the time-series data by performing a rank one update on previously estimated parameters of the predictive model. An uncertainty interval for a future observed value is generated using the predictive model with the updated estimated parameters. The anomaly system determines that an observed value corresponding to the future observed value is outside of the uncertainty interval. An indication is generated that the observed value is an anomaly.
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公开(公告)号：US20240119831A1
公开(公告)日：2024-04-11
申请号：US17938742
申请日：2022-10-07
Applicant: Adobe Inc.
Inventor： Wei Zhang , Ilya Borisovich Reznik
IPC: G08G1/01
CPC classification number: G08G1/0145 , G08G1/0133 , G08G1/0141
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that determine internet traffic data loss from internet traffic data including bulk ingested data utilizing an internet traffic forecasting model. In particular, the disclosed systems detect that observed internet traffic data includes bulk ingested internet traffic data. In addition, the disclosed systems determine a predicted traffic volume for an outage period from the bulk ingested internet traffic data utilizing an internet traffic forecasting model. The disclosed systems further generate a decomposed predicted traffic volume for the outage period. The disclosed systems also determine an internet traffic data loss for the outage period from the decomposed predicted traffic volume while calibrating for pattern changes and late data from previous periods.
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Patent Agency Ranking
公开(公告)号：US10992972B1
公开(公告)日：2021-04-27
申请号：US16731406
申请日：2019-12-31
Applicant: ADOBE INC.
Inventor： Wei Zhang , Chris Challis
IPC: H04N21/61 , H04N21/239 , H04W4/02 , H04W4/029 , H04W64/00 , H04L29/06 , H04N21/258 , H04L29/08
Abstract: The present disclosure relates to a method for detecting impermissible account sharing among user accounts of a streaming media service including the steps of determining a plurality of locations accessed by a given user account of the user accounts; determining a device access count for each of the locations, the device access count indicating how many times the corresponding location was accessed by at least one device associated with the given user account; identifying one of the locations having the highest device access count as a base location; calculating a risk coefficient for each remaining location; generating a sharing score for the given user account by summing the risk coefficients; and determining impermissible account sharing of the given user account has occurred when the sharing score exceeds a threshold.
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公开(公告)号：US20210117816A1
公开(公告)日：2021-04-22
申请号：US17136727
申请日：2020-12-29
Applicant: Adobe Inc.
Inventor： Wei Zhang , Scott Tomko
Abstract: The present disclosure includes methods and systems for generating digital predictive models by progressively sampling a repository of data samples. In particular, one or more embodiments of the disclosed systems and methods identify initial attributes for predicting a target attribute and utilize the initial attributes to identify a coarse sample set. Moreover, the disclosed systems and methods can utilize the coarse sample set to identify focused attributes pertinent to predicting the target attribute. Utilizing the focused attributes, the disclosed systems and methods can identify refined data samples and utilize the refined data samples to identify final attributes and generate a digital predictive model.
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公开(公告)号：US20220292074A1
公开(公告)日：2022-09-15
申请号：US17200522
申请日：2021-03-12
Applicant: ADOBE INC.
Inventor： Wei Zhang , Christopher Challis
Abstract: Embodiments of the present technology provide systems, methods, and computer storage media for facilitating anomaly detection. In some embodiments, a prediction model is generated using a training data set. The prediction model is used to predict an expected value for a latest (current) timestamp, which is used to determine that the incoming observed data value is an anomaly. Based on the incoming observed data value determined to be the anomaly or not, a corrected data value is generated to be included in the training data set. Thereafter, the training data set having the corrected data value is used to update the prediction model for use in determining whether a subsequent observed data value is anomalous. Such a process may be performed in an iterative manner to maintain optimized training data and prediction model.
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公开(公告)号：US10885441B2
公开(公告)日：2021-01-05
申请号：US15388922
申请日：2016-12-22
Applicant: Adobe Inc.
Inventor： Wei Zhang , Scott Tomko
Abstract: The present disclosure includes methods and systems for generating digital predictive models by progressively sampling a repository of data samples. In particular, one or more embodiments of the disclosed systems and methods identify initial attributes for predicting a target attribute and utilize the initial attributes to identify a coarse sample set. Moreover, the disclosed systems and methods can utilize the coarse sample set to identify focused attributes pertinent to predicting the target attribute. Utilizing the focused attributes, the disclosed systems and methods can identify refined data samples and utilize the refined data samples to identify final attributes and generate a digital predictive model.
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20.发明申请公开(公告)号：US20200241861A1
公开(公告)日：2020-07-30
申请号：US16259454
申请日：2019-01-28
Applicant: Adobe Inc.
Inventor： Wei Zhang , Christopher Challis
Abstract: Systems and methods for facilitating updates to software programs via machine-learning techniques are disclosed. In an example, an application generates a feature vector from a textual description of a software defect by applying a topic model to the textual description. The application uses the feature vector and one or more machine-learning models configured to predict classifications and sub-classifications of the textual description. The application integrates the classifications and the sub-classifications into a final classification of the textual description that indicates a software component responsible for causing the software defect. The final classification is usable for correcting the software defect.
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