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公开(公告)号：US11907232B2
公开(公告)日：2024-02-20
申请号：US17146651
申请日：2021-01-12
Applicant: ADOBE INC.
Inventor： Wei Zhang , Christopher Challis
IPC: G06F16/00 , G06F16/2457 , G06F17/18 , H04L67/306 , H04L67/50
CPC classification number: G06F16/24578 , G06F17/18 , H04L67/306 , H04L67/535
Abstract: The present technology provides for facilitating efficient identification of relevant metrics. In one embodiment, a set of candidate metrics for which to determine relevance to a user is identified. For each candidate metric, a set of distribution parameters is determined, including a first distribution parameter based on implicit positive feedback associated with the metric and usage data associated with the metric and a second distribution parameter based on the usage data associated with the metric. Such usage data can efficiently facilitate identifying relevance even with an absence of negative feedback. Using the set of distribution parameters, a corresponding distribution is generated. Each distribution can then be sampled to identify a relevance score for each candidate metric indicating an extent of relevance of the corresponding metric. Based on the relevance scores for each candidate metric, a candidate metric is designated as relevant to the user.
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公开(公告)号：US11775502B2
公开(公告)日：2023-10-03
申请号：US17200522
申请日：2021-03-12
Applicant: ADOBE INC.
Inventor： Wei Zhang , Christopher Challis
IPC: G06F16/23 , G06F11/34 , G06F11/30 , H04L41/16 , G06N20/00 , H04L41/147 , H04L43/08 , H04L9/40 , H04L41/14
CPC classification number: G06F16/2365 , G06F11/3452 , G06N20/00 , H04L41/145 , H04L41/147 , H04L43/08 , H04L63/1425
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：US11467817B2
公开(公告)日：2022-10-11
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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公开(公告)号：US20220237066A1
公开(公告)日：2022-07-28
申请号：US17158643
申请日：2021-01-26
Applicant: Adobe Inc.
Inventor： Wei Zhang , Christopher John Challis
Abstract: A server monitoring methodology uses a time-series model for predicting value of a metric of a server. The model is built using initial training data that includes median values of the metric, each median value based on previously measured values of that metric, from servers of a group to which the server is being added. The methodology includes observing the value of the metric of the server, and comparing that observed value to a predicted value of the model. In response to the observed value being within an expected tolerance, the training data is updated to include the observed value; and in response to the observed value being outside the expected tolerance, the training data is updated to include a value between the observed value of the server metric and the predicted value. The model is updated using the updated training data, and eventually adapts to performance of the server.
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公开(公告)号：US12287797B2
公开(公告)日：2025-04-29
申请号：US18406426
申请日：2024-01-08
Applicant: Adobe Inc.
Inventor： Wei Zhang , Christopher Challis
IPC: G06F16/00 , G06F16/2457 , G06F17/18 , H04L67/306 , H04L67/50
Abstract: The present technology provides for facilitating efficient identification of relevant metrics. In one embodiment, a set of candidate metrics for which to determine relevance to a user is identified. For each candidate metric, a set of distribution parameters is determined, including a first distribution parameter based on implicit positive feedback associated with the metric and usage data associated with the metric and a second distribution parameter based on the usage data associated with the metric. Such usage data can efficiently facilitate identifying relevance even with an absence of negative feedback. Using the set of distribution parameters, a corresponding distribution is generated. Each distribution can then be sampled to identify a relevance score for each candidate metric indicating an extent of relevance of the corresponding metric. Based on the relevance scores for each candidate metric, a candidate metric is designated as relevant to the user.
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Patent Agency Ranking
公开(公告)号：US20250077549A1
公开(公告)日：2025-03-06
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: G06F16/28 , G06F16/242 , 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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公开(公告)号：US12177088B2
公开(公告)日：2024-12-24
申请号：US17938742
申请日：2022-10-07
Applicant: Adobe Inc.
Inventor： Wei Zhang , Ilya Borisovich Reznik
IPC: H04L41/149 , H04L41/14 , H04L41/147
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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公开(公告)号：US12061874B2
公开(公告)日：2024-08-13
申请号：US17929267
申请日：2022-09-01
Applicant: Adobe Inc.
Inventor： Wei Zhang , Christopher Challis
IPC: G06F40/284 , G06F8/65 , G06F8/70 , G06F18/10 , G06F18/214 , G06F18/24 , G06F40/44 , G06N20/00
CPC classification number: G06F40/284 , G06F8/65 , G06F8/70 , G06F18/10 , G06F18/214 , G06F18/24 , G06F40/44 , G06N20/00
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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公开(公告)号：US11948094B2
公开(公告)日：2024-04-02
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：US10346861B2
公开(公告)日：2019-07-09
申请号：US14933254
申请日：2015-11-05
Applicant: ADOBE INC.
Inventor： Wei Zhang , Said Kobeissi , Anandhavelu Natarajan , Shiv Kumar Saini , Ritwik Sinha , Scott Allen Tomko
Abstract: Embodiments of the present invention relate to providing business customers with predictive capabilities, such as identifying valuable customers or estimating the likelihood that a product will be purchased. An adaptive sampling scheme is utilized, which helps generate sample data points from large scale data that is imbalanced (for example, digital website traffic with hundreds of millions of visitors but only a small portion of them are of interest). In embodiments, a stream of sample data points is received. Positive samples are added to a positive list until the desired number of positives is reached and negative samples are added to a negative list until the desired number of negative samples is reached. The positive list and the negative list can then be combined, shuffled, and fed into a prediction model.
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