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公开(公告)号：US20240143607A1
公开(公告)日：2024-05-02
申请号：US18406426
申请日：2024-01-08
Applicant: Adobe Inc.
Inventor： Wei ZHANG , Christopher Challis
IPC: 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：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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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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Patent Agency Ranking
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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5.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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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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