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公开(公告)号：US20250005289A1
公开(公告)日：2025-01-02
申请号：US18343389
申请日：2023-06-28
Applicant: Adobe Inc.
Inventor： Haoliang Wang , Kaige Xie , Tong Yu , Junda Wu , Handong Zhao , Ruiyi Zhang , Kanak Vivek Mahadik , Ani Nenkova
Abstract: Dialogue state aware dialogue summarization techniques are described that enable generation of dialogue summaries from target domains with limited training data. A content processing system, for instance, generates one or more clusters based on training dialogues from one or more source domains. The clusters represent domain-specific features of the training dialogues and are further based on dialogue states of the training dialogues. The content processing system trains a machine learning model to generate summaries of dialogues by using the one or more clusters as prefixes in a prefix-tuning approach. The content processing system receives an input that includes a dialogue from a target domain. The content processing system generates an input prompt based on the dialogue and the one or more clusters, and the model generates a summary of the dialogue based on the input prompt.
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公开(公告)号：US20210357255A1
公开(公告)日：2021-11-18
申请号：US16867104
申请日：2020-05-05
Applicant: ADOBE INC.
Inventor： Kanak Vivek Mahadik , Ryan A. Rossi , Sana Malik Lee , Georgios Theocharous , Handong Zhao , Gang Wu , Youngsuk Park
Abstract: A system and method for automatically adjusting computing resources provisioned for a computer service or application by applying historical resource usage data to a predictive model to generate predictive resource usage. The predictive resource usage is then simulated for various service configurations, determining scaling requirements and resource wastage for each configuration. A cost value is generated based on the scaling requirement and resource wastage, with the cost value for each service configuration used to automatically select a configuration to apply to the service. Alternatively, the method for automatically adjusting computer resources provisioned for a service may include receiving resource usage data of the service, applying it to a linear quadratic regulator (LQR) to find an optimal stationary policy (treating the resource usage data as states and resource-provisioning variables as actions), and providing instructions for configuring the service based on the optimal stationary policy.
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公开(公告)号：US20210334664A1
公开(公告)日：2021-10-28
申请号：US16865605
申请日：2020-05-04
Applicant: Adobe Inc.
Inventor： Kai Li , Christopher Alan Tensmeyer , Curtis Michael Wigington , Handong Zhao , Nikolaos Barmpalios , Tong Sun , Varun Manjunatha , Vlad Ion Morariu
Abstract: Adapting a machine learning model to process data that differs from training data used to configure the model for a specified objective is described. A domain adaptation system trains the model to process new domain data that differs from a training data domain by using the model to generate a feature representation for the new domain data, which describes different content types included in the new domain data. The domain adaptation system then generates a probability distribution for each discrete region of the new domain data, which describes a likelihood of the region including different content described by the feature representation. The probability distribution is compared to ground truth information for the new domain data to determine a loss function, which is used to refine model parameters. After determining that model outputs achieve a threshold similarity to the ground truth information, the model is output as a domain-agnostic model.
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公开(公告)号：US20210232908A1
公开(公告)日：2021-07-29
申请号：US16751755
申请日：2020-01-24
Applicant: Adobe Inc.
Inventor： Yikun Xian , Tak Yeon Lee , Sungchul Kim , Ryan Rossi , Handong Zhao
IPC: G06N3/08
Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for dynamically determining schema labels for columns regardless of information availability within the columns. For example, the disclosed systems can identify a column that contains an arbitrary amount of information (e.g., a header-only column, a cell-only column, or a whole column). Additionally, the disclosed systems can generate a vector embedding for an arbitrary input column by selectively using a header neural network and/or a cell neural network based on whether the column includes a header label and/or whether the column includes a populated column cell. Furthermore, the disclosed systems can compare the column vector embedding to schema vector embeddings of candidate schema labels in a d-dimensional space to determine a schema label for the column.
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公开(公告)号：US20250013866A1
公开(公告)日：2025-01-09
申请号：US18347877
申请日：2023-07-06
Applicant: ADOBE INC.
Inventor： Handong Zhao , Yue Bai , Zhe Lin , Ajinkya Gorakhnath Kale , Jiuxiang Gu , Tong Yu , Sungchul Kim
Abstract: Systems and methods for reducing inference time of vision-language models, as well as for multimodal search, are described herein. Embodiments are configured to obtain an embedding neural network. The embedding neural network is pretrained to embed inputs from a plurality of modalities into a multimodal embedding space. Embodiments are further configured to perform a first progressive pruning stage, where the first progressive pruning stage includes a first pruning of the embedding neural network and a first fine-tuning of the embedding neural network. Embodiments then perform a second progressive pruning stage based on an output of the first progressive pruning stage, where the second progressive pruning stage includes a second pruning of the embedding neural network and a second fine-tuning of the embedding neural network.
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Patent Agency Ranking
公开(公告)号：US12182086B2
公开(公告)日：2024-12-31
申请号：US17347133
申请日：2021-06-14
Applicant: Adobe Inc.
Inventor： Ritwik Sinha , Saayan Mitra , Handong Zhao , Somdeb Sarkhel , Trevor Paulsen , William Brandon George
IPC: G06F16/215 , G06F16/242 , G06N5/04 , G06N20/00
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating automatic suggestions to effectively modify the organization of an ingested data collection without destruction of the underlying raw data. In particular, in one or more embodiments, the disclosed systems utilize multiple machine learning models in sequence to determine likelihoods that the organizational structure of an ingested data collection should be modified in various ways. In response to generating these likelihoods, the disclosed systems generate corresponding automatic suggestions to modify the organization of the ingested data collection. In response to a detected selection of one or more of the automatic suggestions, the disclosed systems read data out of the ingested data collection in accordance with the selected automatic suggestions to effectively modify the organization of the ingested data collection.
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公开(公告)号：US20240311221A1
公开(公告)日：2024-09-19
申请号：US18120773
申请日：2023-03-13
Applicant: Adobe Inc.
Inventor： Jaeho Bang , Sungchul Kim , Ryan A. Rossi , Tong Yu , Handong Zhao
CPC classification number: G06F11/0769 , G06F11/0778 , G06N20/00
Abstract: In implementations of systems for detection and interpretation of log anomalies, a computing device implements an anomaly system to receive input data describing a two-dimensional representation of log templates and timestamps. The anomaly system processes the input data using a machine learning model trained on training data to detect anomalies in two-dimensional representations of log templates and timestamps. A log anomaly is detected in the two-dimensional representation using the machine learning model based on processing the input data. The anomaly system generates an indication of an interpretation of the log anomaly for display in a user interface based on a log template included in the two-dimensional representation.
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公开(公告)号：US11978272B2
公开(公告)日：2024-05-07
申请号：US17883811
申请日：2022-08-09
Applicant: Adobe Inc.
Inventor： Kai Li , Christopher Alan Tensmeyer , Curtis Michael Wigington , Handong Zhao , Nikolaos Barmpalios , Tong Sun , Varun Manjunatha , Vlad Ion Morariu
IPC: G06V30/413 , G06F17/18 , G06F18/213 , G06F18/2415 , G06N3/047 , G06N3/084 , G06N20/00 , G06N20/10 , G06V10/25 , G06V10/82 , G06V20/20 , G06V30/19 , G06V30/414
CPC classification number: G06V30/413 , G06F17/18 , G06F18/213 , G06F18/2415 , G06N3/047 , G06N3/084 , G06N20/00 , G06N20/10 , G06V10/25 , G06V10/82 , G06V20/20 , G06V30/19173 , G06V30/414
Abstract: Adapting a machine learning model to process data that differs from training data used to configure the model for a specified objective is described. A domain adaptation system trains the model to process new domain data that differs from a training data domain by using the model to generate a feature representation for the new domain data, which describes different content types included in the new domain data. The domain adaptation system then generates a probability distribution for each discrete region of the new domain data, which describes a likelihood of the region including different content described by the feature representation. The probability distribution is compared to ground truth information for the new domain data to determine a loss function, which is used to refine model parameters. After determining that model outputs achieve a threshold similarity to the ground truth information, the model is output as a domain-agnostic model.
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公开(公告)号：US20230094415A1
公开(公告)日：2023-03-30
申请号：US17487889
申请日：2021-09-28
Applicant: Adobe Inc.
Inventor： Handong Zhao
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that generate a target classifier for a target domain via domain adaptation using a source classifier learned on a source domain. For instance, in one or more embodiments, the disclosed systems utilize an embedding model, a target classifier, and a source classifier to analyze sets of target samples and generate classification probabilities for the target samples based on the analysis. In some cases, the disclosed systems utilize the classification probabilities to modify the parameters of the target classifier via adaptive adversarial inference. In some implementations, the disclosed systems further utilize the classification probabilities to modify the parameters of the embedding model via contrastive category-wise matching. Thus, in some cases, the disclosed systems utilize the target classifier with the modified parameters to generate classifications for digital data from the target domain.
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公开(公告)号：US11487579B2
公开(公告)日：2022-11-01
申请号：US16867104
申请日：2020-05-05
Applicant: ADOBE INC.
Inventor： Kanak Vivek Mahadik , Ryan A. Rossi , Sana Malik Lee , Georgios Theocharous , Handong Zhao , Gang Wu , Youngsuk Park
Abstract: A system and method for automatically adjusting computing resources provisioned for a computer service or application by applying historical resource usage data to a predictive model to generate predictive resource usage. The predictive resource usage is then simulated for various service configurations, determining scaling requirements and resource wastage for each configuration. A cost value is generated based on the scaling requirement and resource wastage, with the cost value for each service configuration used to automatically select a configuration to apply to the service. Alternatively, the method for automatically adjusting computer resources provisioned for a service may include receiving resource usage data of the service, applying it to a linear quadratic regulator (LQR) to find an optimal stationary policy (treating the resource usage data as states and resource-provisioning variables as actions), and providing instructions for configuring the service based on the optimal stationary policy.
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