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    Transferable clustering of contextual bandits for cloud service resource allocation
    2.
    发明授权
    Transferable clustering of contextual bandits for cloud service resource allocation 有权

    公开(公告)号：US12294529B2

    公开(公告)日：2025-05-06

    申请号：US18342516

    申请日：2023-06-27

    Applicant: ADOBE INC.

    Inventor： Kanak Mahadik ,  Tong Yu ,  Junda Wu

    IPC: H04L41/16 ,  H04L47/78

    Abstract: Methods for determining optimal cloud service resource include determining a reward function for a set of resource configurations identifying cloud service resource parameters. The cloud service resource parameters include a source parameter and a target parameter of services to provide a client computing device. A source parameter dataset for the source parameter and a target parameter dataset is generated using the reward function and historical source parameter data. The matrices are then subject to SVD and clustering. A target parameter reward dataset is learned from output of the SVD and clustering. The target parameter dataset is used to determine the parameters for the target parameter for providing corresponding cloud service resources.

    CONTEXTUAL QUERY GENERATION
    4.
    发明申请
    CONTEXTUAL QUERY GENERATION 有权

    公开(公告)号：US20240427998A1

    公开(公告)日：2024-12-26

    申请号：US18339694

    申请日：2023-06-22

    Applicant: Adobe Inc.

    Inventor： Haoliang Wang ,  Tong Yu ,  Sungchul Kim ,  Ruiyi Zhang ,  Paiheng Xu ,  Junda Wu ,  Handong Zhao ,  Ani Nenkova

    IPC: G06F40/30 ,  G06N5/04

    Abstract: Contextual query generation techniques are described that enable generation of a contextual query for output to a question-answering (QA) model. A content processing system, for instance, configures a language model using in-context learning to generate queries based on semantic contexts of input documents, e.g., based on one or more linguistic cues from text of the input documents. The content processing system receives an input that includes a document having text and a reference query. The content processing system leverages the language model to generate a contextual query based on a semantic context of the text of the document and the reference query. The content processing system then outputs the contextual query and the document to a QA model. Using the QA model, the content processing system generates a response as an answer to the contextual query based on the contextual query and the document.

    PREDICTIVE AGENTS FOR MULTI-ROUND CONVERSATIONAL RECOMMENDATIONS OF BUNDLED ITEMS
    5.
    发明公开
    PREDICTIVE AGENTS FOR MULTI-ROUND CONVERSATIONAL RECOMMENDATIONS OF BUNDLED ITEMS 审中-公开

    公开(公告)号：US20240169410A1

    公开(公告)日：2024-05-23

    申请号：US17980790

    申请日：2022-11-04

    Applicant: Adobe Inc.

    Inventor： Handong Zhao ,  Zhankui He ,  Tong Yu ,  Fan Du ,  Sungchul Kim

    IPC: G06Q30/06

    CPC classification number: G06Q30/0631

    Abstract: Techniques for predicting and recommending item bundles in a multi-round conversation to discover a target item bundle that would be accepted by a client. An example method includes receiving an input response in reply to a first item bundle that includes one or more items. A state model is updated to reflect the input response to the first item bundle. A machine-learning (ML) conversation module is applied to the state model to determine an action type as a follow-up to the input response to the first item bundle. Based on selection of a recommendation action as the action type, an ML bundling module is applied to the state model to generate a second item bundle different than the first item bundle. The second item bundle is then recommended.

    BUILDING TIME-DECAYED LINE GRAPHS FOR DIRECT EMBEDDING OF CONTINUOUS-TIMED INTERACTIONS IN GENERATING TIME-AWARE RECOMMENDATIONS
    8.
    发明公开
    BUILDING TIME-DECAYED LINE GRAPHS FOR DIRECT EMBEDDING OF CONTINUOUS-TIMED INTERACTIONS IN GENERATING TIME-AWARE RECOMMENDATIONS 审中-公开

    公开(公告)号：US20240311623A1

    公开(公告)日：2024-09-19

    申请号：US18183387

    申请日：2023-03-14

    Applicant: Adobe Inc.

    Inventor： Ryan Rossi ,  Eunyee Koh ,  Jane Hoffswell ,  Nedim Lipka ,  Shunan Guo ,  Sudhanshu Chanpuriya ,  Sungchul Kim ,  Tong Yu

    IPC: G06N3/049

    CPC classification number: G06N3/049

    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for building time-decayed line graphs from temporal graph networks for efficiently and accurately generating time-aware recommendations. For example, the time-decayed line graph system creates a line graph of the temporal graph network by deriving interaction nodes from temporal edges (e.g., timed interactions) and connecting interactions that share an endpoint node. Then, the time-decayed line graph system determines the edge weights in the line graph based on differences in time between interactions, with interactions that occur closer together in time being connected with higher weights. Notably, by using this method, the derived time-decayed line graph directly represents topological proximity and temporal proximity. Upon generating the time-decayed line graphs, the system performs downstream predictive modeling such as predicted edge classifications and/or temporal link predictions.

    Teaching a machine classifier to recognize a new class
    9.
    发明授权
    Teaching a machine classifier to recognize a new class 有权

    公开(公告)号：US11995403B2

    公开(公告)日：2024-05-28

    申请号：US17524282

    申请日：2021-11-11

    Applicant: ADOBE INC.

    Inventor： Sungchul Kim ,  Subrata Mitra ,  Ruiyi Zhang ,  Rui Wang ,  Handong Zhao ,  Tong Yu

    IPC: G06F40/295 ,  G06N20/00

    CPC classification number: G06F40/295 ,  G06N20/00

    Abstract: Embodiments of the technology described herein describe a machine classifier capable of continually learning new classes through a continual few-shot learning approach. A natural language processing (NLP) machine classifier may initially be trained to identify a plurality of other classes through a conventional training process. In order to learn a new class, natural-language training data for a new class is generated. The training data for the new class may be few-shot training data. The training also uses synthetic training data that represents each of the plurality of other classes. The synthetic training data may be generated through a model inversion of the original classifier. The synthetic training data and the natural-language training data are used to retrain the NLP classifier to identify text in the plurality of other classes and the new class using.

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