中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

63 records (took 0.028 seconds)

    EFFICIENT VISION-LANGUAGE RETRIEVAL USING STRUCTURAL PRUNING
    32.
    发明申请
    EFFICIENT VISION-LANGUAGE RETRIEVAL USING STRUCTURAL PRUNING 有权

    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

    IPC: G06N3/082 ,  G06N3/04

    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.

    DETECTION AND INTERPRETATION OF LOG ANOMALIES
    33.
    发明公开
    DETECTION AND INTERPRETATION OF LOG ANOMALIES 审中-公开

    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

    IPC: G06F11/07 ,  G06N20/00

    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.

    Latent network summarization
    34.
    发明授权
    Latent network summarization 有权

    公开(公告)号：US11860675B2

    公开(公告)日：2024-01-02

    申请号：US17373281

    申请日：2021-07-12

    Applicant: ADOBE INC.

    Inventor： Di Jin ,  Ryan A. Rossi ,  Eunyee Koh ,  Sungchul Kim ,  Anup Rao

    IPC: G06F16/24 ,  G06F16/2458 ,  G06F16/901 ,  G06F16/26 ,  G06F16/215 ,  G06F16/28

    CPC classification number: G06F16/2465 ,  G06F16/215 ,  G06F16/26 ,  G06F16/288 ,  G06F16/9024 ,  G06F2216/03

    Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for latent summarization of a graph. Structural features can be captured from feature vectors associated with each node of the graph by applying base functions on the feature vectors and iteratively applying relational operators to successive feature matrices to derive deeper inductive relational functions that capture higher-order structural information in different subgraphs of increasing size (node separations). Heterogeneity can be summarized by performing capturing features in appropriate subgraphs (e.g., node-centric neighborhoods associated with each node type, edge direction, and/or edge type). Binning and/or dimensionality reduction can be applied to the resulting feature matrices. The resulting set of relational functions and multi-level feature matrices can form a latent summary that can be used to perform a variety of graph-based tasks, including node classification, node clustering, link prediction, entity resolution, anomaly and event detection, and inductive learning tasks.

    Personalized visualization recommendation system
    36.
    发明授权
    Personalized visualization recommendation system 有权

    公开(公告)号：US11720590B2

    公开(公告)日：2023-08-08

    申请号：US17091941

    申请日：2020-11-06

    Applicant: ADOBE INC.

    Inventor： Ryan Rossi ,  Vasanthi Holtcamp ,  Tak Yeon Lee ,  Sungchul Kim ,  Sana Lee ,  Nathan Ross ,  John Anderson ,  Fan Du ,  Eunyee Koh ,  Xin Qian

    IPC: G06F16/9535 ,  G06F16/26 ,  G06F3/0482 ,  G06F11/34 ,  G06F11/30 ,  G06F16/9038

    CPC classification number: G06F16/26 ,  G06F3/0482 ,  G06F11/302 ,  G06F11/3438 ,  G06F16/9038

    Abstract: Systems and methods for personalized visualization recommendation are described. Embodiments of the described systems and methods are configured to identify a first matrix representing user interactions with a plurality of data attributes corresponding to a plurality of datasets, a second matrix representing user interactions with a plurality of visualizations, and a third matrix representing a plurality of meta-features for each of the data attributes; compute low-dimensional embeddings representing user characteristics, the data attributes, visualization configurations, and the meta-features using joint factorization of the first matrix, the second matrix and the third matrix; generate a model for predicting visualization preference weights based on the low-dimensional embeddings; predict the visualization preference weights for a user corresponding to a plurality of candidate visualizations of dataset using the model; and generate a personalized visualization of the dataset for the user based on the predicted visualization preference weights.

    Temporal-based network embedding and prediction
    37.
    发明授权
    Temporal-based network embedding and prediction 有权

    公开(公告)号：US11621892B2

    公开(公告)日：2023-04-04

    申请号：US17095070

    申请日：2020-11-11

    Applicant: Adobe Inc.

    Inventor： Sungchul Kim ,  Di Jin ,  Ryan A. Rossi ,  Eunyee Koh

    IPC: H04L41/12 ,  H04L43/067 ,  G06F16/901 ,  H04L41/14 ,  H04L43/045

    Abstract: Deriving network embeddings that represent attributes of, and relationships between, different nodes in a network while preserving network data temporal and structural properties is described. A network representation system generates a plurality of graph time-series representations of network data that each includes a subset of nodes and edges included in a time segment of the network data, constrained either by time or a number of edges included in the representation. A temporal graph of the network data is generated by implementing a temporal model that incorporates temporal dependencies into the graph time-series representations. From the temporal graph, network embeddings for the network data are derived, where the network embeddings capture temporal dependencies between nodes, as indicated by connecting edges, as well as temporal structural properties of the network data. Network embeddings represent network data in a low-dimensional latent space, which is useable to generate a prediction regarding the network data.

    PROVISIONING INTERACTIVE CONTENT BASED ON PREDICTED USER-ENGAGEMENT LEVELS
    38.
    发明申请
    PROVISIONING INTERACTIVE CONTENT BASED ON PREDICTED USER-ENGAGEMENT LEVELS 有权

    公开(公告)号：US20220366299A1

    公开(公告)日：2022-11-17

    申请号：US17322108

    申请日：2021-05-17

    Applicant: Adobe Inc.

    Inventor： Atanu R. Sinha ,  Xiang Chen ,  Sungchul Kim ,  Omar Rahman ,  Jean Bernard Hishamunda ,  Goutham Srivatsav Arra

    IPC: G06N20/00 ,  G06F3/0484 ,  H04L29/08

    Abstract: Methods and systems disclosed herein relate generally to systems and methods for using a machine-learning model to predict user-engagement levels of users in response to presentation of future interactive content. A content provider system accesses a machine-learning model, which was trained using a training dataset including previous user-device actions performed by a plurality of users in response to previous interactive content. The content provider system receives user-activity data of a particular user and applies the machine-learning model to the user-activity data, in which the user-activity data includes user-device actions performed by the particular user in response to interactive content. The machine-learning model generates an output including a categorical value that represents a predicted user-engagement level of the particular user in response to a presentation of the future interactive content.

    Predicting and visualizing outcomes using a time-aware recurrent neural network
    40.
    发明授权
    Predicting and visualizing outcomes using a time-aware recurrent neural network 有权

    公开(公告)号：US11475295B2

    公开(公告)日：2022-10-18

    申请号：US16394227

    申请日：2019-04-25

    Applicant: Adobe Inc.

    Inventor： Fan Du ,  Eunyee Koh ,  Sungchul Kim ,  Shunan Guo ,  Sana Malik Lee

    IPC: G06N3/04 ,  G06N3/08 ,  G06N5/02 ,  G06N7/00 ,  G06N20/10

    Abstract: Disclosed systems and methods predict and visualize outcomes based on past events. For example, an analysis application encodes a sequence of events into a feature vector that includes, for each event, a numerical representation of a respective category and a respective timestamp. The application applies a time-aware recurrent neural network to the feature vector, resulting in one or more of (i) a set of future events in which each event is associated with a probability and a predicted duration and (ii) a sequence embedding that contains information about predicted outcomes and temporal patterns observed in the sequence of events. The application applies a support vector model classifier to the sequence embedding. The support vector model classifier computes a likelihood of a categorical outcome for each of the events in the probability distribution. The application modifies interactive content according to the categorical outcomes and probability distribution.

Patent Agency Ranking