中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

56 records (took 0.018 seconds)

    HIERARCHICAL TOPIC MODEL WITH AN INTERPRETABLE TOPIC HIERARCHY
    31.
    发明公开
    HIERARCHICAL TOPIC MODEL WITH AN INTERPRETABLE TOPIC HIERARCHY 审中-公开

    公开(公告)号：US20240004912A1

    公开(公告)日：2024-01-04

    申请号：US17853141

    申请日：2022-06-29

    Applicant: Adobe Inc.

    Inventor： Tanay Anand ,  Sumit Bhatia ,  Simra Shahid ,  Nikitha Srikanth ,  Nikaash Puri

    IPC: G06F16/35 ,  G06K9/62 ,  G06F16/93 ,  G06F40/30 ,  G06F16/33

    CPC classification number: G06F16/35 ,  G06K9/6239 ,  G06K9/6276 ,  G06F16/93 ,  G06F40/30 ,  G06F16/3347

    Abstract: Some techniques described herein relate to generating a hierarchical topic model (HTM), which can be used to generate custom content. In one example, a method includes determining first-level topics in a topic hierarchy related to a corpus of documents. A first-level topic of the first-level topics includes multiple words. The multiple words are grouped into clusters based on word embeddings of the multiple words. The multiple words are then subdivided into second-level topics as subtopics of the first-level topic, such that the number of second-level topics equals the number of clusters. A document of the corpus of documents is assigned to the first-level topic and to a second-level topic of the second-level topics, and an indication is received of access by a user to the document. Custom content is generated for the user based on one or more other documents assigned to the first-level topic and the second-level topic.

    SELF-SUPERVISED HIERARCHICAL EVENT REPRESENTATION LEARNING
    33.
    发明公开
    SELF-SUPERVISED HIERARCHICAL EVENT REPRESENTATION LEARNING 审中-公开

    公开(公告)号：US20230154186A1

    公开(公告)日：2023-05-18

    申请号：US17455126

    申请日：2021-11-16

    Applicant: ADOBE INC.

    Inventor： Sumegh Roychowdhury ,  Sumedh A. Sontakke ,  Mausoom Sarkar ,  Nikaash Puri ,  Pinkesh Badjatiya ,  Milan Aggarwal

    IPC: G06K9/00 ,  G06N3/08

    CPC classification number: G06K9/00718 ,  G06K9/00751 ,  G06N3/088 ,  G06K2009/00738

    Abstract: Systems and methods for video processing are described. Embodiments of the present disclosure generate a plurality of image feature vectors corresponding to a plurality of frames of a video; generate a plurality of low-level event representation vectors based on the plurality of image feature vectors, wherein a number of the low-level event representation vectors is less than a number of the image feature vectors; generate a plurality of high-level event representation vectors based on the plurality of low-level event representation vectors, wherein a number of the high-level event representation vectors is less than the number of the low-level event representation vectors; and identify a plurality of high-level events occurring in the video based on the plurality of high-level event representation vectors.

    Machine learning-based generation of target segments
    34.
    发明授权
    Machine learning-based generation of target segments 有权

    公开(公告)号：US11538051B2

    公开(公告)日：2022-12-27

    申请号：US16168288

    申请日：2018-10-23

    Applicant: Adobe Inc.

    Inventor： Praveen Kumar Goyal ,  Piyush Gupta ,  Nikaash Puri ,  Balaji Krishnamurthy ,  Arun Kumar ,  Atul Kumar Shrivastava

    IPC: G06Q30/02 ,  G06N7/00 ,  H04N21/442 ,  H04N21/81 ,  G06N20/00 ,  G06F16/28

    Abstract: Techniques are described for machine learning-based generation of target segments is leveraged in a digital medium environment. A segment targeting system generates training data to train a machine learning model to predict strength of correlation between a set of users and a defined demographic. Further, a machine learning model is trained with visit statistics for the users to predict the likelihood that the users will visit a particular digital content platform. Those users with the highest predicted correlation with the defined demographic and the highest likelihood to visit the digital content platform can be selected and placed within a target segment, and digital content targeted to the defined demographic can be delivered to users in the target segment.

    GENERATING COMBINED FEATURE EMBEDDING FOR MINORITY CLASS UPSAMPLING IN TRAINING MACHINE LEARNING MODELS WITH IMBALANCED SAMPLES
    35.
    发明申请
    GENERATING COMBINED FEATURE EMBEDDING FOR MINORITY CLASS UPSAMPLING IN TRAINING MACHINE LEARNING MODELS WITH IMBALANCED SAMPLES 有权

    公开(公告)号：US20210073671A1

    公开(公告)日：2021-03-11

    申请号：US16564531

    申请日：2019-09-09

    Applicant: Adobe, Inc.

    Inventor： Nikaash Puri ,  Balaji Krishnamurthy ,  Ayush Chopra

    IPC: G06N20/00 ,  G06N5/04

    Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for generating combined feature embeddings for minority class upsampling in training machine learning models with imbalanced training samples. For example, the disclosed systems can select training sample values from a set of training samples and a combination ratio value from a continuous probability distribution. Additionally, the disclosed systems can generate a combined synthetic training sample value by modifying the selected training sample values using the combination ratio value and combining the modified training sample values. Moreover, the disclosed systems can generate a combined synthetic ground truth label based on the combination ratio value. In addition, the disclosed systems can utilize the combined synthetic training sample value and the combined synthetic ground truth label to generate a combined synthetic training sample and utilize the combined synthetic training sample to train a machine learning model.

    Recommendations based on feature usage in applications
    37.
    发明授权
    Recommendations based on feature usage in applications 有权

    公开(公告)号：US10536580B2

    公开(公告)日：2020-01-14

    申请号：US15705042

    申请日：2017-09-14

    Applicant: ADOBE INC.

    Inventor： Nikaash Puri ,  Shagun Sodhani

    IPC: G06F17/30 ,  H04M7/00 ,  H04L29/08 ,  H04M1/725

    Abstract: Some implementations provide a feature recommendation system that receives sequences from user sessions with applications, where each sequence is of features of the applications in an order the features were used by a user. The sequences are applied to a feature embedding model that learns semantic similarities between the features based on occurrences of the features in the sequences in a same user session. A request is received for a feature recommendation that identifies a feature of an application used by a given user in a user session. A recommended feature for the feature recommendation is determined from a set of the semantic similarities that are between the identified feature and others of the features. The feature recommendation is presented on a user device associated with the given user.

    Self-supervised hierarchical event representation learning
    40.
    发明授权
    Self-supervised hierarchical event representation learning 有权

    公开(公告)号：US11948358B2

    公开(公告)日：2024-04-02

    申请号：US17455126

    申请日：2021-11-16

    Applicant: ADOBE INC.

    Inventor： Sumegh Roychowdhury ,  Sumedh A. Sontakke ,  Mausoom Sarkar ,  Nikaash Puri ,  Pinkesh Badjatiya ,  Milan Aggarwal

    IPC: G06V20/40 ,  G06N3/088

    CPC classification number: G06V20/41 ,  G06N3/088 ,  G06V20/47 ,  G06V20/44

    Abstract: Systems and methods for video processing are described. Embodiments of the present disclosure generate a plurality of image feature vectors corresponding to a plurality of frames of a video; generate a plurality of low-level event representation vectors based on the plurality of image feature vectors, wherein a number of the low-level event representation vectors is less than a number of the image feature vectors; generate a plurality of high-level event representation vectors based on the plurality of low-level event representation vectors, wherein a number of the high-level event representation vectors is less than the number of the low-level event representation vectors; and identify a plurality of high-level events occurring in the video based on the plurality of high-level event representation vectors.

Patent Agency Ranking