利索-全球专利检索

  1. 登录
  2. 注册

搜索结果

57553 条记录 (耗时 0.044 秒)

    STROKE BASED CONTROL OF HANDWRITING INPUT
    3.
    发明公开
    STROKE BASED CONTROL OF HANDWRITING INPUT 审中-公开

    公开(公告)号：US20240362943A1

    公开(公告)日：2024-10-31

    申请号：US18766494

    申请日：2024-07-08

    申请人： Apple Inc.

    发明人： Ryan S. DIXON ,  Adrien DELAYE ,  Dominic L. HOWELL ,  Andrew E. PLATZER

    IPC分类号： G06V30/32 ,  G06F3/04883 ,  G06F40/279 ,  G06N20/00

    CPC分类号： G06V30/36 ,  G06F3/04883 ,  G06F40/279 ,  G06N20/00

    摘要： The subject technology provides for stroke based control of handwriting input. The disclosed stroke based control facilitates selection, copy, paste, search, data detection and other operations for handwritten electronic text. The selection of text represented by handwritten strokes can be performed without drawing a lasso or other loop around the desired text, by using known boundaries of words and phrases in stroke space. Selection of text in this manner allows copy and/or paste of recognized words or phrases, of images of the words or phrases, and/or of the strokes themselves. Boundaries, in stroke space, of actionable data represented by the strokes can also allow action options to be provided when a user interacts with strokes within the boundary.

    METHODS FOR GENERATING IMAGE SUPER-RESOLUTION DATA SET, IMAGE SUPER-RESOLUTION MODEL AND TRAINING METHOD
    4.
    发明公开
    METHODS FOR GENERATING IMAGE SUPER-RESOLUTION DATA SET, IMAGE SUPER-RESOLUTION MODEL AND TRAINING METHOD 审中-公开

    公开(公告)号：US20240362747A1

    公开(公告)日：2024-10-31

    申请号：US18535223

    申请日：2023-12-11

    申请人： BEIJING WEILING TIMES TECHNOLOGY CO., LTD.

    发明人： Jianjun GUO

    IPC分类号： G06T3/4053 ,  G06N20/00

    CPC分类号： G06T3/4053 ,  G06N20/00

    摘要： A method for generating an image super-resolution data set, an image super-resolution model and a training method. The method for generating an image super-resolution data set comprises steps of: S101: constructing a high-resolution image set; S102: performing image blind degradation processing on all high-resolution images HR1 to obtain an LR1-HR1 data set; S103: training a first model with the LR1-HR1 data set to obtain a model parameter of the first model and saving the model parameter; S104: constructing a low-resolution image set; and S105: inputting all low-resolution images LR2 into the first model to obtain an LR2-SR2 data set after inference by the first model. Using the LR2-SR2 data set of the present disclosure, training can be performed on a model with a relatively simple structure, the learning speed is fast, and the trained model has strong generalization ability.

    TRAINING REGRESSION MODELS USING TRUTH SET DATA PROXIES
    7.
    发明公开
    TRAINING REGRESSION MODELS USING TRUTH SET DATA PROXIES 审中-公开

    公开(公告)号：US20240362538A1

    公开(公告)日：2024-10-31

    申请号：US18645222

    申请日：2024-04-24

    申请人： Daash Intelligence, Inc.

    发明人： Justin T. Stewart ,  Philip M. Smolin ,  Melissa S. Munnerlyn ,  Liam N. Isaacs ,  Phillip J. Markert ,  Vinoad Senguttuvan

    IPC分类号： G06N20/00

    CPC分类号： G06N20/00

    摘要： A method of training a machine learning regression model includes defining a prediction accuracy grading function, the prediction accuracy grading function being a many-to-one function that maps prediction accuracies to proxies, each of the prediction accuracies being derivable from a respective prediction of the model and a corresponding actual. The method may further include receiving a plurality of proxies corresponding respectively to a plurality of predictions of the model and, for each of the plurality of proxies, deriving a corresponding approximated actual according to the prediction accuracy grading function. The method may further include calculating an approximated residual for each of the plurality of predictions of the model based on the corresponding approximated actual and adjusting the model based on the approximated residuals.

    AUTOMATED TRAINING BASED DATA LABELING METHOD
    8.
    发明公开
    AUTOMATED TRAINING BASED DATA LABELING METHOD 审中-公开

    公开(公告)号：US20240362536A1

    公开(公告)日：2024-10-31

    申请号：US18642635

    申请日：2024-04-22

    申请人： datamaker

    发明人： Enoch Lee

    IPC分类号： G06N20/00

    CPC分类号： G06N20/00

    摘要： An automated training-based data labeling method according to the present disclosure is configured to generate an artificial intelligence model in which a processor separates some of labeled data into training data as soon as it receives a certain amount of labeled data from a worker terminal, and automatically performs the data labeling on objects in source data through automated training of the training data. According to the present disclosure, since a proportion of worker participation is reduced when labeling the data for the objects in the source data, it is possible to dramatically reduce operation costs required for the data labeling.

    SYSTEMS AND METHODS FOR DATA STRUCTURE GENERATION BASED ON OUTLIER CLUSTERING
    9.
    发明公开
    SYSTEMS AND METHODS FOR DATA STRUCTURE GENERATION BASED ON OUTLIER CLUSTERING 审中-公开

    公开(公告)号：US20240362535A1

    公开(公告)日：2024-10-31

    申请号：US18527542

    申请日：2023-12-04

    申请人： Strategic Coach

    发明人： Barbara Sue Smith ,  Daniel J. Sullivan

    IPC分类号： G06N20/00

    CPC分类号： G06N20/00

    摘要： Disclosed herein are systems and methods for determining data structures. In some embodiments, a classifier may be used to determine one or more attributes of an entity. In some embodiments, a clustering algorithm may be used to determine an attribute cluster. In some embodiments, an impact metric machine learning model may be used to determine an outlier cluster. In some embodiments, an outlier process may be determined as a function of the outlier cluster. In some embodiments, a visual element may be determined as a function of an outlier process and may be displayed to a user.