利索-全球专利检索

  1. 登录
  2. 注册

搜索结果

4 条记录 (耗时 0.012 秒)

    Method and system for predicting task completion of a time period based on task completion rates of prior time periods using machine learning
    2.
    发明授权
    Method and system for predicting task completion of a time period based on task completion rates of prior time periods using machine learning 有权

    公开(公告)号：US10430239B2

    公开(公告)日：2019-10-01

    申请号：US15246513

    申请日：2016-08-24

    申请人： Clari Inc.

    发明人： Lei Tang ,  Mark Shuai Sun ,  Xin Xu

    IPC分类号： G06F9/48 ,  G06F17/18

    摘要： A request is received from a client for determining task completion of a first set of tasks associated with attributes, the first set of tasks scheduled to be performed within a first time period. For each of the attributes, a completion rate of one or more of a second set of tasks is calculated that are associated with the attribute. The second set of tasks has been performed during a second time period in the past. An isotonic regression operation and/or temporal smoothing are performed on the completion rates associated with the attributes of the second set of tasks that have been performed during the second time period to calibrate the completion rates. Possible completion for the attributes of the first set of tasks to be performed in the first time period is calculated based on the calibrated completion rates of the second set of tasks.

    System and method for generating scores for predicting probabilities of task completion
    3.
    发明授权
    System and method for generating scores for predicting probabilities of task completion 有权

    公开(公告)号：US11651212B2

    公开(公告)日：2023-05-16

    申请号：US16729097

    申请日：2019-12-27

    申请人： Clari Inc.

    发明人： Xin Xu ,  Venkat Rangan

    IPC分类号： G06N3/00 ,  G06F16/00 ,  G06Q10/00 ,  G06Q30/00 ,  G06N3/08 ,  G06F16/25 ,  G06Q10/0631 ,  G06N3/04 ,  G06Q30/016

    CPC分类号： G06N3/08 ,  G06F16/254 ,  G06N3/0454 ,  G06Q10/06316 ,  G06Q30/016

    摘要： According to various embodiments, described herein are systems and methods for training machine learning (ML) models to generate real-time scores to predict the probabilities of task completion. In one embodiment, an exemplary method includes the operations of receiving, from a data store, a set of features and a workflow for training a first type of ML models, the workflow specifying a data source, a number of stages and associated parameters for training the ML models; retrieving, from the data source, training data for the set of features; and segmenting the training data into different segments. The method further includes the operations of training a separate first type of ML model using each of the different segment of the training data in accordance with the workflow; and persisting the first type of trained ML models into the data storage. The method also includes using a trained ML model to generate probability scores and displaying the scores to users in real-time.

    Method for estimating amount of task objects required to reach target completed tasks
    4.
    发明授权
    Method for estimating amount of task objects required to reach target completed tasks 有权

    公开(公告)号：US11250357B2

    公开(公告)日：2022-02-15

    申请号：US16163546

    申请日：2018-10-17

    申请人： Clari Inc.

    发明人： Xin Xu ,  Chunyue Du ,  Xincheng Ma ,  Kaiyue Wu ,  Venkat Rangan

    IPC分类号： G06Q10/06 ,  G06F16/903 ,  G06F9/48 ,  G06N7/00

    摘要： In an embodiment, described herein is a system and method for creating a suggested task set to meet a target value. A cloud server, in response to receiving a request specifying a target value, retrieves completed task sets from a database. Each completed task set includes a same set of task categories. The cloud server derives a number of ratios from the retrieved completed task sets, including a composition ratio and a conversion rate for each task category, and an addition ratio for the number of completed task sets. Based on the derived ratios and the specified target value, the cloud server constructs the suggested task set, and displays in real-time the suggested task set together with current values for the task categories. The cloud server alerts users of a discrepancy between a current value and the corresponding suggested value for a task category when the discrepancy reaches a predetermined level.