公开(公告)号：US20160378577A1

公开(公告)日：2016-12-29

申请号：US15089049

申请日：2016-04-01

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Jerry Paul RUSSELL ,  Haobo HE ,  Greg MA ,  Xin XU

IPC: G06F9/54 ,  G06F9/445

CPC classification number: G06F17/30554 ,  G06F3/04842 ,  G06F9/44505 ,  G06F9/542 ,  G06F11/00 ,  G06F17/2282 ,  G06F17/2705 ,  G06F17/30073 ,  G06F17/30091 ,  G06F17/30289 ,  G06F17/30321 ,  G06F17/30368 ,  G06F17/30477 ,  G06F17/30707 ,  G06F17/30914 ,  G06N99/005 ,  H04L41/145 ,  H04L41/5074 ,  H04L43/04

Abstract: Disclosed is a system, method, and computer program product for implementing a log analytics method and system that can configure, collect, and analyze log records in an efficient manner. An improved approach is provided for identifying log files that have undergone a change in status that would require retrieve of its log data, by including a module directly into the operating system that allows the log collection component to be reactively notified of any changes to pertinent log files.

Abstract translation: 公开了一种用于实现可以以有效的方式配置，收集和分析日志记录的日志分析方法和系统的系统，方法和计算机程序产品。 提供了一种改进的方法，用于识别已经经历了需要检索其日志数据的状态更改的日志文件，方法是将模块直接包含在操作系统中，从而使日志收集组件能够被反应地通知有关日志的任何更改 文件。

公开(公告)号：US20230419052A1

公开(公告)日：2023-12-28

申请号：US18163231

申请日：2023-02-01

Applicant: Oracle International Corporation

Inventor： Ahmed Ataallah Ataallah Abobakr ,  Shivashankar Subramanian ,  Ying Xu ,  Vladislav Blinov ,  Umanga Bista ,  Tuyen Quang Pham ,  Thanh Long Duong ,  Mark Edward Johnson ,  Elias Luqman Jalaluddin ,  Vanshika Sridharan ,  Xin XU ,  Srinivasa Phani Kumar Gadde ,  Vishal Vishnoi

IPC: G06F40/56 ,  G06F40/295 ,  G06F40/247

CPC classification number: G06F40/56 ,  G06F40/247 ,  G06F40/295

Abstract: Novel techniques are described for positive entity-aware augmentation using a two-stage augmentation to improve the stability of the model to entity value changes for intent prediction. In one particular aspect, a method is provided that includes accessing a first set of training data for an intent prediction model, the first set of training data comprising utterances and intent labels; applying one or more positive data augmentation techniques to the first set of training data, depending on the tuning requirements for hyper-parameters, to result in a second set of training data, where the positive data augmentation techniques comprise Entity-Aware (“EA”) technique and a two-stage augmentation technique; combining the first set of training data and the second set of training data to generate expanded training data; and training the intent prediction model using the expanded training data.