公开(公告)号：US12282738B2

公开(公告)日：2025-04-22

申请号：US17735525

申请日：2022-05-03

Applicant: SAP SE

Inventor： Lei Xu ,  Deng Feng Wan

IPC: G06F40/00 ,  G06F40/166 ,  G06F40/279 ,  G06Q10/0631

Abstract: Various examples are directed to systems and methods for characterizing natural language text units. A plurality of text units may be used to train a bidirectional model. A bidirectional model may be applied to a set of annotated text units to generate a plurality of span context vectors. The plurality of span context vectors may be used to train a span prediction model. The span prediction model may be applied to at least a portion of the plurality of text units to generate a plurality of span characterizations, a first span characterization corresponding to a first span indicating that the first span describes a first job skill.

公开(公告)号：US11580135B2

公开(公告)日：2023-02-14

申请号：US17089335

申请日：2020-11-04

Applicant: SAP SE

Inventor： Deng Feng Wan ,  Yangchun Deng ,  Hui Zhang ,  Zuxing Wang

IPC: G06F15/173 ,  G06F16/28 ,  G06F11/30 ,  H04L43/08 ,  H04L67/10 ,  H04L67/02 ,  G06F11/34 ,  H04L41/142 ,  H04L9/40

Abstract: Requests are received for handling by a cloud computing environment which are then executed by the cloud computing environment. While each request is executing, performance metrics associated with the request are monitored. A vector is subsequently generated that encapsulates information associated with the request including the text within the request and the corresponding monitored performance metrics. Each request is then assigned (after it has been executed) to either a normal request cluster or an abnormal request cluster based on which cluster has a nearest mean relative to the corresponding vector. In addition, data can be provided that characterizes requests assigned to the abnormal request cluster. Related apparatus, systems, techniques and articles are also described.

公开(公告)号：US20220129745A1

公开(公告)日：2022-04-28

申请号：US17081579

申请日：2020-10-27

Applicant: SAP SE

Inventor： Deng Feng Wan ,  Hui Zhang ,  Zuxing Wang ,  Yangchun Deng

IPC: G06N3/08 ,  G06F16/25

Abstract: Supervised learning creates and trains a model to predict resource consumption by a remote system. Historical time-series data (e.g., monitor logs of CPU consumption, memory consumption) are collected from systems called upon to perform a task. This raw data is transformed into a labeled data set ready for supervised learning. Using the labeled data set, a model is constructed to correlate the input data with a resulting load. The constructed model may be a Sequence to Sequence (Seq2Seq) model based upon Gated Recurrent Units of a Recurrent Neural Network. After training, the model is saved for re-use to predict future load based upon an existing input. For example, the existing input may be data from a most recent 24 hour period (hour0-hour23), and the output of the model may be the load predicted for the next 24 hour period (hour24-hour47). This prediction promotes efficient reservation remote server resources.

公开(公告)号：US20210397625A1

公开(公告)日：2021-12-23

申请号：US16909843

申请日：2020-06-23

Applicant: SAP SE

Inventor： Deng Feng Wan ,  Yangchun Deng ,  Zuxing Wang ,  Hui Zhang

IPC: G06F16/2457 ,  G06N20/00 ,  G06N3/08

Abstract: A query is received that requests issues relevant to a user. Thereafter, a plurality of issues responsive to the query are retrieved. The retrieved issues are ranked using a first machine learning model to result in a first subset of the retrieved issues. The first subset of the retrieved issues are then ranked using a second, different machine learning model to result in a second subset of the retrieved issues which are a subset of the first subset of the retrieved issues. Data can then be provided which is responsive the query and includes at least a portion of the second subset of the retrieved issues. Related apparatus, systems, techniques and articles are also described.