公开(公告)号：US20240330020A1

公开(公告)日：2024-10-03

申请号：US18128106

申请日：2023-03-29

Applicant: International Business Machines Corporation

Inventor： Jin Wang ,  A Peng Zhang ,  Lei Gao ,  Xian Wu ,  Xiang Zhen Gan ,  Ke Du

IPC: G06F9/451 ,  G06F3/0482

CPC classification number: G06F9/451 ,  G06F3/0482

Abstract: Techniques are described with regard to user interface configuration in a computing environment. An associated computer-implemented method includes initializing an element layout within a set of user interface layers for a certain user based upon random determination, wherein the element layout includes a plurality of elements on which the certain user operates. Responsive to determining that a predefined user history data threshold is exceeded, the method further includes deriving weight metrics for the plurality of elements in association with each of the set of user interface layers based upon user history data, applying at least one layout mode to respective elements among the plurality of elements associated with each of the set of user interface layers based upon the derived weight metrics, and updating the element layout within each of the set of user interface layers for the certain user consequent to applying the at least one layout mode.

公开(公告)号：US11567753B1

公开(公告)日：2023-01-31

申请号：US17462263

申请日：2021-08-31

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Lei Gao ,  Yan Liu ,  Bo Shen ,  Zhen Liu ,  Kai Li ,  Jin Wang ,  A Peng Zhang

IPC: G06F8/65 ,  G06N20/00 ,  G06F11/36

Abstract: Systems and methods are provided to recommend software patches based on task operation mapping. In embodiments, a method includes abstracting test cases for a software patch into a sequence of task operations and parameters associated with each task operation; encoding the task operations and the parameters associated with each task operation based on predetermined rules, thereby generating encoded task operations with unique identifiers assigned thereto and associated encoded parameters with numeric values assigned thereto; generating, using machine learning, a list of frequent operation items, based on the encoded task operations and the associated encoded parameters; generating, using clustering, clusters of parameters for each frequent operation item in the list of frequent operation items; and sending a software patch package including the list of frequent operation items, the clusters of parameters and the software patch to a remote server for distribution to one or more user devices.

公开(公告)号：US20250077325A1

公开(公告)日：2025-03-06

申请号：US18457435

申请日：2023-08-29

Applicant: International Business Machines Corporation

Inventor： Lei Gao ,  Jin Wang ,  A Peng Zhang ,  Kai Li ,  Matthew Wayne Howard ,  YU WANG

IPC: G06F9/54

Abstract: A method for dynamically adjusting a proxy server timeout includes identifying each microservice within a proxy server and mapping a topology of microservices, assigning each microservice to one of an application layer, a middleware layer and an infrastructure layer. Defining each representational state transfer (REST) application programming interface (API) calling relationship between each microservice and each other microservice. Determining a corresponding regression model defining a response time of each microservice based at least in part on a set of available response time predictors. Building a sequence model for at least one microservice in the application layer. Predicting an incoming REST API call and identifying a probable sequence model corresponding to the predicted incoming REST API call. Updating a timeout value of the predicted REST API call within the proxy server based on the sequentially predicted response times.

公开(公告)号：US20240086730A1

公开(公告)日：2024-03-14

申请号：US17943398

申请日：2022-09-13

Applicant: International Business Machines Corporation

Inventor： Jin Wang ,  Lei Gao ,  A Peng Zhang ,  Kai Li ,  Xin Feng Zhu ,  Geng Wu Yang ,  Jia Xing Tang ,  Yan Liu

IPC: G06N5/02

CPC classification number: G06N5/022

Abstract: At least one processor identifies dependency relationships among libraries in a repository of libraries. Using the dependency relationships among libraries, at least one machine learning model can be created that predicts with a confidence value a dependency between a given library and a target library. An L layer tree-like graph can be created, using the dependency relationships among libraries and an application package. L can be configurable. Versions of the libraries to use can be determined by running the at least one machine learning model for each pair of nodes having a dependency relationship in the L layer tree-like graph, the at least one machine learning model identifying the dependency relationship with a confidence value, where pairs of nodes having largest confidence values are selected as the versions of the libraries to use in the application package.

公开(公告)号：US20220245483A1

公开(公告)日：2022-08-04

申请号：US17166793

申请日：2021-02-03

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Jing James Xu ,  Lei Gao ,  A Peng Zhang ,  Rui Wang ,  Si Er Han ,  Xiao Ming Ma

IPC: G06N5/04

Abstract: A method for identifying influential effects that contribute most to a status change of a target index for goal seeking analysis. The method includes generating a candidate list of significant changed predictors between the normal and abnormal status time periods in collected data, and building a plurality of regression models from the collected data. The method determines a first value (trend value or Pearson correlation value) for each of the significant changed predictors based on whether at least one of the significant changed predictors have a significant change trend using the regression models. The method obtains a second predictor importance value for each of the significant changed predictors from a single model built on all the collected data. The method generates a final predictor value for each of the significant changed predictors by combining the first value with the second predictor importance value for each of the significant changed predictors.

公开(公告)号：US11150631B2

公开(公告)日：2021-10-19

申请号：US16427383

申请日：2019-05-31

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Lei Fan ,  Sier Han ,  Xiao Ming Ma ,  A Peng Zhang

IPC: G05B19/4065 ,  G05B23/02

Abstract: Statistically significant event patterns predict the timing for performing entity maintenance. Event patterns are determined based on a target variable having an undesired value for a given entity when the event pattern occurs. Event patterns are filtered based on distributions of the event patterns across multiple entities and distributions of event patterns during desired operation of the entities and undesired operation of the entities. A predictive maintenance process is established having significant event patterns as the basis for maintenance tasks.

公开(公告)号：US20250110707A1

公开(公告)日：2025-04-03

申请号：US18476435

申请日：2023-09-28

Applicant: International Business Machines Corporation

Inventor： Lei Gao ,  Jin Wang ,  A Peng Zhang ,  Kai Li ,  Yan Liu ,  Geng Wu Yang

IPC: G06F9/38

Abstract: Computer-implemented methods for discovery and reuse of a high value pipeline segment are provided. Aspects include defining a set of datasets associated with a processing pipeline based on a set of data operations of the processing pipeline. Aspects also include generating a library of pipeline segments based on the processing pipeline and at least one dataset of the set of datasets. In some aspects, generating the library of pipeline segments includes adding a pipeline segment of the processing pipeline to the library based on one or more characteristics of a dataset generated by the pipeline segment, where the dataset is included in the set of datasets.

公开(公告)号：US12153953B2

公开(公告)日：2024-11-26

申请号：US17225427

申请日：2021-04-08

Applicant: International Business Machines Corporation

Inventor： A Peng Zhang ,  Lei Gao ,  Jin Wang ,  Jing James Xu ,  Jun Wang ,  Dong Hai Yu

IPC: G06F9/48 ,  G06F9/50 ,  G06F11/30 ,  G06F11/34 ,  G06F12/0893 ,  G06N20/00

Abstract: Mechanisms are provided for intelligently identifying an execution environment to execute a computing job. An execution time of the computing job in each execution environment of a plurality of execution environments is predicted by applying a set of existing machine learning models matching execution context information and key parameters of the computing job and execution environment information of the execution environment. The predicted execution time of the machine learning models is aggregated. The aggregated predicted execution times of the computing job are summarized for the plurality of execution environments. Responsive to a selection of an execution environment from the plurality of execution environments based on the summary of the aggregated predicted execution times of the computing job, the computing job is executed in the selected execution environment. Related data during the execution of the computing job in the selected execution environment is collected.

公开(公告)号：US11729273B2

公开(公告)日：2023-08-15

申请号：US17647094

申请日：2022-01-05

Applicant: International Business Machines Corporation

Inventor： Jin Wang ,  Lei Gao ,  A Peng Zhang ,  Kai Li ,  Jun Wang ,  Yan Liu ,  Jia Xing Tang

IPC: H04L67/143

CPC classification number: H04L67/143

Abstract: Systems and techniques for determining an idle timeout for a cloud computing session are described. An example technique includes determining a first one or more attributes associated with a user of the cloud computing session and determining a second one or more attributes associated with an operation of the cloud computing session. An idle timeout for the cloud computing session is determined, based at least in part on the first one or more attributes and the second one or more attributes. User activity is monitored during the cloud computing session. Upon determining, based on the monitoring, an absence of the activity of the user within a duration of the idle timeout, the cloud computing session is terminated.

公开(公告)号：US20220351069A1

公开(公告)日：2022-11-03

申请号：US17245363

申请日：2021-04-30

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Shuo Li ,  Meng Wan ,  A Peng Zhang ,  Xiaobo Wang ,  Sheng Yan Sun

IPC: G06N20/00 ,  H04L29/08

Abstract: The invention provides a federated model based on locally trained machine learning models. In embodiments, a method includes: monitoring, by a computing device, cached data of an entity in a networked group of entities for changes in data, wherein the cached data includes model output data from worker models and a master feature model of the entity, and wherein the worker models and the master model comprise machine learning models; iteratively updating, by the computing device, parameter weights of the worker models and the master feature model based on the monitoring, thereby generating updated worker models and an updated master feature model; and providing, by the computing device, the updated worker models and an updated master feature model to a remote federated server for use in a federated model incorporating the updated worker models and an updated master feature model of the entity with other updated master feature models and other updated worker models of other entities in the networked group of entities.