公开(公告)号：US12231491B1

公开(公告)日：2025-02-18

申请号：US18509247

申请日：2023-11-14

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Bo Song ,  Jun Wang ,  Dong Hai Yu ,  Yao Dong Liu ,  Xiao Ming Ma ,  Jiang Bo Kang

IPC: H04L67/1008 ,  H04L41/16 ,  H04L67/1012

Abstract: A method for forecasting server demand includes collecting a historical number of scoring requests from a network using a serverless architecture. A scoring request capacity per server is determined using the historical number of scoring requests. A prediction model predicts a first future value of scoring requests for a first future time span. A current number of servers in a pool of servers handling the scoring requests. Using the prediction model, a determination of whether the current number of servers is capable of handling the first future value of scoring requests for the first future time span is made. Upon determining that the current number of servers is incapable of handling the first future value of scoring requests, one or more additional servers are warmed up. The warmed-up additional servers are added to the pool of servers prior to an arrival of the first future time span.

公开(公告)号：US20250053858A1

公开(公告)日：2025-02-13

申请号：US18446149

申请日：2023-08-08

Applicant: International Business Machines Corporation

Inventor： Si Er Han ,  Xiao Ming Ma ,  Wen Pei Yu ,  Xue Ying Zhang ,  Jing Xu ,  Jing James Xu ,  Jun Wang ,  Lei Tian

IPC: G06N20/00

Abstract: In an approach, a processor selects a top N features for a machine learning (ML) model; discretizes values of each continuous feature of the top N features; generates a set of combination values that each represent a unique combination of feature values in for a data record; predicts, using the ML model, a target value for each record generating predicted target values; groups the predicted target values based on the combination value for each respective record; fits a distribution for each grouping of the predicted target values associated with a respective combination value generating a set of distributions; clusters and refits the set of distributions using a clustering algorithm resulting in a set of clusters and a refitted distribution for each cluster of the set of clusters; and outputs a visualization of the refitted distribution for each cluster as a distribution curve on a graph along with the associated records.

公开(公告)号：US20240427684A1

公开(公告)日：2024-12-26

申请号：US18337469

申请日：2023-06-20

Applicant: International Business Machines Corporation

Inventor： Si Er Han ,  Xiao Ming Ma ,  Jun Wang ,  Wen Pei Yu ,  Xue Ying Zhang ,  Jing James Xu ,  Jing Xu

IPC: G06F11/30

Abstract: A computer-implemented method, a system and a computer program product for abnormal point simulation are disclosed. A processor analyzes a plurality of data blocks in first time series data to determine traits of respective data blocks. For the respective data blocks, a processor simulates one or more abnormal points based on the traits of the respective data blocks.

公开(公告)号：US20240411783A1

公开(公告)日：2024-12-12

申请号：US18333510

申请日：2023-06-12

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Xue Ying Zhang ,  Si Er Han ,  Jing Xu ,  Xiao Ming Ma ,  Wen Pei Yu ,  Jing James Xu ,  Jun Wang ,  Ji Hui Yang

IPC: G06F16/28 ,  G06F11/34

Abstract: A computer-implemented method for treating post-modeling data includes computing, sequentially for each category of a feature, a category importance (CI) value. The CI value is based on a model accuracy change when records of a category being examined are reassigned to a remaining set of categories of the feature according to a cumulative distribution of records among the remaining set of categories of the feature, wherein the remaining set of categories include all categories of the feature, except for the category being examined. A post-modeling category is performed to merge of each category having the CI value less than a CI value threshold.

公开(公告)号：US11966340B2

公开(公告)日：2024-04-23

申请号：US17654965

申请日：2022-03-15

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Long Vu ,  Bei Chen ,  Xuan-Hong Dang ,  Peter Daniel Kirchner ,  Syed Yousaf Shah ,  Dhavalkumar C. Patel ,  Si Er Han ,  Ji Hui Yang ,  Jun Wang ,  Jing James Xu ,  Dakuo Wang ,  Gregory Bramble ,  Horst Cornelius Samulowitz ,  Saket K. Sathe ,  Wesley M. Gifford ,  Petros Zerfos

IPC: G06F12/0871 ,  G06N20/00

CPC classification number: G06F12/0871 ,  G06N20/00 ,  G06F2212/604

Abstract: To automate time series forecasting machine learning pipeline generation, a data allocation size of time series data may be determined based on one or more characteristics of a time series data set. The time series data may be allocated for use by candidate machine learning pipelines based on the data allocation size. Features for the time series data may be determined and cached by the candidate machine learning pipelines. Predictions of each of the candidate machine learning pipelines using at least the one or more features may be evaluated. A ranked list of machine learning pipelines may be automatically generated from the candidate machine learning pipelines for time series forecasting based upon evaluating predictions of each of the one or more candidate machine learning pipelines.

公开(公告)号：US11836483B1

公开(公告)日：2023-12-05

申请号：US17804322

申请日：2022-05-27

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Jun Wang ,  Dong Hai Yu ,  Bo Song ,  Rui Wang ,  Yao Dong Liu ,  Jiang Bo Kang

IPC: H04L9/08 ,  G06F9/445 ,  G06N20/00 ,  G06F9/455 ,  G06F8/71

CPC classification number: G06F8/71 ,  G06N20/00

Abstract: Described are techniques for machine learning library management. The techniques include generating a table including a plurality of machine learning libraries and their current versions that are used in a deployed machine learning platform (MLP) instance, a first available version upgrade for a first machine learning library of the plurality of machine learning libraries, a security indication associated with the first available version upgrade relative to a current version implemented by the first machine learning library, and a compatibility indication between the first available version upgrade and the current version of the first machine learning library. The techniques further include generating a recommendation related to upgrading the first machine learning library based on the security indication and the compatibility indication.

公开(公告)号：US20230359941A1

公开(公告)日：2023-11-09

申请号：US17739716

申请日：2022-05-09

Applicant: International Business Machines Corporation

Inventor： Dong Hai Yu ,  Jun Wang ,  Bo Song ,  Yao Dong Liu ,  Jiang Bo Kang ,  Lei Tian ,  XING WEI

IPC: G06N20/20 ,  G06Q20/40

CPC classification number: G06N20/20 ,  G06Q20/4016

Abstract: A computer-implemented system, platform, programing product, and/or method for improving transformation selection in an ensemble machine learning (ML) model that includes: providing all base ML models of the ensemble ML model; identifying all of a plurality of Derived Fields in all the base ML models; performing a Derived Field run prediction analysis for all the Derived Fields; computing the Derived Field Importance Weight for Field (DFIW4F) and the Derived Field Importance Weight for Model (DFIW4M) for all the Derived Fields; clustering all the Derived Fields into a plurality of Derived Field clusters, wherein each Derived Field cluster is based upon the DFIW4M and the DFIW4F for the Derived Field; sorting all the Derived Field clusters by best cluster based upon DFIW4M and DFIW4F; and running the base ML models based upon the Derived Fields in the best Derived Field cluster until sufficient base ML models have been run.

公开(公告)号：US20230289693A1

公开(公告)日：2023-09-14

申请号：US17654612

申请日：2022-03-14

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Wen Pei Yu ,  Xiao Ming Ma ,  Xue Ying Zhang ,  Si Er Han ,  Jing James Xu ,  Jing Xu ,  Rui Wang ,  Jun Wang ,  Ji Hui Yang

IPC: G06Q10/06 ,  G06F11/34

CPC classification number: G06Q10/06375 ,  G06F11/3457

Abstract: A method, computer system, and a computer program product for performing an interactive outcome analysis is provided. The present invention may include generating, by a computer, a first estimation outcome from a first plurality of input conditions. The present invention may include generating, by the computer, a parallel estimation outcome from a second plurality of input conditions, wherein at least one of said input conditions in said first plurality of input conditions is different from any of said second plurality of input conditions. The present invention may include selecting, by the computer, either said first or said parallel estimation outcome by analyzing said outcomes with one another and with a target goal outcome.

公开(公告)号：US20230214454A1

公开(公告)日：2023-07-06

申请号：US17568305

申请日：2022-01-04

Applicant: International Business Machines Corporation

Inventor： Ke Wei Wei ,  Jun Wang ,  Shuang YS Yu ,  Guang Ming Zhang ,  Yuan Feng ,  Yi Dai ,  Ling Zhuo ,  Jing Xu

IPC: G06K9/62 ,  G06N20/20

CPC classification number: G06K9/6257 ,  G06K9/6263 ,  G06K9/6219 ,  G06N20/20

Abstract: An embodiment generates an initial set of training data from monitoring data. The initial set of training data is generated by combining outputs from a plurality of pretrained classifiers. The embodiment trains a new classification model using the initial set of training data to identify anomalies in monitoring data. The embodiment performs a multiple-level clustering of the data samples resulting in a plurality of clusters of sub-clusters of data samples, and generates a review list of data samples by selecting a representative data sample from each of the clusters. The embodiment receives an updated data sample from the expert review that includes a revised target classification for at least one of the data samples of the expert review list. The embodiment then trains another replacement classification model using a revised set of training data that includes the updated data sample and associated revised target classification.

公开(公告)号：US20230138987A1

公开(公告)日：2023-05-04

申请号：US17453565

申请日：2021-11-04

Applicant: International Business Machines Corporation

Inventor： Song Bo ,  Dong Hai Yu ,  Jun Wang ,  Jiang Bo Kang ,  Yao Dong Liu

IPC: G06N3/08 ,  G06N20/20

Abstract: One or more computer processors calculate a cache prediction for a received inference request within an inference cache structured as a self-learning tree, wherein the inference request comprises a set of input values. The one or more computer processors responsive to the retrieved cache prediction exceeding a cache prediction threshold, transmit the cache prediction. The one or more computer processors parallel compute a model prediction for the received inference request utilizing a trained model. The one or more computer processors responsive to the retrieved model prediction exceeding a model prediction threshold, convert the trained model into a tree structure. The one or more computer processors update the inference cache with the converted train model. The one or more computer processors transmit the model prediction.