专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

1. 发明授权

US11966340B2 Automated time series forecasting pipeline generation 有权
公开(公告)号：US11966340B2
公开(公告)日：2024-04-23
申请号：US17654965
申请日：2022-03-15
申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION
发明人： 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分类号： G06F12/0871 , G06N20/00
CPC分类号： G06F12/0871 , G06N20/00 , G06F2212/604
摘要： 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.

2. 发明申请

US20220138616A1 SCALABLE DISCOVERY OF LEADERS FROM DYNAMIC COMBINATORIAL SEARCH SPACE USING INCREMENTAL PIPELINE GROWTH APPROACH 有权
公开(公告)号：US20220138616A1
公开(公告)日：2022-05-05
申请号：US17086277
申请日：2020-10-30
申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION
发明人： Dhavalkumar C. Patel , Shrey Shrivastava , Jayant R. Kalagnanam , Stuart Siegel , Wesley M. Gifford , Chandrasekhara K. Reddy
IPC分类号： G06N20/00 , G06F16/28
摘要： A computer implemented method includes generating a pipeline graph having a plurality of layers, each of the plurality of layers having one or more machine learning components for performing a predictive modeling task. A plurality of pipelines are operated through the pipeline graph on a training dataset to determine a respective plurality of results. Each of the plurality of pipelines are distinct paths through selected ones of the one or more machine learning components at each of the plurality of layers. The plurality of results are compared to known results based on a user-defined metric to output one or more leader pipelines.

3. 发明授权

US10831827B2 Automatic extraction of user mobility behaviors and interaction preferences using spatio-temporal data 有权
公开(公告)号：US10831827B2
公开(公告)日：2020-11-10
申请号：US15088227
申请日：2016-04-01
申请人： International Business Machines Corporation
发明人： Wesley M. Gifford , Ying Li , Rong Liu , Anshul Sheopuri
IPC分类号： G06F16/901 , G06N20/00 , G06Q30/02
摘要： A user trajectory graph may be constructed based on spatio-temporal data. A mobility pattern may be extracted from the user trajectory graph. Users may be clustered into groups, wherein the users in a same group possess similar feature values in the mobility pattern, and the users in different groups have different feature values, to identify personas and location sets. A distribution model may be constructed that models user timing and location preference, wherein an outcome indicates a preference for a particular time bin on a particular day for a particular location.

4. 发明申请

US20160335909A1 ENHANCING ENTERPRISE LEARNING OUTCOMES 审中-公开
标题翻译：增强企业学习成果
公开(公告)号：US20160335909A1
公开(公告)日：2016-11-17
申请号：US14711877
申请日：2015-05-14
申请人： International Business Machines Corporation , Skillsoft Ireland Limited
发明人： John J. Ambrose , Yi-Min Chee , Wesley M. Gifford , Ashish Jagmohan , Laura W. Rexford , Suzanne M. Rodeman , Anshul Sheopuri , Piyawadee Sukaviriya
IPC分类号： G09B19/00 , G06N5/02 , G09B5/02
CPC分类号： G09B19/00 , G06F16/9535 , G06N7/005 , G06Q10/10 , G09B5/02
摘要： Enterprise learning system may receive as input learning goals for a learner. Learning goals may also be specified by job-roles. The system may output course-sequence recommendations, sequence-associations to learning goals and learning-goal recommendations. A semantic analysis may automatically relate learning-goals to learning assets and job-roles. Computational semantic relation-finding may leverage available knowledge-bases. A sequence-recommendation recommends course or learning asset sequences to the learner based on historical usage, the learner's current and/or desired job-roles and learning goals.
摘要翻译：企业学习系统可以作为学习者的学习目标。学习目标也可以由作业角色指定。该系统可以输出课程顺序建议，序列关联到学习目标和学习目标建议。语义分析可能会自动将学习目标与学习资产和工作角色相关联。计算语义关系发现可以利用现有的知识库。序列建议根据历史使用，学习者目前和/或期望的工作角色和学习目标，建议对学习者进行课程或学习资源序列。

5. 发明申请

US20160055443A1 GENERATING ORGANIZATIONAL MENTORING RELATIONSHIPS 审中-公开
标题翻译：产生组织发展关系
公开(公告)号：US20160055443A1
公开(公告)日：2016-02-25
申请号：US14466152
申请日：2014-08-22
申请人： International Business Machines Corporation
发明人： Yi-Min Chee , Wesley M. Gifford , Ashish Jagmohan , Anshul Sheopuri
IPC分类号： G06Q10/06 , G06F17/30
CPC分类号： G06Q10/063112
摘要： A tool for computational generation of organizational mentoring relationships. The tool determines a mentor pool and a mentee pool based, at least in part, on per-person domain metric data for each person in a general pool. The tool determines a plurality of per-metric ranked mentor lists for each of the one or more mentees in the mentee pool. The tool determines a per-mentee fused rank list for each of the one or more mentees in the mentee pool. The tool determines, based, at least in part, on the per-mentee fused rank list for each of the one or more mentees in the mentee pool, one or more cross-organizational mentorship assignments. The tool establishes, based, at least in part, on the one or more cross-organizational mentorship assignments, at least one mentor-mentee relationship for each of the one or more mentees in the mentee pool.
摘要翻译：用于计算生成组织辅导关系的工具。该工具至少部分地基于一般池中的每个人的每人的域度量数据来确定指导者池和指导者池。该工具为指示者池中的一个或多个被指导者中的每一个确定多个每度量排序的导师列表。该工具确定指定人员池中的每一名受监督者的受监督者融合排名。该工具至少部分地基于指导者池中的一个或多个被指导者的受监督者融合等级名单，一个或多个跨组织指导作业。该工具至少部分地基于一个或多个跨组织指导作业，至少有一名指导者关系，指导者池中的一名或多名参与者中的每一名。

6. 发明申请

US20140222488A1 Quantifying the Quality of Trend Lines 有权
标题翻译：量化趋势线的质量
公开(公告)号：US20140222488A1
公开(公告)日：2014-08-07
申请号：US13761639
申请日：2013-02-07
申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION
发明人： Carl Engel , Wesley M. Gifford , Craig A. Rahenkamp , Krishna Ratakonda , Nianjun Zhou
IPC分类号： G06Q10/06
CPC分类号： G06Q10/06313 , G06Q10/0631
摘要： Techniques, apparatus and articles of manufacture for quantifying the quality of trend lines. A method includes defining a collection of reference data sets derived from multiple historical projects in a similar subject area as the target project, defining one or more indicators of data set applicability to a target project estimation, said defining based on multiple characteristics of the target project, evaluating applicability of each of the reference data sets based on the one or more indicators, and identifying one or more of the reference data sets as applicable for use in the target project estimation.
摘要翻译：用于量化趋势线质量的技术，设备和制品。一种方法包括定义从作为目标项目的类似主题区域中的多个历史项目导出的参考数据集合的集合，将针对目标项目估计的数据集适用性的一个或多个指标定义为基于目标项目的多个特征基于所述一个或多个指标来评估每个所述参考数据集的适用性，以及识别适用于所述目标项目估计中的一个或多个所述参考数据集。

7. 发明公开

US20240211835A1 Automatic and Dynamic Adaptation of Hierarchical Reconciliation for Time Series Forecasting 审中-公开
公开(公告)号：US20240211835A1
公开(公告)日：2024-06-27
申请号：US18089030
申请日：2022-12-27
申请人： International Business Machines Corporation
发明人： Anna Yanchenko , Wesley M. Gifford , Brian Leo Quanz , Nam H. Nguyen , Pavithra Harsha
IPC分类号： G06Q10/0631
CPC分类号： G06Q10/06313
摘要： Mechanisms are provided for performing automated and dynamic reconciliation of forecasts for hierarchical datasets. A machine learning training is executed on a dynamic reconciliation computer model engine to train the dynamic reconciliation computer model engine, based on historical data and forecast data, to learn an association of reconciliation computer models with structural changes in a hierarchical dataset. Runtime forecast data is generated based on a runtime hierarchical dataset, and the trained dynamic reconciliation computer model engine is executed on the runtime forecast data to reconcile the runtime forecast data across a hierarchy of the runtime forecast data. The trained dynamic reconciliation computer model applies different reconciliation computer models to the runtime forecast data based on structural changes in the runtime forecast data. Reconciled runtime forecast data is generated based on results of executing the trained dynamic reconciliation computer model engine on the runtime forecast data, which is then output.

8. 发明授权

US11593680B2 Predictive models having decomposable hierarchical layers configured to generate interpretable results 有权
公开(公告)号：US11593680B2
公开(公告)日：2023-02-28
申请号：US16928171
申请日：2020-07-14
申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION
发明人： Nianjun Zhou , Wesley M. Gifford , Ta-Hsin Li , Pietro Mazzoleni , Pavankumar Murali
IPC分类号： G06Q30/00 , G06N5/04 , G06N20/00
摘要： A computer-implemented method for providing interpretable predictions from a machine learning model includes receiving a data structure that represents a hierarchical structure of a set of features (X) used by one or more predictive models to generate a set of predictions (Y). An interpretability model is built corresponding to the predictive models, by assigning an interpretability to each prediction Yi based on the hierarchical structure. Assigning the interpretability includes decomposing X into a plurality of partitions Xj using the hierarchical structure, wherein X=U1NXj, N being the number of partitions. Further, each partition is decomposed into a plurality of sub-partitions using the hierarchical structure until atomic sub-partitions are obtained. A score is computed for each partition as a function of the predicted scores of the sub-partitions, wherein the predicted scores represent interactions between the sub-partitions. Further, an interpretation of a prediction is outputted.

9. 发明申请

US20140236666A1 ESTIMATING, LEARNING, AND ENHANCING PROJECT RISK 审中-公开
标题翻译：估算，学习和提高项目风险
公开(公告)号：US20140236666A1
公开(公告)日：2014-08-21
申请号：US13770654
申请日：2013-02-19
申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION
发明人： Wesley M. Gifford , Anshul Sheopuri , Rose M. Williams
IPC分类号： G06Q10/06
CPC分类号： G06Q10/0635
摘要： A method for ranking a plurality of objects includes obtaining an initial set of data relating to the objects, generating an initial set of estimates based on the initial set of data, wherein the initial set of estimates includes, for each of the objects, an initial estimated change in performance and an initial estimated likelihood of decline in the performance, incrementally and dynamically refining the initial set of estimates in accordance with a new set of data from new data sources and relating to the objects to produce a refined set of estimates, wherein the refined set of estimates includes, for each of the objects, a refined estimated change in performance and a refined estimated likelihood of decline in the performance, without modifying or replacing a system used to generate the initial set of estimates, and generating a list that ranks the objects according to the refined set of estimates.
摘要翻译：用于对多个对象进行排序的方法包括：获得与该对象有关的初始数据集合，基于该初始数据集生成一组初始估计值，其中该初始估计集包括对于每个对象，初始值估计性能变化和初始估计的性能下降的可能性，根据来自新数据源的新的数据集并且与对象相关以产生精确的估计集合来逐渐地和动态地改进初始估计集合，其中精确的估计集合包括对于每个对象，精简的性能估计改变和精简的估计的性能下降的可能性，而不修改或替换用于生成初始估计集合的系统，并且生成列表根据精确的估计数排列对象。

10. 发明公开

US20240045926A1 HIERARCHICAL OPTIMIZATION OF TIME-SERIES FORECASTING MODEL 审中-公开
公开(公告)号：US20240045926A1
公开(公告)日：2024-02-08
申请号：US17879538
申请日：2022-08-02
申请人： International Business Machines Corporation
发明人： Arindam Jati , Vijay Ekambaram , Sumanta Mukherjee , Brian Leo Quanz , Wesley M. Gifford , Pavithra Harsha
IPC分类号： G06K9/62 , G06N20/00
CPC分类号： G06K9/6256 , G06K9/6219 , G06N20/00
摘要： An example operation may include one or more of storing a hierarchical time-series data set in memory, initially training a first time-series forecasting model based on a lower level of time-series data in the hierarchical data set, training a second time-series teaching forecasting model based on an upper level of time-series data from the hierarchical data set which includes an additional level of aggregation with respect to the lower level of time-series data, optimizing one or more parameters of the initially trained first time-series forecasting model based on predicted outputs from the trained second time-series forecasting model in comparison to predicted outputs from the initially trained first time-series forecasting model, and storing the modified first time-series forecasting model in the memory.

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式