Dhavalkumar A Patel

US Patent:

20220327058, Oct 13, 2022

Filed:

Mar 15, 2022

Appl. No.:

17/654965

Inventors:

- Armonk NY, US
Bei CHEN - Blanchardstown, IE
Peter Daniel KIRCHNER - Putnam Valley NY, US
Syed Yousaf SHAH - Yorktown Heights NY, US
Dhavalkumar C. PATEL - White Plains NY, US
Si Er HAN - Xi'an, CN
Ji Hui YANG - Beijing, CN
Jun WANG - Yan Ta Zone, CN
Jing James XU - Xi'an, CN
Gregory BRAMBLE - Larchmont NY, US
Horst Cornelius SAMULOWITZ - Armonk NY, US
Saket K. SATHE - Mohegan Lake NY, US
Wesley M. GIFFORD - Ridgefield CT, US
Petros ZERFOS - New York City NY, US

Assignee:

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY

International Classification:

G06F 12/0871
G06N 20/00

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.

US Patent:

20220261598, Aug 18, 2022

Filed:

Oct 26, 2021

Appl. No.:

17/452287

Inventors:

- Armonk NY, US
Long VU - Chappaqua NY, US
Dhavalkumar C. PATEL - White Plains NY, US
Syed Yousaf SHAH - Yorktown Heights NY, US
Gregory BRAMBLE - Larchmont NY, US
Peter Daniel KIRCHNER - Putnam Valley NY, US
Horst Cornelius SAMULOWITZ - Armonk NY, US
Petros ZERFOS - New York City NY, US

Assignee:

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY

International Classification:

G06K 9/62
G06N 20/00

Abstract:

To rank time series forecasting in machine learning pipelines, time series data may be incrementally allocated from a time series data set for testing by candidate machine learning pipelines based on seasonality or a degree of temporal dependence of the time series data. Intermediate evaluation scores may be provided by each of the candidate machine learning pipelines following each time series data allocation. One or more machine learning pipelines may be automatically selected from a ranked list of the one or more candidate machine learning pipelines based on a projected learning curve generated from the intermediate evaluation scores.

US Patent:

20220188775, Jun 16, 2022

Filed:

Dec 15, 2020

Appl. No.:

17/123088

Inventors:

- ARMONK NY, US
Dhavalkumar C. Patel - White Plains NY, US
Anuradha Bhamidipaty - Yorktown Heights NY, US

International Classification:

G06Q 10/00
G06K 9/62
G06F 16/9035
G06Q 50/06

Abstract:

A computer implemented federated learning method of predicting failure of assets includes generating a local model at a local site for each of the cohorts and training the local model on local data for each of the cohorts for each failure type. The local model is shared with a central database. A global model is created based on an aggregation of a plurality of the local models from a plurality of the local sites. At each of the plurality of local sites, one of the global model and the local model is chosen for each of the cohorts. The chosen model operates on local data to predict failure of the assets. The utilized features include partitioning features of the assets into static features, semi-static features, and dynamic features, and forming cohorts of the assets based on the static features and the semi-static features.

US Patent:

20230048378, Feb 16, 2023

Filed:

Aug 12, 2021

Appl. No.:

17/400276

Inventors:

- Armonk NY, US
Nianjun Zhou - Chappaqua NY, US
Anuradha Bhamidipaty - Yorktown Heights NY, US
Dhavalkumar C. Patel - White Plains NY, US
Arun Kwangil Iyengar - Yorktown Heights NY, US
Shrey Shrivastava - White Plains NY, US

International Classification:

G06Q 30/00
G06Q 10/02
G06K 9/62
G06N 20/00

Abstract:

Methods and systems to provide a form of probabilistic labeling to associate an outage with a disturbance, which could itself be either known based on the available data or unknown. In the latter case, labeling is especially challenging, as it necessitates the discovery of the disturbance. One approach incorporates a statistical change-point analysis to time-series events that correspond to service tickets in the relevant geographic sub-regions. The method is calibrated to separate the regular periods from the environmental disturbance periods, under the assumption that disturbances significantly increase the rate of loss-causing events. To obtain the probability that a given loss-causing event is related to an environmental disturbance, the method leverages the difference between the rate of events expected in the absence of any disturbances (baseline) and the rate of actually observed events. In the analysis, the local disturbances are identified and estimators of their duration and magnitude are provided.

US Patent:

20210357781, Nov 18, 2021

Filed:

May 15, 2020

Appl. No.:

16/875533

Inventors:

- Armonk NY, US
Dhavalkumar C. PATEL - White Plains NY, US

International Classification:

G06N 5/04
G06F 17/17

Abstract:

A processing system, a computer program product, and a method for efficiently determining a best imputation algorithm from a plurality of imputation algorithms A method includes: providing a plurality of imputation algorithms; providing a time parameter tmax to limit an amount of time spent determining a best imputation algorithm; maintaining past information i on accuracy and execution time for at least one of the imputation algorithms; using said information i to compute a utility score for each of the at least one the imputation algorithms; and testing imputation algorithms and associated parameters in an order based on said utility scores.

US Patent:

20210357794, Nov 18, 2021

Filed:

May 15, 2020

Appl. No.:

16/875450

Inventors:

- Armonk NY, US
Dhavalkumar C. PATEL - White Plains NY, US

International Classification:

G06N 7/00
G06F 16/906
G06F 17/18
G06F 17/17

Abstract:

A processing system, a computer program product, and a method for determining a best imputation algorithm from a plurality of imputation algorithms A method includes: providing a plurality of imputation algorithms; defining a data analytics task in which at least one step of the data analytics task includes determining at least one missing data value by imputation; executing the data analytics task multiple times wherein each execution of the data analytics task uses a data imputation algorithm of the plurality of data imputation algorithms to determine at least one missing data value; determining an error for each execution of the data analytics task; and selecting an imputation algorithm which results in a least error for the data analytics task.

US Patent:

20210326334, Oct 21, 2021

Filed:

Oct 20, 2020

Appl. No.:

17/075617

Inventors:

- Armonk NY, US
Anuradha Bhamidipaty - Yorktown Heights NY, US
Dhavalkumar C. Patel - White Plains NY, US

International Classification:

G06F 16/23

Abstract:

A computing device, method, and system are provided of improving data quality to conserve computational resources. The computing device receives a raw dataset. One or more data quality metric goals corresponding to the received raw dataset are received. A schema of the dataset is determined. An initial set of validation nodes is identified based on the schema of the dataset. The initial set of validation nodes are executed. A next set of validation nodes are iteratively expanded and executed based on the schema of the dataset until a termination criterion is reached. A corrected dataset of the raw dataset is provided based on the iterative execution of the initial and next set of validation nodes.