IP Library Granted Patent US 9,536,214
Granted Patent B2
US 9,536,214 · App. 15/075,603 · Granted Jan 3, 2017

Weather-driven multi-category infrastructure impact forecasting

Inventors: Fook-Luen Heng (Yorktown Heights, NY); Zhiguo Li (Yorktown Heights, NY); Stuart A. Siegel (Milburn, NJ); Amith Singhee (Bangalore, IN); Haijing Wang (Valhalla, NY)
Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
G06Q10/06315G06N5/04G06N99/005
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Quick Facts
Patent No.
US 9,536,214
App. No.
15/075,603
Granted
Jan 3, 2017
Kind
B2
Abstract

A method, system, and computer program product for resource management are described. The method includes selecting trouble regions within the service area, generating clustered regions, and training a trouble forecast model for the trouble regions for each type of damage, the training for each trouble region using training data from every trouble region within the clustered region associated with the trouble region. The method also includes applying the trouble forecast model for each trouble region within the service area for each type of damage, determining a trouble forecast for the service area for each type of damage based on the trouble forecast for each of the trouble regions within the service area, and determining a job forecast for the service area based on the trouble forecast for the service area, wherein the managing resources is based on the job forecast for the service area.

Claims (7)

1. A computer implemented method of managing resources based on weather-related damage in a service area, the method comprising:

selecting, by a processor, trouble regions within the service area;

generating clustered regions, each of the clustered regions including at least one of the trouble regions within the service area and each of the trouble regions within the service area being associated with one of the clustered regions;

training a trouble forecast model for each of the trouble regions for each type of the weather-related damage, the training for each of the trouble regions using training data from every one of the trouble regions within the clustered region associated with the trouble region, wherein the training data from each of the trouble regions within the clustered region is multiplied by a different scaling factor;

applying the trouble forecast model for each of the trouble regions within the service area for each type of the weather-related damage to obtain a trouble forecast for each of the trouble regions within the service area for each type of the weather-related damage;

determining a trouble forecast for the service area for each type of the weather-related damage based on the trouble forecast for each of the trouble regions within the service area; and

determining a job forecast for the service area based on the trouble forecast for the service area according to a trouble-to-job mapping, wherein the managing resources is based on the job forecast for the service area, the applying the trouble forecast model for each of the trouble regions includes spatially interpolating weather forecast information within the respective trouble region to a centroid of the respective trouble region to obtain interpolated data and computing scoring input features from the interpolated data that are used to determine the trouble forecast for the respective trouble region.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 6, 2017
From: INTERNATIONAL BUSINESS MACHINES CORPORATION
To: UTOPUS INSIGHTS, INC.
Reel/Frame 042700/0530 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 21, 2016
From: HENG, FOOK-LUEN; LI, ZHIGUO; SIEGEL, STUART A.; SINGHEE, AMITH; WANG, HAIJING
To: INTERNATIONAL BUSINESS MACHINES CORPORATION
Reel/Frame 038049/0344 →
Continuity (3)
Continuation 15002494 · Jan 21, 2016
Provisional Application 62147003 · Apr 14, 2015
Related Publication 20160307138A1 · Oct 20, 2016