US 12,586,015 B2Granted

Resource-related forecasting using machine learning techniques

AssigneeDell Products L.P.

InventorsAli Mehrnezhad, Siamak Saliminejad, Prateek Srivastava, Saurabh Guleria, Akshit Sharma, Akhil Koppera

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Abstract

Methods, apparatus, and processor-readable storage media for resource-related forecasting using machine learning techniques are provided herein. An example computer-implemented method includes obtaining multiple items of data related to one or more resources associated with an enterprise; correlating at least a portion of the multiple items of data with at least one target variable using one or more correlation techniques; generating one or more forecasts pertaining to the at least one target variable and at least a portion of the one or more resources by processing at least a portion of the correlated data using one or more machine learning techniques; and performing one or more automated actions based at least in part on the one or more forecasts.

Claims20

Dependent claims

A computer-implemented method comprising:

obtaining multiple items of data related to one or more resources associated with an enterprise;
processing at least a plurality of the multiple items of data into at least one structured query language server;
correlating at least a portion of the multiple items of data within the at least one structured query language server with at least one target variable using one or more correlation techniques, wherein correlating the at least a portion of the multiple items of data with at least one target variable comprises calculating at least one rank correlation between temporally based differenced values derived from the multiple items of data and the at least one target variable;
automatically training at least one random forest model and at least one linear regression model based at least in part on the correlated portion of the multiple items of data associated with correlation values above at least one designated threshold, wherein automatically training the at least one random forest model comprises automatically learning model parameters comprising at least one number of samples in at least one node of at least one decision tree, depth of the at least one decision tree, at least one number of random features to associate with the at least one node, and at least one number of trees to be built in one or more random forests;
generating one or more forecasts pertaining to the at least one target variable and at least a portion of the one or more resources by processing input data related to the at least a portion of the one or more resources using the at least one trained random forest model and the at least one trained linear regression model;
and
performing one or more automated actions based at least in part on the one or more forecasts, wherein performing the one or more automated actions comprises automatically deploying, in accordance with the one or more forecasts, at least a portion of the one or more resources, wherein the at least a portion of the one or more resources comprises one or more hardware devices;
wherein the method is performed by at least one processing device comprising a processor coupled to a memory.

5 more elements

A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device:

to obtain multiple items of data related to one or more resources associated with an enterprise;
to process at least a plurality of the multiple items of data into at least one structured query language server;
to correlate at least a portion of the multiple items of data within the at least one structured query language server with at least one target variable using one or more correlation techniques, wherein correlating the at least a portion of the multiple items of data with at least one target variable comprises calculating at least one rank correlation between temporally based differenced values derived from the multiple items of data and the at least one target variable;
to automatically train at least one random forest model and at least one linear regression model based at least in part on the correlated portion of the multiple items of data associated with correlation values above at least one designated threshold, wherein automatically training the at least one random forest model comprises automatically learning model parameters comprising at least one number of samples in at least one node of at least one decision tree, depth of the at least one decision tree, at least one number of random features to associate with the at least one node, and at least one number of trees to be built in one or more random forests;
to generate one or more forecasts pertaining to the at least one target variable and at least a portion of the one or more resources by processing input data related to the at least a portion of the one or more resources using the at least one trained random forest model and the at least one trained linear regression model;
and
to perform one or more automated actions based at least in part on the one or more forecasts, wherein performing the one or more automated actions comprises automatically deploying, in accordance with the one or more forecasts, at least a portion of the one or more resources, wherein the at least a portion of the one or more resources comprises one or more hardware devices.

4 more elements

An apparatus comprising:

at least one processing device comprising a processor coupled to a memory;
the at least one processing device being configured:
to obtain multiple items of data related to one or more resources associated with an enterprise;
to process at least a plurality of the multiple items of data into at least one structured query language server;
to correlate at least a portion of the multiple items of data within the at least one structured query language server with at least one target variable using one or more correlation techniques, wherein correlating the at least a portion of the multiple items of data with at least one target variable comprises calculating at least one rank correlation between temporally based differenced values derived from the multiple items of data and the at least one target variable;
to automatically train at least one random forest model and at least one linear regression model based at least in part on the correlated portion of the multiple items of data associated with correlation values above at least one designated threshold, wherein automatically training the at least one random forest model comprises automatically learning model parameters comprising at least one number of samples in at least one node of at least one decision tree, depth of the at least one decision tree, at least one number of random features to associate with the at least one node, and at least one number of trees to be built in one or more random forests;
to generate one or more forecasts pertaining to the at least one target variable and at least a portion of the one or more resources by processing input data related to the at least a portion of the one or more resources using the at least one trained random forest model and the at least one trained linear regression model;
and
to perform one or more automated actions based at least in part on the one or more forecasts, wherein performing the one or more automated actions comprises automatically deploying, in accordance with the one or more forecasts, at least a portion of the one or more resources, wherein the at least a portion of the one or more resources comprises one or more hardware devices.

6 more elements

Dependencies are inferred from the claim text.

Facts

Application: 18/196,543Filed 12 May 2023
Published: US 2024/0378520 A114 Nov 2024
Granted: 24 Mar 2026
Term ends: Not estimatedThe source records don’t support an estimate.

Classification3 codes

G06Q 10/06313
Resource planning, allocation, distributing or scheduling for enterprises or organisations: resource planning in a project environment
G06Q 10/06315
Resource planning, allocation, distributing or scheduling for enterprises or organisations: needs-based resource requirements planning or analysis
G06Q
Information and communication technology [ict] specially adapted for administrative, commercial, financial, managerial