Method and system for predicting task completion of a time period based on task completion rates and data trend of prior time periods in view of attributes of tasks using machine learning models
US10846643B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 29, 2018 |
| Grant date | Nov 24, 2020 |
| Priority date | — |
| Expiry date | Oct 30, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q10/0639
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A request is received for determining a task completion rate of each of a first set of tasks associated with a set of task attributes. The first set of tasks are scheduled to be completed within a first timer period. An MAPE score is calculated or obtained for each of the completion rate predictive models, which is determined based on prior predictions performed in a second time period in the past. The duration of the second time period is a multiple of the first time period. One of the predictive models is selected based on the MAPE scores of the predictive models, where the selected model has the lowest MAPE score amongst the predictive models in the set. In another embodiment, a predictive model is selected further based on the volatility scores of the predictive models. A model with a combination of lowest MAPE score and volatility score is selected.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.