Techniques for processing queries relating to task-completion times or cross-data-structure interactions
US9678794B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 1, 2016 |
| Grant date | Jun 13, 2017 |
| Priority date | — |
| Expiry date | Dec 1, 2036 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG16H50/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and systems disclosed herein relate generally to data processing by applying machine learning techniques to iteration data to identify anomaly subsets of iteration data. More specifically, iteration data for individual iterations of a workflow involving a set of tasks may contain a client data set, client-associated sparse indicators and their classifications, and a set of processing times for the set of tasks performed in that iteration of the workflow. These individual iterations of the workflow may also be associated with particular data sources. Using the iteration data, anomaly subsets within the iteration data can be identified, such as data items resulting from systematic error associated with particular data sources, sets of sparse indicators to be validated or double-checked, or tasks that are associated with long processing times. The anomaly subsets can be provided in a generated communication or report in order to optimize future iterations of the workflow.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.