18297481. TIMED PARTIAL ORDER IDENTIFICATION FOR TASK LEARNING FROM DATA simplified abstract (TOYOTA JIDOSHA KABUSHIKI KAISHA)
Contents
TIMED PARTIAL ORDER IDENTIFICATION FOR TASK LEARNING FROM DATA
Organization Name
TOYOTA JIDOSHA KABUSHIKI KAISHA
Inventor(s)
Kandai Watanabe of Boulder CO (US)
Georgios Fainekos of Novi MI (US)
Tomoya Yamaguchi of Plano TX (US)
Danil Prokhorov of Canton MI (US)
TIMED PARTIAL ORDER IDENTIFICATION FOR TASK LEARNING FROM DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 18297481 titled 'TIMED PARTIAL ORDER IDENTIFICATION FOR TASK LEARNING FROM DATA
The system described in the abstract is designed to work with multiple timed traces to complete a task efficiently.
- Data receiver to collect timed traces
- Memory with stored instructions
- Processor to execute instructions
- Store timed traces in memory
- Generate partial order graph of time constraints
- Create transitive reduced partial order graph
- Develop timed partial order graph with minimal clocks needed
Potential Applications: - Task scheduling in complex systems - Process optimization in manufacturing - Resource allocation in project management
Problems Solved: - Efficient handling of multiple timed traces - Reduction of redundant time constraints - Minimization of clocks required for task completion
Benefits: - Improved task efficiency - Enhanced system performance - Simplified time constraint management
Commercial Applications: Title: "Advanced Task Scheduling System for Enhanced Efficiency" This technology can be utilized in industries such as: - Logistics and supply chain management - Telecommunications for network optimization - Automotive manufacturing for production line efficiency
Questions about the technology: 1. How does this system handle the complexity of multiple timed traces efficiently? 2. What are the key advantages of using a transitive reduced partial order graph in this context?
Frequently Updated Research: Stay updated on the latest advancements in task scheduling algorithms and optimization techniques to enhance the performance of this system.
Original Abstract Submitted
A system is provided for use with a plurality of timed traces for performing a task. The system includes: a data receiver configured to receive the plurality of timed traces; a memory having instructions stored therein; and a processor configured to execute the instructions stored in the memory to cause the system to: store the received plurality of timed traces into the memory; generate a partial order graph of time constraints between all of the plurality of timed traces; generate a transitive reduced partial order graph from the partial order graph, the transitive reduced partial order graph not including redundant time constraints within the partial order graph; and generate a timed partial order graph from the transitive reduced partial order graph, the timed partial order graph having a minimum number of clocks required to explain the time constraints between all of the plurality of timed traces.
