Distributed activity control systems and methods

Patent metadata

Field	Value
Publication number	US-10565507-B2
Application number	US-201916280451-A
Country	US
Kind code	B2
Filing date	Feb 20, 2019
Priority date	Sep 18, 2014
Publication date	Feb 18, 2020
Grant date	Feb 18, 2020

Abstract

A dynamic, distributed directed activity network comprising a directed activity control program specifying tasks to be executed including required individual task inputs and outputs, the required order of task execution, and permitted parallelism in task execution; a plurality of task execution agents, individual of said agents having a set of dynamically changing agent attributes and capable of executing different required tasks in said activity control; a plurality of task execution controllers, each controller associated with one or more of the task execution agents with access to dynamically changing agent attributes; a directed activity controller for communicating with said task execution controllers for directing execution of said activity control program; a communications network capable of supporting communication between said directed activity controller and task execution controllers; and wherein said directed activity controller and task execution controllers communicate via said communication network to execute said directed activity control program using selected task execution agents.

First claim

The embodiments of the invention in which an exclusive property or privilege is claimed are defined as follows:

1. A distributed directed activity control method comprising a cloud-based data processing and storage system and internet connected computers, data storage, controllers and task execution agents for directed activity control execution with at least one specialized computer machine including electronic artificial intelligence expert system decision making capability and further comprising:
the step of connecting said task execution agents to the internet via task execution agent controllers;
the step of creating graphic representation of multiple interconnected nodes for all or part of said directed activity control method wherein said nodes further comprise function nodes, analytic nodes and/or storage nodes with inputs and outputs;
the step of storing in memory one or more of said directed activity control programs comprising digital workflow representations for control of execution of interrelated tasks including permitted parallelism in task execution;
the step of storing in memory digital model history files comprising potentially dynamically changing task execution agent attributes describing task execution agent characteristic and operational status;
the step of storing in memory digital task input and output object flow representations of said directed activities;
the step of storing in memory artificial intelligence expert system specified workflow propositional logic rules defining task execution agent asset attribute ranges and defined threshold values for triggering activities depending on said task execution agent asset attribute values and said propositional logic rules;
the step of electronically receiving from task execution agents messages providing task execution status, sensor derived information and potentially dynamically changing task execution agent attributes;
the step of updating said digital model history files and statistical analysis of potentially dynamically changing task execution attributes stored in digital model history files based on information in said task execution agent received messages;
the step of digital model artificial intelligence expert system analysis based on said workflow propositional logic rules;
the step of designating a particular internet accessible task execution agent for executing a particular task with said digital model artificial intelligence expert system analysis and load balancing based on said task execution agent availability and utilization;
the step of electronically transmitting by the one or more electronic programmable artificial intelligence control computer machines one or more control messages to at least one selected internet accessible contained distributed task execution agent to direct execution of particular tasks; and
whereby improved resource utilization efficiency is achieved based on the use of artificial intelligence expert systems decision making in the execution of artificial intelligence expert system directed activity control program and sensor derived information.

2. The method of claim 1 wherein the task execution agents may comprise computers, storage units, transportation equipment, manufacturing equipment, factories, assembly lines, personnel and/or communications equipment.

3. The method of claim 1 wherein said task execution agent controllers comprise digital processors with memory and interface port internet connections.

4. The method of claim 1 wherein said task execution agent controllers comprise personal computers.

5. The method of claim 3 wherein said interface port internet connection is a Wi-Fi port connection.

6. The method of claim 3 wherein said interface port internet connection is a Bluetooth port connection.

7. The method of claim 1 wherein said task execution agent controllers comprise cellular telephones.

8. The method of claim 1 wherein said task execution agent controllers comprise radio frequency transceivers.

9. The method of claim 3 wherein said interface port internet connection is a USB port connection.

10. The method of claim 3 wherein said interface port internet connection is a cellular telephone network port connection.

11. The method of claim 3 wherein said interface port internet connection is a data transceiver port connection.

12. The method of claim 1 wherein said digital workflow representations comprise directed activity task workflow definitions with task inputs and outputs and task precedence requirements.

13. The method of claim 1 wherein said digital workflow representations comprise directed activity task workflow definitions with expert defined task workflow requirements.

14. The method of claim 1 wherein said input and output object flow representations comprise directed activity workflow material flows between and among activity tasks.

15. The method of claim 1 wherein said input and output object flow representations comprise directed activity information flows between and among activity tasks.

16. The method of claim 1 wherein said directed activity control artificial intelligence expert system method comprises defined ranges of said potentially dynamically changing task execution agent parameters with expert system decisions based at least in part on parameter membership in said defined ranges.

17. The method of claim 16 wherein said directed activity control artificial intelligence expert system method parameter ranges define boundaries for triggering control actions.

18. The method of claim 1 wherein said task execution agent controllers further comprise agent location sensors.

19. The method of claim 18 wherein said agent location sensor is a GPS sensor.

20. The method of claim 1 wherein distributed task execution agent dynamically variable attributes may comprise product, production process, or performance parameters.

21. The method of claim 1 wherein the time required to execute a particular task with a particular task execution agent may vary depending upon the utilization of the resource.

22. The method of claim 1 wherein the time required to execute a particular task with a particular task execution agent may vary depending upon the availability of the resource.

23. The method of claim 1 wherein the time required to execute a particular task with a particular task execution agent may vary depending upon maintenance issues with the resource.

24. The method of claim 1 wherein said distributed task execution agents comprise factory machinery.

Assignees

• Pedersen Robert D

Inventors

• Pedersen Robert D

Classifications

• G06F9/5027

  the resource being a machine, e.g. CPUs, Servers, Terminals · CPC title

• H04L67/10 Primary

  in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46) · CPC title

• G06F9/4843

  by program, e.g. task dispatcher, supervisor, operating system · CPC title

• G06N5/02 Primary

  Knowledge representation; Symbolic representation · CPC title