Object memory management system

• US 9,342,500 B2
• Filed: 03/15/2011
• Issued: 05/17/2016
• Est. Priority Date: 03/15/2010
• Status: Expired due to Fees

• Current Assignee: Michael V. Liguori
• Original Assignee: Michael V. Liguori
• Inventors: Liguori, Michael V.
• Primary Examiner(s): Badawi, Sherief
• Assistant Examiner(s): Raab, Christopher J
• Application Number:
  US13/048,183
• Publication Number:
  US 20110225204A1
• Time in Patent Office: 1,890 Days
• Field of Search: 707/792, 710/62
• US Class Current: 1/1
• CPC Class Codes: G05B 2219/2613 Household appliance in general
• CPC Class Codes: G06F 3/002 Specific input/output arrangements not covered by G06F3/01 - G06F3/16 other optical apparatus G02B27/00
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G10L 15/1822 Parsing for meaning understanding

Abstract

An object memory management system (OMMS) provides a communications link between a plurality of various types of inputs and a plurality of customers who would normally receive one or more of the various types of inputs. Included among the input signal devices are GPS, infrared (IR), photo cell, range finder, and other type sensors, and signal sources such as data bit stream, pulsed digital signals, verbal and visual signals. Included among the customers are artificial intelligence (AI) and software service providers. The system recognizes a received incoming signal, converts it into a signal the designated customer can recognize and sends it to the designated customer. Signals are processed on a first-in-first-out (FIFO) basis unless a priority designation is received. Complex incoming signals (verbal and visual) require pattern recognition wherein an object database is utilized to understand such incoming signals.

Claims

1. A system for implementing a learning process comprising:

a plurality of sources; and

an operating system processor including instructions for;

receiving input signals from said plurality of sources, said receiving including the learning of the destination, the characteristics, the format and the protocol of each received signal;

recognizing said source of said input signals and the customer of said input signals;

converting said input signals according to object data into a points of interest (POI) set;

creating a customer instruction set recognizable by said recognized customer from said POI set according to object data; and

sending said customer instruction set to destination of said recognized customer;

wherein said input signals from said plurality of sources are used for creating instruction signals recognizable by customers;

wherein said operating system processor is a hardware processor operating under the control of said instructions;

said plurality of sources include individual inputs from textual (OCR) sources and verbal signal sources; and

said input signals of said plurality of sources are received by said operating system processor via local area networks (LANs) or wide area networks (Internet);

wherein said recognizing includes using an object database to detect message characteristics, comparing message characteristics of said input signals to said object data, repeated comparing with object data in said object database until sufficient matching results in deciphering said input signals or object data is recognized not to exist for sufficient matching;

wherein when object data is recognized to not exist, said recognizing further includes adding of object data by successively testing the bounds of message characteristics each time in accordance with service actions within customer requirements by verifying words through a neo-objects external application for new object creation, wherein a new item of object data is learned.

2. The system of claim 1, wherein said comparing includes recognizing patterns in textual and verbal content or other sensor information in said input signals and addressing object data associated therewith.

3. The system of claim 2, wherein said receiving said input signals includes receiving multiple input signals that are processed on a first-in-first-out priority.

4. A system for implementing a learning process comprising:

a plurality of sources; and

an operating system processor including instructions for;

receiving input signals from said plurality of sources, said receiving including the learning of the destination, the characteristics, the format and the protocol of each received signal;

recognizing said source of said input signals and the customer of said input signals;

converting said input signals according to object data from an object database into a points of interest (POI) set;

creating a customer instruction set recognizable by said recognized customer from said POI set according to object data; and

sending said customer instruction set to destination of said recognized customer;

wherein said input signals from said plurality of sources are used for creating instruction signals recognizable by customers;

wherein said operating system processor is a hardware processor operating under the control of said instructions;

wherein said points of interest set is used to derive a focal point and create a plurality of customer instructions sets until released;

wherein said object database having a relationship structure for use by said operating system processor comprising;

object data defines a relationship structure to itself to define how it relates to other object data; and

object data relationships defines an association of use; and

object data characteristics define attributes; and

object data characteristics dictate possible use of customer instructions; and

object data characteristics dictate possible use of information for a piece of data within the customer instruction sets; and

object data defining file usages that are used in conjunction with input sources and customers.

5. The system of claim 4, wherein said focal point handles the adding and releasing of said points of interest set, and allocation of object data to discern a customer action.

6. The system of claim 4, wherein said focal point derives a point of view for calculating the customer instruction sets while using pattern recognition to handle errors received in the input signals.

7. The system of claim 4, wherein said customer instruction set is created through the use of object data while using pattern recognition to handle errors received in the signal from the sensors, recognition devices and services.

8. The system of claim 4, wherein said operating system processor uses said object database relationship structure to calculate a probability of the customer instruction set.

9. The system of claim 4, wherein said operating system processor uses said object database relationship structure to create said customer instruction set by allowing the population of data format for customer instructions.

10. The system of claim 4, wherein said customer instruction set includes structure comprising:

customer instructions defining an intended action; and

customer instructions defining a data format and protocol; and

customer instructions defining attributes that associates object data to the intended action; and

customer instructions containing attributes for the allowed data format of said action; and

customer instructions having data format attributes defining use of object data function to compute said data format of said intended action; and

customer instructions triggering additional customer instructions to be created for one or many actions as dictated by the customer instructions.

11. The system of claim 10, wherein said customer instruction data format is customizable by a user to a desired format for initial implementation and subsequent implementations.

12. The system of claim 10, wherein said action defines a destination to receive the customer instruction set.

13. A system for implementing a learning process in a control system that operates machinery or equipment comprising:

a plurality of sources including communication devices; and

an operating system processor including instructions for;

receiving input signals in the form of voice signals or text messages from said plurality of sources, said receiving including the learning of the destination, the characteristics, the format and the protocol of each received signal;

recognizing said source of said input signals and the customer of said input signals;

converting said input signals according to object data into a points of interest (POI) set;

creating a customer instruction set recognizable by said recognized customer from said POI set according to object data; and

sending said customer instruction set to destination of said recognized customer;

wherein said input signals from said plurality of sources are used for creating instruction signals recognizable by customers to operate said machinery or said equipment;

wherein said operating system processor is a hardware processor operating under the control of said instructions;

said plurality of sources include software operating on cell phones; and

said input signals of said plurality of sources are received by said operating system processor via local area networks (LANs) or wide area networks (Internet);

wherein said recognizing includes using said object database to detect message characteristics, comparing message characteristics of said input signals to said object data, repeated comparing with object data in said object database until sufficient matching results in deciphering said input signals or object data is recognized not to exist for sufficient matching;

wherein when object data is recognized to not exist, said recognizing further includes adding of object data by successively testing the bounds of message characteristics each time in accordance with service actions within customer requirements by verifying words through a neo-objects external application for new object creation, wherein a new item of object data is learned.

14. The system of claim 13, wherein said comparing includes recognizing patterns in textual and verbal content in said input signals and addressing object data associated therewith and said creating includes recognizing a first machine to be operated and the instruction signals necessary to operate said first machine.