Systems and methods of using an artificially intelligent database management system and interfaces for mobile, embedded, and other computing devices

• US 10,353,901 B2
• Filed: 04/30/2016
• Issued: 07/16/2019
• Est. Priority Date: 08/08/2013
• Status: Active Grant

• Current Assignee: Jasmin Cosic
• Original Assignee: Jasmin Cosic
• Inventors: Cosic, Jasmin
• Primary Examiner(s): Wong, Lut
• Application Number:
  US15/143,580
• Publication Number:
  US 20160246819A1
• Time in Patent Office: 1,172 Days
• Field of Search: 706 46, 707765
• CPC Class Codes: G06F 16/21 Design, administration or maintenance of databases
• CPC Class Codes: G06F 16/2379 Updates performed during online database operations; commit processing
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/252 between a Database Management System and a front-end application
• CPC Class Codes: G06F 16/284 Relational databases
• CPC Class Codes: G06F 16/8358 Query translation
• CPC Class Codes: G06F 3/0481 based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• CPC Class Codes: G06F 3/04842 Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06F 3/167 Audio in a user interface, e.g. using voice commands for navigating, audio feedback
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G10L 15/26 Speech to text systems G10L15/08 takes precedence
• CPC Class Codes: G10L 21/10 Transforming into visible information

Abstract

The current disclosure generally relates to database management systems (DBMSs) and may be generally directed to methods and systems of using artificial intelligence (i.e. machine learning and/or anticipation functionalities, etc.) to learn a user'"'"'s use of a DBMS, store this “knowledge” in a knowledgebase, and anticipate the user'"'"'s future operating intentions. The current disclosure may also be generally directed to associative methods and systems of constructing DBMS commands. The current disclosure may also be generally directed to methods and systems of using a simplified DBMS command language (SDCL) for associative DBMS command construction. The current disclosure may also be generally directed to artificially intelligent methods and systems for associative DBMS command construction. The current disclosure may also be generally directed to methods and systems for associative DBMS command construction through voice input. Other methods, systems, features, elements and/or their embodiments are also disclosed.

Claims

1. An artificially intelligent database management system (DBMS) comprising:

one or more processor circuits;

a DBMS;

a memory that stores a first knowledge cell that includes a plurality of instruction sets for performing one or more operations on the DBMS and a second knowledge cell that includes a plurality of instruction sets for performing one or more operations on the DBMS, wherein the first knowledge cell is learned in a first learning process and the second knowledge cell is learned in a second learning process; and

a DBMS artificial intelligence unit that;

accesses the first knowledge cell and the second knowledge cell in the memory;

determines the plurality of instruction sets of the second knowledge cell based on at least partial match between at least one portion of a new one or more instruction sets and at least one portion of a subset of the plurality of instruction sets of the second knowledge cell, or

receives a selection of the plurality of instruction sets of the second knowledge cell; and

causes an execution of the plurality of instruction sets of the second knowledge cell at least in response to the determines or the receives of the DBMS artificial intelligence unit, wherein the execution of the plurality of instruction sets of the second knowledge cell is performed in a temporal order in which the plurality of instruction sets of the second knowledge cell are learned in the second learning process, and wherein the DBMS or another DBMS performs the one or more operations defined by the plurality of instruction sets of the second knowledge cell.

2. The system of claim 1, wherein the second knowledge cell includes an instruction set for creating an element of the DBMS, and wherein the element of the DBMS is not:

a query, a portion of a query, an instruction, a portion of an instruction, an instruction set, or a portion of an instruction set.

3. The system of claim 1, wherein the DBMS artificial intelligence unit determines the plurality of instruction sets of the second knowledge cell based on the at least partial match between the at least one portion of the new one or more instruction sets and the at least one portion of the subset of the plurality of instruction sets of the second knowledge cell, and wherein the determines the plurality of instruction sets of the second knowledge cell based on the at least partial match between the at least one portion of the new one or more instruction sets and the at least one portion of the subset of the plurality of instruction sets of the second knowledge cell includes:

determining that a number of at least partially matching portions of the new one or more instruction sets and portions of the subset of the plurality of instruction sets of the second knowledge cell exceeds a threshold number, or

determining that a percentage of at least partially matching portions of the new one or more instruction sets and portions of the subset of the plurality of instruction sets of the second knowledge cell exceeds a threshold percentage.

4. The system of claim 1, wherein the second knowledge cell includes an instruction set for deleting an element of the DBMS, and wherein the element of the DBMS is not:

a query, a portion of a query, an instruction, a portion of an instruction, an instruction set, or a portion of an instruction set.

5. The system of claim 1, wherein the DBMS includes an Oracle DBMS, a Microsoft SQL Server DBMS, a MySQL DBMS, an IBM DB2 DBMS, a relational DBMS, an application that uses the DBMS, or an interface for the DBMS, and wherein the another DBMS includes an Oracle DBMS, a Microsoft SQL Server DBMS, a MySQL DBMS, an IBM DB2 DBMS, a relational DBMS, an application that uses the another DBMS, or an interface for the another DBMS.

6. The system of claim 1, wherein the first learning process and the second learning process use a same learning algorithm.

7. The system of claim 1, wherein the DBMS artificial intelligence unit further:

alters at least one instruction set of the second knowledge cell or a copy of the at least one instruction set of the second knowledge cell based on a pattern recognized from at least one portion of the new one or more instruction sets.

8. The system of claim 1, wherein the DBMS artificial intelligence unit further:

alters at least one instruction set of the second knowledge cell or a copy of the at least one instruction set of the second knowledge cell in response to receiving a user selection to modify the at least one instruction set of the second knowledge cell or the copy of the at least one instruction set of the second knowledge cell.

9. The system of claim 1, wherein the DBMS artificial intelligence unit determines the plurality of instruction sets of the second knowledge cell based on the at least partial match between the at least one portion of the new one or more instruction sets and the at least one portion of the subset of the plurality of instruction sets of the second knowledge cell, and wherein the causes the execution of the plurality of instruction sets of the second knowledge cell includes causing the execution of the plurality of instruction sets of the second knowledge cell with no user input.

10. The system of claim 1, wherein the first learning process includes receiving at least one instruction set of the first knowledge cell from a first application that uses the DBMS, and wherein the second learning process includes receiving at least one instruction set of the second knowledge cell from a second application that uses the DBMS.

11. The system of claim 1, wherein the DBMS includes at least one of:

a hardware element that is included in the one or more processor circuits, a hardware element that is included in another one or more processor circuits, an application operating on the one or more processor circuits, an application operating on another one or more processor circuits, or an element coupled to the one or more processor circuits, and wherein the DBMS artificial intelligence unit includes at least one of;

a hardware element that is included in the one or more processor circuits, a hardware element that is included in another one or more processor circuits, an application operating on the one or more processor circuits, an application operating on another one or more processor circuits, or an element coupled to the one or more processor circuits.

12. The system of claim 1, wherein the DBMS artificial intelligence unit determines the plurality of instruction sets of the second knowledge cell based on the at least partial match between the at least one portion of the new one or more instruction sets and the at least one portion of the subset of the plurality of instruction sets of the second knowledge cell, and wherein the determines the plurality of instruction sets of the second knowledge cell based on the at least partial match between the at least one portion of the new one or more instruction sets and the at least one portion of the subset of the plurality of instruction sets of the second knowledge cell includes determining the plurality of instruction sets of the second knowledge cell based on at least partial match between at least one portion of two or more instruction sets of the new one or more instruction sets and at least one portion of the subset of the plurality of instruction sets of the second knowledge cell.

13. The system of claim 1, wherein the causes the execution of the plurality of instruction sets of the second knowledge cell is performed at least in response to receiving a user confirmation of the execution of the plurality of instruction sets of the second knowledge cell.

14. The system of claim 1, wherein the DBMS artificial intelligence unit receives the selection of the plurality of instruction sets of the second knowledge cell, and wherein the receives the selection of the plurality of instruction sets of the second knowledge cell includes receiving a user selection of the plurality of instruction sets of the second knowledge cell.

15. The system of claim 1, wherein the DBMS is not a search engine, and wherein the another DBMS is not a search engine, and wherein at least one instruction set of the second knowledge cell is not a search query, and wherein at least one instruction set of the second knowledge cell is not a select Structured Query Language (SQL) query.

16. The system of claim 1, wherein the subset of the plurality of instruction sets of the second knowledge cell includes one or more instruction sets of the second knowledge cell.

17. The system of claim 1, wherein the execution of the plurality of instruction sets of the second knowledge cell is performed by at least one from a list of:

the one or more processor circuits, another one or more processor circuits, the DBMS, and the another DBMS.

18. The system of claim 1, wherein the first knowledge cell is a data structure for storing the plurality of instruction sets of the first knowledge cell, and wherein the second knowledge cell is a data structure for storing the plurality of instruction sets of the second knowledge cell.

19. A non-transitory machine readable medium having a program stored thereon that when executed by one or more processor circuits causes the one or more processor circuits to perform operations comprising:

accessing a memory that stores a first knowledge cell that includes a plurality of instruction sets for performing one or more operations on a DBMS and a second knowledge cell that includes a plurality of instruction sets for performing one or more operations on the DBMS, wherein the first knowledge cell is learned in a first learning process and the second knowledge cell is learned in a second learning process;

determining the plurality of instruction sets of the second knowledge cell based on at least partial match between at least one portion of a new one or more instruction sets and at least one portion of a subset of the plurality of instruction sets of the second knowledge cell, or

receiving a selection of the plurality of instruction sets of the second knowledge cell; and

causing an execution of the plurality of instruction sets of the second knowledge cell at least in response to the determining or the receiving, wherein the execution of the plurality of instruction sets of the second knowledge cell is performed in a temporal order in which the plurality of instruction sets of the second knowledge cell are learned in the second learning process, and wherein the DBMS or another DBMS performs the one or more operations defined by the plurality of instruction sets of the second knowledge cell.

20. A method comprising:

(a) accessing, by one or more processor circuits, a memory that stores a first knowledge cell that includes a plurality of instruction sets for performing one or more operations on a DBMS and a second knowledge cell that includes a plurality of instruction sets for performing one or more operations on the DBMS, wherein the first knowledge cell is learned in a first learning process and the second knowledge cell is learned in a second learning process;

(b1) determining, by the one or more processor circuits, the plurality of instruction sets of the second knowledge cell based on at least partial match between at least one portion of a new one or more instruction sets and at least one portion of a subset of the plurality of instruction sets of the second knowledge cell, or

(b2) receiving, by the one or more processor circuits, a selection of the plurality of instruction sets of the second knowledge cell;

(c) executing the plurality of instruction sets of the second knowledge cell at least in response to the determining of (b1) or the receiving of (b2), wherein the executing the plurality of instruction sets of the second knowledge cell is performed in a temporal order in which the plurality of instruction sets of the second knowledge cell are learned in the second learning process, and

(d) performing, by the DBMS or by another DBMS, the one or more operations defined by the plurality of instruction sets of the second knowledge cell.