Systems and methods of using an artificially intelligent database management system and interfaces for mobile, embedded, and other computing devices
First Claim
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, orreceives 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.
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Accused Products
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.
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Citations
20 Claims
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1. An artificially intelligent database management system (DBMS) comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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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:
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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.
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20. A method comprising:
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(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.
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Specification