NATURAL LANGUAGE EXECUTION SYSTEM, METHOD AND COMPUTER READABLE MEDIUM

• US 20160124937A1
• Filed: 11/02/2015
• Published: 05/05/2016
• Est. Priority Date: 11/03/2014
• Status: Abandoned Application

• Current Assignee: Service Paradigm Pty Ltd
• Original Assignee: Service Paradigm Pty Ltd
• Inventors: Elhaddad, Mike Fathy
• Application Number:
  US14/930,326
• Publication Number:
  US 20160124937A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 40/205 Parsing

Abstract

Disclosed is a method, system, and computer readable medium for natural language execution. The method includes, in a processing system: receiving input data indicative of natural language text; using a natural language processor to generate natural language parse information; generating, using the natural language parse information, an input object composite including objects; determining, for the objects of the input object composite and using an object knowledge network, a plurality of interpretation object composites that represent interpretation functions; executing each interpretation function; determining, for the objects of the input object composite and using the object knowledge network, executable object composites that represent executable functions; executing the executable functions thereby generating an output object composite; updating the object knowledge network based on the input and out object composite and the execution of each interpretation and execution function; and outputting, based on the output object composite, output data indicative of natural language text.

Claims

1. A computer implemented method for natural language execution, wherein the method includes, in a processing system, steps of:

(a) receiving input data indicative of natural language text;

(b) using a natural language processor to generate natural language parse information based on the input data;

(c) generating, using the natural language parse information, an input object composite including a plurality of linked objects, wherein each object represents a word or group of words of the input text;

(d) determining, for the objects of the input object composite and using an object knowledge network stored in a data store, a plurality of interpretation object composites that represent interpretation functions to interpret the respective word or group of words represented by each object;

(e) executing each interpretation function to modify one or more objects of the input object composite;

(f) determining, for the objects of the input object composite and using the object knowledge network, executable object composites that represent executable functions to execute actions associated with the objects representing the input text;

(g) executing the executable functions thereby generating an output object composite;

(h) updating the object knowledge network based on the input object composite, the output object composite and the execution of each interpretation and execution function represented by the respective interpretation and executable object composites; and

(i) outputting, based on the output object composite, output data indicative of natural language text.

2. The method according to claim 1, wherein the step of determining the plurality of interpretation object composites includes searching the object knowledge network to identify if interpretation object composites that represent the interpretation functions exists for each object.

3. The method according to claim 2, wherein in response to failing to identify the interpretation functions for one of the objects, the step of determining the plurality of interpretation object composites further includes attempting to infer the interpretation object composites that represent the interpretation functions using an inference engine and based on the object knowledge network.

4. The method according to claim 3, wherein the interpretation functions for each object are only executed in the event that the plurality of interpretation functions for all objects of the object composite have been identified or inferred.

5. The method according to claim 4, wherein in the event that the interpretation function for all objects cannot be successfully identified or inferred, the step of determining the plurality of interpretation object composites further includes:

(j) generating and outputting a request to a user for clarification of the respective word or group of words represented by a remainder of the objects which the respective interpretation function cannot be identified or inferred;

(k) receiving clarification text from the user, wherein the clarification text is natural language text;

(l) using the natural language processor to generate the natural language parse information based on the clarification text;

(m) performing steps (c) to (h) to for the natural language parse information generated based on the clarification text; and

(n) inferring or identifying the interpretation function for the remainder of the objects which previously could not be inferred or identified.

6. The method according to claim 5, wherein in the event that at least some of the interpretation functions associated with the clarification text cannot be identified or inferred, the step of determining the executable object composites further includes recursively performing steps (j) to (n) in relation to a further clarification request until the interpretation function associated with the further clarification text can be inferred or identified thereby allowing inference or identification of one or more interpretation functions which previously could not be inferred or identified.

7. The method according to claim 1, wherein the plurality of interpretation functions include:

a. sentence normaliser functions;

b. word and sentence disambiguation functions; and

c. co-reference functions.

8. The method according to claim 1, wherein the method includes normalizing the input data using a text normalizer prior to generating the input object composite.

9. The method according to claim 1, wherein the method includes selecting an object engine from a plurality of object engines to perform steps (d) to (h).

10. The method according to claim 9, wherein each object engine includes a scope definition, wherein the method includes:

generating a plurality of lexical affinity scores for the input object composite based on the scope definition of the plurality of object engines; and

selecting one of the object engines with the best lexical affinity score.

11. The method according to claim 1, wherein the step of determining the executable object composites include searching the object knowledge network to identify if an executable object composite representing the execution function exists for each object.

12. The method according to claim 11, wherein in response to failing to identify the execution function for one of the objects, the step of determining the executable object composites further includes attempting to infer the execution function based on the object knowledge network.

13. The method according to claim 11, wherein each execution function for the plurality of objects is only executed in the event that the execution function for all objects of the input object composite have been identified or inferred.

14. The method according to claim 13, wherein in the event that the execution function for all objects cannot be successfully identified or inferred, the step of determining the executable object composites further includes:

(o) outputting a request for clarification of the respective word or group of words represented by at least some of the objects which the respective execution function cannot be identified or inferred;

(p) receiving clarification text, wherein the clarification text is natural language text;

(q) using a natural language processor to generate the natural language parse information based on the clarification text;

(r) attempting to perform steps (c) to (h) for the natural language parse information generated based on the clarification text; and

(s) in the event that plurality of the execution functions associated with the clarification text are determined and executed, the method includes inferring or identifying the execution function for the at least some of the objects which previously could not be inferred or identified.

15. The method according to claim 14, wherein in the event that at least some of the execution functions associated with the clarification text cannot be identified or inferred, the step of determining the executable object composites further includes recursively performing steps (o) to (s) in relation to a further clarification request until the interpretation function associated with the further clarification text can be inferred or identified thereby allowing inference or identification of one or more interpretation functions which previously could not be inferred or identified.

16. The method of claim 1, wherein the method includes the processing system executing a software application performs the steps of the method, wherein the software application is an executable object composite.

17. A processing system for natural language execution, wherein the processing system is configured to:

receive input data, wherein the input data is natural language text;

use a natural language processor to generate natural language parse information based on the input data;

generate, using the natural language parse information, an input object composite including a plurality of linked objects, wherein each object represents a word or group of words of the input text;

determine, for the objects of the input object composite and using an object knowledge network stored in a data store, a plurality of interpretation object composites that represent interpretation functions to interpret the respective word or group of words represented by each object;

execute each interpretation function to modify one or more objects of the input object composite;

determine, for the objects of the input object composite and using the object knowledge network, a plurality of execution object composites that represent executable functions to execute actions associated with the objects representing the input text;

execute the executable functions, thereby generating an output object composite;

update the object knowledge network based on the input object composite, the output object composite and the execution of each interpretation and execution function represented by the respective interpretation and executable object composites; and

output, based on the output object composite, output data indicative of natural language text.

19. A system for natural language execution, wherein the system includes:

the processing system according to claim 17; and

a user device in data communication with the processing system, wherein the user device is configured to;

transfer the input data to the processing system; and

receive the output data from the processing system.

20. The system according to claim 19, wherein the user device is configured to:

generate the input data based upon one of;

text data input via a first input device of the user device image data captured via a second input device of the user device; and

audio data captured via a third input device of the user device; and

process the output data to generate at least one of;

textual output presented via a first output device of the user device; and

audio output presented via a second output device of the user device.

18. A computer readable medium for configuring a server processing system for natural language execution, wherein the computer readable medium includes executable instructions from executable object composites which, when executed, configure the server processing system to:

receive input data, wherein the input data is natural language text;

use a natural language processor to generate natural language parse information based on the input data;

generate, using the natural language parse information, an input object composite including a plurality of linked objects, wherein each object represents a word or group of words of the input text;

determine, for the objects of the input object composite and using an object knowledge network stored in a data store, a plurality of interpretation object composites that represent the interpretation functions to interpret the respective word or group of words represented by each object;

execute each interpretation function to modify one or more objects of the input object composite;

determine, for the objects of the input object composite and using the object knowledge network, a plurality of execution object composites that represent executable functions to execute actions associated with the objects representing the input text;

execute the executable functions, thereby generating an output object composite;

update the object knowledge network based on the input object composite, the output object composite and the execution of each interpretation and execution function represented by the respective interpretation and executable object composites; and

output, based on the output object composite, output data indicative of natural language text.