Emotion processing systems and methods

• US 10,268,507 B2
• Filed: 06/30/2017
• Issued: 04/23/2019
• Est. Priority Date: 11/22/2013
• Status: Active Grant

• Current Assignee: TNHC Investments LLC
• Original Assignee: Decooda International, Inc.
• Inventors: Wardell, Charles, Johnson, David
• Primary Examiner(s): St Leger, Geoffrey R
• Application Number:
  US15/640,163
• Publication Number:
  US 20170308399A1
• Time in Patent Office: 662 Days
• Field of Search: None
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9538 Presentation of query results
• CPC Class Codes: G06F 2209/5017 Task decomposition
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06F 9/4806 Task transfer initiation or dispatching
• CPC Class Codes: G06F 9/4843 by program, e.g. task dispatcher, supervisor, operating system
• CPC Class Codes: G06F 9/5027 the resource being a machine, e.g. CPUs, Servers, Terminals
• CPC Class Codes: G06F 9/5072 Grid computing
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

A system for conducting parallelization of tasks is disclosed, The system includes an interface for receiving messages comprising a representation of logic describing two tasks to be executed in parallel, the messages further comprising a content payload for use in the tasks. The system further includes a parallel processing grid comprising devices running on independent machines, each device comprising a processing manager unit and at least two processing units, The processing manager is configured to parse the received messages and to distribute the at least two tasks to the at least two processing units for independent and parallel processing relative to the content payload.

Claims

1. A system for conducting parallelization of tasks, comprising:

an interface for receiving messages, each message comprising a representation of logic describing at least two tasks to be executed in parallel, and a content payload to be processed by conducting the tasks; and

a parallel processing grid comprising devices running on independent machines, each device comprising a processing manager unit and at least two processing units, wherein the processing manager unit is configured to parse the received messages and to distribute each task to a corresponding processing unit for independent and parallel processing relative to the content payload, wherein each of the at least two tasks are associated with a function to be executed h the processing units;

wherein the processing units are configured to identify the function associated with each task and to recall resources for executing the functions from a plug-in library, Wherein the functions comprise an emotional scoring function, Wherein the emotional scoring function is a task in parallel with a context determination task.

2. The system of claim 1, further comprising:

a messaging source having a website crawler, the website crawler configured to generate the message having the representation of logic describing parallel tasks and the content payload for use in the parallel tasks.

3. The system of claim 1, further comprising:

a messaging source having a streaming data interface for receiving streaming data, wherein the messaging source is configured to process the streaming data and to generate the messages using the streaming data.

4. The system of claim 3, wherein the messaging source is configured to use the streaming data to create the content payload and wherein identification of the tasks is not a part of the streaming data.

5. The system of claim 1, further comprising:

a messaging source comprising a query engine for querying a data source and for generating a series of the messages using the query results, wherein identification of the tasks is not a part of the query result data.

6. The system of claim 1, wherein the interface comprises a framework manager configured to queue the messages, and wherein each processing manager unit is configured to request new messages from the queue when resources permit.

7. The system of claim 1, wherein the messages are not compiled and do not include the source code for the tasks, and wherein the messages utilize a mark-up language to identify the parallel tasks and the content payload.

8. The system of claim 7, wherein the content payload comprises a reference to data not embedded within the messages.

9. The system of claim 8, wherein the function comprises a first emotional scoring function for a first emotion and another task includes a second emotional scoring function for a second emotion, wherein both of the first and second emotional scoring functions are executed as parallel tasks and represented as parallel tasks in the logic of the message.

10. The system of claim 1, wherein the function comprises a first emotional scoring function for a first emotion and another task includes a second emotional scoring function for a second emotion, wherein both of the first and second emotional scoring functions are executed as parallel tasks and represented as parallel tasks in the logic of the message.

11. A computerized method for processing tasks, comprising:

receiving a message describing at least two processing tasks to be parallelized and completed relative to a content payload of the message;

parsing the received message to identify the tasks for parallelization;

distributing the tasks, in parallel, to at least two discrete processing units, each task corresponding to a different discrete processing unit, wherein each of the at least two tasks is associated with a function to be executed by the processing units;

wherein the discrete processing units are configured to identify the function associated with each task and to recall resources for executing the functions from a plug-in library, wherein the functions comprise an emotional scoring function, wherein the emotional scoring function is a task in parallel with a context determination task.

12. The method of claim 11, further comprising:

generating the message having the content payload by crawling a changing web-based data source and creating a message for discrete updates to the data source.

13. The method of claim 11, further comprising:

generating the message using streaming data to create the content payload and wherein identification of the tasks is not a part of the streaming data.

14. The method of claim 11, further comprising:

generating the message using a query engine, by querying a data source and generating a series of messages using the query results, wherein identification of the tasks is not a part of the query result data.

15. The method of claim 11, wherein a processing manager unit conducts the receiving, parsing, and distribution steps, the method further comprising:

queuing the received message at a framework manager;

at each processing manager unit, requesting new messages from the queue when resources permit.

16. The method of claim 11, wherein the message is not compiled and does not include the source code for the tasks, and wherein the message utilizes a mark-up language to identify the tasks to be completed in parallel and the content payload.

17. The method of claim 16, wherein the content payload comprises a reference to data not embedded within the message.

18. The method of claim 17, wherein the function comprises a first emotional scoring function for a first emotion and another task includes a second emotional scoring function for a second emotion, wherein both of the first and second emotional scoring functions are executed as parallel tasks and represented as parallel tasks in the logic of the message.

19. The method of claim 11, wherein the function comprises a first emotional scoring function for a first emotion and another task includes a second emotional scoring function for a second emotion, wherein both of the first and second emotional scoring functions are executed as parallel tasks and represented as parallel tasks in the logic of the message.

20. The method of claim 11, wherein the function comprises a first emotional scoring function for a first emotion and another task includes a second emotional scoring function for a second emotion, wherein both of the first and second emotional scoring functions are executed as parallel tasks and represented as parallel tasks in the logic of the message.