Methods and Systems for Applying Machine Learning to Automatically Solve Problems

• US 20170011308A1
• Filed: 07/06/2016
• Published: 01/12/2017
• Est. Priority Date: 07/09/2015
• Status: Active Application

• Current Assignee: Sunview Software Inc. (ServiceAide, Inc.)
• Original Assignee: Sunview Software Inc. (ServiceAide, Inc.)
• Inventors: Sun, Seng, Porth, Joshua, Panicker, George, Kramer, Kurt, McNally, Daniel N.
• Application Number:
  US15/202,723
• Publication Number:
  US 20170011308A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 11/00 Error detection; Error correction; Monitoring error detection, correction or monitoring in information storage based on relative movement between record carrier and transducer G11B20/18; monitoring, i.e. supervising the progress of recording or reproducing G11B27/36; in static stores G11C29/00
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/016 After-sales

Abstract

A method for receiving a description of a problem and applying machine learning to automatically solve the problem includes receiving, by a first computing device, from a second computing device, via a user interface component, a description of a first problem. The method includes assigning, by a clustering engine executing on the first computing device, the first problem to a class. The method includes identifying, by a correlation engine executing on the first computing device, a first database associated with the class. The method includes retrieving, by the correlation engine, from the identified database, first data relevant to the first problem. The method includes providing, by the first computing device, via the user interface component, a suggestion for solving the first problem, based on the retrieved first data.

Claims

1. A method for receiving a description of a problem and applying machine learning to automatically solve the problem, the method comprising:

receiving, by a first computing device, from a second computing device, via a user interface component, a description of a first problem;

assigning, by a clustering engine executing on the first computing device, the first problem to a class;

identifying, by a correlation engine executing on the first computing device, a first database associated with the class;

retrieving, by the correlation engine, from the identified database, first data relevant to the first problem; and

providing, by the first computing device, via the user interface component, a suggestion for solving the first problem, based on the retrieved first data.

2. The method of claim 1, further comprising:

providing, by the user interface component, to a machine learning interface, the description of the first problem; and

providing, by the machine learning interface, to the clustering engine, the description of the first problem.

3. The method of claim 1, wherein assigning further comprises applying, by a clustering engine executing on the first computing device, machine learning to identify at least one keyword in the description of the first problem.

4. The method of claim 1, wherein assigning further comprises assigning the first problem to a class, wherein the class includes at least a second problem including at least one keyword included in the description of the first problem.

5. The method of claim 1 further comprising providing, by the clustering engine, to the correlation engine, the description of the first problem and the assigned class.

6. The method of claim 1, wherein identifying further comprises querying a database to identify the first database.

7. The method of claim 1, wherein identifying further comprises:

applying, by the correlation engine, a machine learning model to identify a second problem in the class; and

identifying, by the correlation engine, an association between the second problem in the class and the first database.

8. The method of claim 1, wherein identifying further comprises identifying the second problem, the second problem having at least one characteristic substantially similar to at least one characteristic of the first problem.

9. The method of claim 1, wherein retrieving further comprises querying a database to identify the first data for retrieval from the first database.

10. The method of claim 1, wherein retrieving further comprises:

applying, by the correlation engine, a machine learning model to identify a second problem in the class; and

identifying, by the correlation engine, the first data associated with the second problem and with a resolution to the second problem.

11. The method of claim 10, wherein applying, by the correlation engine, the machine learning model further comprises:

retrieving at least one historical event associated with the first problem; and

determining that the at least one historical event has at least one characteristic that is substantially similar to at least one characteristic of the second problem.

12. The method of claim 1, wherein retrieval further comprises identifying the second problem, the second problem having at least one characteristic substantially similar to at least one characteristic of the first problem.

13. The method of claim 1 further comprising analyzing the retrieved first data to identify second data relevant to the first problem.

14. The method of claim 1, wherein analyzing further comprises:

applying, by the correlation engine, a machine learning model to identify a second problem in the class; and

identifying, by the correlation engine, the second data associated with the second problem and with a resolution to the second problem.

15. The method of claim 1, wherein retrieval further comprises identifying the second problem, the second problem having at least one characteristic substantially similar to at least one characteristic of the first problem.

16. The method of claim 1, wherein providing further comprises:

applying, by the correlation engine, a machine learning model to identify a second problem in the class;

identifying, by the correlation engine, a resolution associated with the second problem; and

determining that the resolution to the second problem resolves the first problem.

17. The method of claim 1 further comprising automating, by the first computing device, execution of the suggestion.

18. The method of claim 1 further comprising:

receiving, by the machine learning interface, an identification of a second database accessible for solving problems in the class; and

updating, by the machine learning interface, a database storing at least one association between the class of problems and at least one database accessible for solving problems in the class, to include an identification of the second database.

19. The method of claim 1, wherein receiving the description of the first problem further comprises receiving a description of a technical support problem.

20. The method of claim 1, further comprising:

identifying, by the correlation engine, a task to be assigned to a user to implement the suggested solution;

transmitting, by the machine learning interface, to a user interface displaying at least one category of tasks associated with the user, an identification of the task; and

modification of the user interface to include the identification of the task.

21. The method of claim 1, further comprising:

identifying, by the correlation engine, a first task to be assigned to a user to implement the suggested solution and a modification of a level of priority of a second task associated with the user before identification of the first task;

transmitting, by the machine learning interface, to a user interface displaying at least one category of tasks associated with the user, a modification of the level of priority of the second task and the identification of the first task; and

modification of the user interface to include the identification of the task and the modified level of priority of the second task.

22. A system for receiving a description of a problem and applying machine learning to automatically solve the problem, the method comprising:

a first computing device receiving, from a second computing device, via a user interface component, a description of a first problem;

a machine learning interface receiving the description of the first problem from the user interface component;

a clustering engine executing on the first computing device, receiving the description of the problem from the machine learning interface, and assigning the first problem to a class;

a correlation engine executing on the first computing device, identifying a first database associated with the class, and retrieving from the identified database, first data relevant to the first problem; and

providing, by the first computing device, to the second computing device, via the user interface component, a suggestion for solving the first problem, based on the retrieved first data.