KNOWLEDGE BASE CONTENT DISCOVERY

• US 20200134512A1
• Filed: 10/30/2018
• Published: 04/30/2020
• Est. Priority Date: 10/30/2018
• Status: Abandoned Application

• Current Assignee: Nutanix Incorporated
• Original Assignee: Nutanix Incorporated
• Inventors: MEESALA, Raviteja, CHAMNESS, Mark Albert
• Application Number:
  US16/175,737
• Publication Number:
  US 20200134512A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/2291 User-Defined Types; Storage management thereof
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/027 Frames
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

Rapid knowledge base discovery techniques. A database describes associations between knowledge base articles and closed problem cases. In periodic batch operations, the associations are used to generate solution probability predictors, each of which predictor corresponds to a probability that a particular knowledge base article was used to resolve a particular problem or case. The solution probability predictors comprise probability predictor parameter values associated with the set of words that occur in closed customer problem cases. A specialized data structure is populated with the probability predictor parameter values. When a new active customer problem case is opened, a set of words pertaining to the new, active case is constructed. The active case words are used with the specialized data structure to generate a probability value for each of a set of knowledge base articles. The knowledge base articles having the highest probability values are identified and presented in an ordered list.

Claims

1. A method for knowledge base content discovery, comprising:

recording mapping data that identifies an association to a description of a problem;

generating a solution probability predictor, the solution probability predictor having a probability predictor parameter value associated with a respective word;

generating a probability value that correlates the respective word with a problem description word; and

identifying an article, the article being identified based at least in part on the probability value.

2. The method of claim 1, further comprising populating a data structure with the probability predictor parameter value, wherein the data structure comprises at least one of, a table, a signed integer column, or a fractional portion column.

3. The method of claim 1, further comprising evaluating a hashing function.

5. The method of claim 1, wherein the probability predictor parameter value is determined based at least in part by performing a logistic regression over the mapping data.

6. The method of claim 1, wherein the solution probability predictor is described by a probability function.

8. The method of claim 1, wherein the article corresponds to a knowledge base article.

10. The method of claim 1, wherein the respective word is composed from at least one of, a description of the problem, an email associated with the problem, or a chat transcript associated with the problem.

24. The method of claim 1, wherein an active case word is generated that corresponds to a problem description, and the active case word is associated with a corresponding probability predictor parameter value to generate the probability value.

4. (canceled)

7. (canceled)

9. (canceled)

11. A non-transitory computer readable medium having stored thereon a sequence of instructions which, when stored in memory and executed by a processor causes the processor to perform a set of acts for knowledge base content discovery, the acts comprising:

recording mapping data that identifies an association to a description of a problem;

generating a solution probability predictor, the solution probability predictor having a probability predictor parameter value associated with a respective word;

generating a probability value that correlates the respective word with a problem description word; and

identifying an article, the article being identified based at least in part on the probability value.

12. The computer readable medium of claim 11, further comprising populating a data structure with the probability predictor parameter value, wherein the data structure comprises at least one of, a table, a signed integer column, or a fractional portion column.

13. The computer readable medium of claim 11, further comprising instructions which, when stored in memory and executed by the processor causes the processor to perform acts of evaluating a hashing function.

15. The computer readable medium of claim 11, wherein the probability predictor parameter value is determined based at least in part by performing a logistic regression over the mapping data.

16. The computer readable medium of claim 11, wherein the solution probability predictor is described by a probability function.

18. The computer readable medium of claim 11, wherein the article corresponds to a knowledge base article.

25. The computer readable medium of claim 11, wherein an active case word is generated that corresponds to a problem description, and the active case word is associated with a corresponding probability predictor parameter value to generate the probability value.

14. (canceled)

17. (canceled)

19. A system for knowledge base content discovery, comprising:

a storage medium having stored thereon a sequence of instructions; and

a processor that execute the instructions to cause the processors to perform a set of acts, the acts comprising,

recording mapping data that identifies an association to a description of a problem;

generating a solution probability predictor, the solution probability predictor having a probability predictor parameter value associated with a respective word;

generating a probability value that correlates the respective word with a problem description word; and

identifying an article, the article being identified based at least in part on the probability value.

20. The system of claim 19, further comprising populating a data structure with the probability predictor parameter value, wherein the data structure comprises at least one of, a table, a signed integer column, or a fractional portion column.

21. The system of claim 19, wherein an active case word is generated that corresponds to a problem description, and the active case word is associated with a corresponding probability predictor parameter value to generate the probability value.

22. The system of claim 19, wherein the article corresponds to a knowledge base article.

23. The system of claim 19, wherein the probability predictor parameter value is determined based at least in part by performing a logistic regression over the mapping data.