CLASSIFYING PARTS VIA MACHINE LEARNING

• US 20200134496A1
• Filed: 10/29/2018
• Published: 04/30/2020
• Est. Priority Date: 10/29/2018
• Status: Abandoned Application

• Current Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Original Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Inventors: Lugo Beauchamp, Wilfredo E, Hawkins, William J.
• Application Number:
  US16/173,599
• Publication Number:
  US 20200134496A1
• Time in Patent Office: Days
• CPC Class Codes: G05B 19/4183 characterised by data acquisition, e.g. workpiece identification
• CPC Class Codes: G05B 2219/49302 Part, workpiece, code, tool identification
• CPC Class Codes: G05B 2219/49304 Tool identification, code
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06F 18/2415 based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 7/00 Computing arrangements based on specific mathematical models
• CPC Class Codes: G06Q 10/20 Administration of product repair or maintenance
• CPC Class Codes: G06V 2201/06 Recognition of objects for industrial automation
• CPC Class Codes: H04L 9/0643 Hash functions, e.g. MD5, SHA, HMAC or f9 MAC
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

Example implementations relate to classifying parts. A computing device may comprise a processing resource; and a memory resource storing non-transitory machine-readable instructions to cause the processing resource to: receive a part description of a part; classify the part by determining a commodity of the part based on the part description using machine learning; and update attributes of the part based on the determined commodity of the classified part.

Claims

1. A computing device, comprising:

a processing resource; and

a memory resource storing non-transitory machine-readable instructions to cause the processing resource to;

receive a part description of a part;

classify the part by determining a commodity of the part based on the part description using machine learning; and

update attributes of the part based on the determined commodity of the classified part.

2. The computing device of claim 1, including instructions to cause the processing resource to classify the part using logistic regression machine learning.

3. The computing device of claim 1, including instructions to cause the processing resource to train the machine learning using a training data set.

4. The computing device of claim 3, wherein the training data set includes a plurality of parts each including predetermined part commodities having parts including predetermined initial attributes.

5. The computing device of claim 1, including instructions to cause the processing resource to generate a report of parts having the same determined commodity of the classified part.

6. The computing device of claim 1, including instructions to cause the processing resource to generate a report of parts having a same updated attribute of the classified part.

7. The computing device of claim 1, wherein the part description of the part includes at least one of:

a description of the part; and

a part number of the part.

8. The computing device of claim 1, wherein the part is a replacement part.

9. The computing device of claim 1, wherein the part is an unclassified part.

10. The computing device of claim 1, including instructions to cause the processing resource to modify the updated attributes of the part in response to a user input.

11. A non-transitory computer readable medium storing instructions executable by a processing resource to cause the processing resource to:

receive a part description of a part, wherein the part description includes initial attributes of the part;

classify the part by determining a commodity of the part based on the part description of the part using machine learning; and

update attributes of the part based on the determined commodity of the classified part from initial attributes to revised attributes.

12. The medium of claim 11, wherein the instructions to classify the part include instructions to generate a token for each word included in the part description of the part.

13. The medium of claim 12, wherein the instructions to classify the part include instructions to apply a hash function to the token for each word included in the part description of the part to generate an index value for each word included in the part description.

14. The medium of claim 13, including instructions to use one versus rest logistic regression machine learning of the index value for each word included in the part description against predetermined training data to classify the part to determine the commodity of the part.

15. The medium of claim 13, including instructions to advance a particular index value of a particular word included in the part description by one in response to the particular index value of the particular word matching an index value of a different word.

16. A method, comprising:

receiving, by a computing device, a part description of a part, wherein the part description includes initial attributes of the part;

classifying, by the computing device, the part by determining a commodity of the part based on the part description of the part using logistic regression machine learning; and

updating, by the computing device, attributes of the part based on the determined commodity of the classified part from initial attributes to revised attributes.

17. The method of claim 16, wherein the method includes modifying, by the computing device, the revised attributes of the part in response to the revised attributes being misclassified.

18. The method of claim 16, wherein the method includes training the logistic regression machine learning utilizing a training data set having parts including predetermined initial attributes.

19. The method of claim 18, wherein the method includes receiving, by the computing device, the training data set from an external server.

20. The method of claim 16, wherein the method includes generating, by the computing device, a report including a replacement rate of parts having the same determined commodity of the classified part.