Method and system for generating vehicle service content based on multi-symptom rule

• US 10,453,036 B1
• Filed: 05/31/2017
• Issued: 10/22/2019
• Est. Priority Date: 11/04/2013
• Status: Active Grant

• Current Assignee: Snap-On Incorporated
• Original Assignee: Snap-On Incorporated
• Inventors: Lewis, Bradley R., Merg, Patrick S., Kelley, Brett A., Foreman, Jacob G.
• Primary Examiner(s): Singh, Satwant K
• Application Number:
  US15/609,868
• Time in Patent Office: 874 Days
• Field of Search: None
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G06Q 10/20 Administration of product repair or maintenance

Abstract

Methods and systems for using natural language processing and machine-learning algorithms to process vehicle-service data to generate metadata regarding the vehicle-service data are described herein. A processor can discover vehicle-service data that can be clustered together based on the vehicle-service data having common characteristics. The clustered vehicle-service data can be classified (e.g., categorized) into any one of a plurality of categories. One of the categories can be for clustered vehicle-service data that is tip-worthy (e.g., determined to include data worthy of generating vehicle-service content (e.g., a repair hint). Another category can track instances of vehicle-service data that are considered to be common to an instance of vehicle-service data classified into the tip-worthy category. The vehicle-service data can be collected from repair orders from a plurality of repair shops. The vehicle-service content generated by the systems can be provided to those or other repair shops.

Claims

1. A method comprising:

generating, by one or more processors including a natural-language processor, a new or modified repair order (RO) cluster based on metadata the natural-language processor associates with multiple computer-readable repair orders, wherein the metadata associated with each repair order indicates a context of the repair order based on one or more taxonomy terms the natural-language processor determines based on natural human-language terms represented in the repair order, wherein the multiple computer-readable repair orders include a first group of repair orders and a second group of repair orders, and wherein a first context of each RO within the first group of repair orders includes an indication a first symptom and a second symptom were resolved by replacing a first vehicle component first and then replacing a second vehicle component, and wherein a second context of each RO within the second group of repair orders include an indication the first symptom and the second symptom were resolved by replacing the second vehicle component;

generating, by the one or more processors, vehicle service content based on a multi-symptom rule, the first context, and the second context, wherein the multi-symptom rule specifies a sequence for working on or diagnosing the first symptom and the second symptom, and wherein the vehicle service content includes an indication to work on or diagnose the second vehicle component first;

receiving, by the one or more processors from a vehicle service tool, a request for vehicle service content, wherein the request comprises an identifier of the first symptom and an identifier of the second symptom; and

transmitting, by the one or more processors in response to the request, the vehicle service content to a communication network for transmission to the vehicle service tool.

2. The method of claim 1,

wherein the identifier of the first symptom comprises a first diagnostic trouble code (DTC) identifier,

wherein the identifier of the second symptom comprises a second DTC identifier, and

wherein the first DTC identifier and the second DTC identifier identify different diagnostic trouble codes.

3. The method of claim 1,

wherein the identifier of the first symptom comprises a first diagnostic trouble code (DTC) identifier,

wherein the identifier of the second symptom comprises a non-DTC symptom identifier.

4. The method of claim 1,

wherein the identifier of the first symptom comprises a first non-DTC symptom identifier,

wherein the identifier of the second symptom comprises a second non-DTC symptom identifier, and

wherein the first non-DTC symptom identifier and the second non-DTC symptom identifier identify different symptoms.

5. The method of claim 1, further comprising:

determining, by the one or more processors, the multi-symptom rule based on the multiple computer-readable repair orders,

wherein the sequence for working on or diagnosing the first symptom and the second symptom is based on a cost resulting from replacement of either or both of the first vehicle component and the second vehicle component.

6. The method of claim 5, wherein the cost is a mean cost determined from one or more repair orders of the RO cluster.

7. The method of claim 1, further comprising:

determining, by the one or more processors, the multi-symptom rule based on the multiple computer-readable repair orders,

wherein the sequence for working on or diagnosing the first symptom and the second symptom is based on a time required to replace either or both of the first vehicle component and the second vehicle component.

8. The method of claim 7, wherein the time is a mean time determined from one or more repair orders of the RO cluster.

9. The method of claim 1, wherein the RO cluster comprises the multiple computer-readable repair orders.

10. The method of claim 1, wherein multiple RO clusters are based on the multiple computer-readable repair orders.

11. A computing device comprising one or more processors including a natural-language processor and a computer-readable medium storing program instructions, that when executed by the one or more processors, cause a set of functions to be performed, the set of functions comprising:

generating, by the one or more processors including a natural language processor, a new or modified repair order (RO) cluster based on metadata the natural-language processor associates with multiple computer-readable repair orders, wherein the metadata associated with each repair order indicates a context of the repair order based on one or more taxonomy terms the natural-language processor determines based on natural human-language terms represented in the repair order, wherein the multiple computer-readable repair orders include a first group of repair orders and a second group of repair orders, and wherein a first context of each RO within the first group of repair orders indicates a first symptom and a second symptom were resolved by replacing a first vehicle component first and then replacing a second vehicle component, and wherein a second context of each RO within the second group of repair orders indicates the first symptom and the second symptom were resolved by replacing the second vehicle component;

generating, by the one or more processors, vehicle service content based on a multi-symptom rule, the first context, and the second context, wherein the multi-symptom rule specifies a sequence for working on or diagnosing the first symptom and the second symptom, and wherein the vehicle service content includes an indication to work on or diagnose the second vehicle component first;

receiving, by the one or more processors from a vehicle service tool, a request for vehicle service content, wherein the request comprises an identifier of the first symptom and an identifier of the second symptom; and

transmitting, by the one or more processors in response to the request, the vehicle service content to a communication network for transmission to the vehicle service tool.

12. The computing device of claim 11,

wherein the identifier of the first symptom comprises a first diagnostic trouble code (DTC) identifier,

wherein the identifier of the second symptom comprises a second DTC identifier, and

wherein the first DTC identifier and the second DTC identifier identify different diagnostic trouble codes.

13. The computing device of claim 11,

wherein the identifier of the first symptom comprises a first diagnostic trouble code (DTC) identifier,

wherein the identifier of the second symptom comprises a non-DTC symptom identifier.

14. The computing device of claim 11,

wherein the identifier of the first symptom comprises a first non-DTC symptom identifier,

wherein the identifier of the second symptom comprises a second non-DTC symptom identifier, and

wherein the first non-DTC symptom identifier and the second non-DTC symptom identifier identify different symptoms.

15. The computing device of claim 11, further comprising:

determining, by the one or more processors, the multi-symptom rule based on the multiple computer-readable repair orders,

wherein the sequence for working on or diagnosing the first symptom and the second symptom is based on a cost resulting from replacement of either or both of the first vehicle component and the second vehicle component.

16. The computing device of claim 11, further comprising:

determining, by the one or more processors, the multi-symptom rule based on the multiple computer-readable repair orders,

wherein the sequence for working on or diagnosing the first symptom and the second symptom is based on a time required to replace either or both of the first vehicle component and the second vehicle component.

17. A non-transitory computer-readable medium storing program instructions, that when executed by one or more processors including a natural-language processor of a computing device, cause the computing device to perform a set of functions comprising:

generating, by the one or more processors, a new or modified repair order (RO) cluster based on metadata the natural-language processor associates with multiple computer-readable repair orders, wherein the metadata associated with each repair order indicates a context of the repair order based on one or more taxonomy terms the natural-language processor determines based on natural human-language terms represented in the repair order, wherein the multiple computer-readable repair orders include a first group of repair orders and a second group of repair orders, and wherein a first context of each RO within the first group of repair orders indicates a first symptom and a second symptom were resolved by replacing a first vehicle component first and then replacing a second vehicle component, and wherein a second context of each RO within the second group of repair orders indicates the first symptom and the second symptom were resolved by replacing the second vehicle component;

generating, by the one or more processors, vehicle service content based on a multi-symptom rule, the first context, and the second context, wherein the multi-symptom rule specifies a sequence for working on or diagnosing the first symptom and the second symptom, and wherein the vehicle service content includes an indication to work on or diagnose the second vehicle component first;

receiving, by the one or more processors from a vehicle service tool, a request for vehicle service content, wherein the request comprises an identifier of the first symptom and an identifier of the second symptom; and

transmitting, by the one or more processors in response to the request, the vehicle service content to a communication network for transmission to the vehicle service tool.