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.

Abstract: An example method involves receiving, at a computing system, vehicle diagnostic information from a vehicle. The vehicle diagnostic information may include one or more sets of parameters corresponding to parameter identifiers (PIDs). The method further involves identifying a first set of parameters corresponding to a PID representing a state of a particular system of the vehicle and determining, using the first set of parameters, a current value of the PID. The method may also involve performing a comparison between the current value and a predetermined value for the PID and determining a health of the particular system of the vehicle such that the health reflects a difference between the current value and the predetermined value for the PID. The method may also involve displaying, by the computing system at a graphical interface, a vehicle health record representing the health of the particular system of the vehicle.

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.

Abstract: Methods and apparatus are provided that are related to generating repair orders, including vehicular repair orders. A computing device can receive repair-related information associated with a repair order. The repair-related information can include information about a first repair attribute of one or more repair attributes. The computing device can determine a first ontology related to the first repair attribute. The first ontology can be further related to a first template. The computing device can determine modified repair-related information by at least utilizing the first template to modify at least a first portion of the repair-related information that includes the information about the first repair attribute. The computing device can generate an output related to the repair order that includes the modified repair-related information.

Abstract: A processor may determine that a particular computer-readable vehicle repair order (RO) (e.g., including first and second RO portions) corresponds to an existing cluster of ROs due to the particular RO including RO data that refers to a particular vehicle symptom. The processor may determine that the first RO portion includes first data representative of a non-specific vehicle component and may then responsively also determine that the second RO portion includes second data that the at least one processor can use to determine a specific vehicle component associated with the particular RO. Responsively, the processor may determine the specific vehicle component based on the first and second data and may then add the particular RO to a different cluster of ROs that is arranged to contain ROs that correspond to the particular vehicle symptom and to the specific vehicle component.

Abstract: A method includes receiving a web-page with a search bar and a first set of card(s) including file(s) of a first set of content files. The method includes transmitting an indication the search bar was selected and receiving tag array(s) associated with a second set of content files. The method includes displaying, within an expanded search bar, tag array(s) including a first tag array associated with file(s) among the second set of files, and transmitting an indication the first tag array was selected from within the search bar, and receiving a third set of content files for populating card(s) on a modified web-page. Each file of the third set of files includes a file most-recently downloaded to a server and associated with the first tag array. The method includes displaying the modified web-page, including a second set of one or more cards including file(s) of the third set of files.

Abstract: An example method includes determining identifying information for a vehicle to be serviced. The method further includes receiving at least one symptom identifier for the vehicle. The method further includes sending a request over a communication network to a remote server for a PID filter list for the vehicle, the request comprising the identifying information for the vehicle and the at least one symptom identifier for the vehicle. The method additionally includes receiving a response to the request over the communication network from the remote server, the response comprising the PID filter list for the vehicle. The method further includes determining, based on the PID filter list for the vehicle, a symptom-based subset of PIDs for the vehicle from a set of available PIDs. The method additionally includes displaying, on a display interface, the symptom-based subset of PIDs for the vehicle.

Abstract: A method includes receiving a web-page with a search bar and a first set of card(s) including file(s) of a first set of content files. The method includes transmitting an indication the search bar was selected and receiving tag array(s) associated with a second set of content files. The method includes displaying, within an expanded search bar, tag array(s) including a first tag array associated with file(s) among the second set of files, and transmitting an indication the first tag array was selected from within the search bar, and receiving a third set of content files for populating card(s) on a modified web-page. Each file of the third set of files includes a file most-recently downloaded to a server and associated with the first tag array. The method includes displaying the modified web-page, including a second set of one or more cards including file(s) of the third set of files.

Abstract: Methods and apparatus are provided that are related to generating repair orders, including vehicular repair orders. A computing device can receive repair-related information associated with a repair order. The repair-related information can include information about a first repair attribute of one or more repair attributes. The computing device can determine a first ontology related to the first repair attribute. The first ontology can be further related to a first template. The computing device can determine modified repair-related information by at least utilizing the first template to modify at least a first portion of the repair-related information that includes the information about the first repair attribute. The computing device can generate an output related to the repair order that includes the modified repair-related information.

Abstract: An example method for outputting a PID filter list (PFL) includes: receiving RO data from one or more ROs that indicate particular vehicle identifying information (PVII), at least one symptom identifier, and a particular vehicle component; determining, symptom-to-parameter-identifier (PID) mapping data (MD) based on the received RO data and component-to-PID MD; determining, based on the set of available PIDs for the SOV and the symptom-to-PID MD, a PFL, wherein the PFL is associated with the PVII and the at least one symptom identifier, and wherein the PFL indicates a symptom-based subset of PIDs from the set of available PIDs for the SOV; receiving, a request sent over a communication network from a display device, wherein the request comprises the PVII and the at least one symptom identifier; and transmitting, over the communication network to the display device, a response to the request, the response comprising the PFL.

Abstract: An example method includes determining identifying information for a vehicle to be serviced. The method further includes receiving at least one symptom identifier for the vehicle. The method further includes sending a request over a communication network to a remote server for a PID filter list for the vehicle, the request comprising the identifying information for the vehicle and the at least one symptom identifier for the vehicle. The method additionally includes receiving a response to the request over the communication network from the remote server, the response comprising the PID filter list for the vehicle. The method further includes determining, based on the PID filter list for the vehicle, a symptom-based subset of PIDs for the vehicle from a set of available PIDs. The method additionally includes displaying, on a display interface, the symptom-based subset of PIDs for the vehicle.

Abstract: An example method for outputting a PID filter list (PFL) includes: receiving RO data from one or more ROs that indicate particular vehicle identifying information (PVII), at least one symptom identifier, and a particular vehicle component; determining, symptom-to-parameter-identifier (PID) mapping data (MD) based on the received RO data and component-to-PID MD; determining, based on the set of available PIDs for the SOV and the symptom-to-PID MD, a PFL, wherein the PFL is associated with the PVII and the at least one symptom identifier, and wherein the PFL indicates a symptom-based subset of PIDs from the set of available PIDs for the SOV; receiving, a request sent over a communication network from a display device, wherein the request comprises the PVII and the at least one symptom identifier; and transmitting, over the communication network to the display device, a response to the request, the response comprising the PFL.

Abstract: An example method for outputting a PID filter list (PFL) includes: receiving RO data from one or more ROs that indicate particular vehicle identifying information (PVII), at least one symptom identifier, and a particular vehicle component; determining, symptom-to-parameter-identifier (PID) mapping data (MD) based on the received RO data and component-to-PID MD; determining, based on the set of available PIDs for the SOV and the symptom-to-PID MD, a PFL, wherein the PFL is associated with the PVII and the at least one symptom identifier, and wherein the PFL indicates a symptom-based subset of PIDs from the set of available PIDs for the SOV; receiving, a request sent over a communication network from a display device, wherein the request comprises the PVII and the at least one symptom identifier; and transmitting, over the communication network to the display device, a response to the request, the response comprising the PFL.

Abstract: An example method for outputting a PID filter list (PFL) includes: receiving RO data from one or more ROs that indicate particular vehicle identifying information (PVII), at least one symptom identifier, and a particular vehicle component; determining, symptom-to-parameter-identifier (PID) mapping data (MD) based on the received RO data and component-to-PID MD; determining, based on the set of available PIDs for the SOV and the symptom-to-PID MD, a PFL, wherein the PFL is associated with the PVII and the at least one symptom identifier, and wherein the PFL indicates a symptom-based subset of PIDs from the set of available PIDs for the SOV; receiving, a request sent over a communication network from a display device, wherein the request comprises the PVII and the at least one symptom identifier; and transmitting, over the communication network to the display device, a response to the request, the response comprising the PFL.

Abstract: A method includes receiving a web-page with a search bar and a first set of card(s) including file(s) of a first set of content files. The method includes transmitting an indication the search bar was selected and receiving tag array(s) associated with a second set of content files. The method includes displaying, within an expanded search bar, tag array(s) including a first tag array associated with file(s) among the second set of files, and transmitting an indication the first tag array was selected from within the search bar, and receiving a third set of content files for populating card(s) on a modified web-page. Each file of the third set of files includes a file most-recently downloaded to a server and associated with the first tag array. The method includes displaying the modified web-page, including a second set of one or more cards including file(s) of the third set of files.

Abstract: An example method for outputting a PID filter list (PFL) includes: receiving RO data from one or more ROs that indicate particular vehicle identifying information (PVII), at least one symptom identifier, and a particular vehicle component; determining, symptom-to-parameter-identifier (PID) mapping data (MD) based on the received RO data and component-to-PID MD; determining, based on the set of available PIDs for the SOV and the symptom-to-PID MD, a PFL, wherein the PFL is associated with the PVII and the at least one symptom identifier, and wherein the PFL indicates a symptom-based subset of PIDs from the set of available PIDs for the SOV; receiving, a request sent over a communication network from a display device, wherein the request comprises the PVII and the at least one symptom identifier; and transmitting, over the communication network to the display device, a response to the request, the response comprising the PFL.

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.

Abstract: A processor may determine that a first repair order (RO) of an existing cluster of ROs (e.g. corresponding to first and second vehicle symptoms and to first and second corrective actions) refers to a first vehicle related to a set of vehicles. The processor may identify ROs that refer to vehicles from the set and may determine among these identified ROs (i) a first relationship between the first symptom and the first action without the first symptom relating to the second action and/or (ii) a second relationship between the second symptom and the second action without the second symptom relating to the first action. The processor may (i) if the first relationship exists, add the first RO to a first different cluster corresponding to the first relationship and (ii) if the second relationship exists, add the first RO to a second different cluster corresponding to the second relationship.

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.

Abstract: Methods and systems may relate to clustering of repair orders based on alternative repair indicators. Accordingly, a processor may determine that a particular computer-readable vehicle repair order (RO) corresponds to an existing cluster of ROs due to the particular RO including data that refers to a particular vehicle symptom. This existing cluster may be arranged to contain ROs that correspond to the particular vehicle symptom. In response to determining that the particular RO correspond to the existing cluster, the processor may determine that the particular RO includes data indicating an alternative repair that defines a vehicle repair for resolving the particular vehicle symptom other than by replacement of a particular vehicle component. Responsively, the processor may add the particular RO to a different cluster of ROs. This different cluster may be arranged to contain ROs that correspond to the particular vehicle symptom and to the alternative repair.