Systems and Methods for Automotive Diagnosis using Generative AI Models

An artificial intelligence (AI) based method for predictive automotive diagnosis comprising:

• receiving, by one or more processors, a set of symptomatic data of a vehicle relating to detected conditions of the vehicle, wherein each symptomatic data includes:
• (i) one or more symptoms comprising at least one of (A) one or more generated error codes from the vehicle or (B) one or more symptomatic descriptions of the vehicle from a user and (ii) date data of when each symptom was detected;
• receiving, by the one or more processors, a set of repair or maintenance procedures of the vehicle relating to addressing each symptom in the set of symptomatic data;
• inputting, by the one or more processors, the set of symptomatic data and the set of repair or maintenance procedures into a generative diagnostic prediction machine learning model to generate one or more predicted error conditions of the vehicle, wherein the generative diagnostic prediction machine learning model is trained on a training set of symptomatic data and a training set of repair or maintenance procedures, and the training set of symptomatic data includes:
• (i) one or more training symptoms comprising at least one of (A) one or more training generated error codes from the vehicle or (B) one or more training symptomatic descriptions of the vehicle and (ii) training date data of when each training symptom occurred;
• and
• presenting, by the one or more processors onto an interactive display, at least a portion of the one or more predicted error conditions.

A tangible, non-transitory computer-readable medium storing instructions for tracking reusable containers that when executed by one or more processors cause the one or more processors to:

• receive a set of symptomatic data of a vehicle relating to detected conditions of the vehicle, wherein each symptomatic data includes:
• (i) one or more symptoms comprising at least one of (A) one or more generated error codes from the vehicle or (B) one or more symptomatic descriptions of the vehicle from a user and (ii) date data of when each symptom was detected;
• receive a set of repair or maintenance procedures of the vehicle relating to addressing each symptom in the set of symptomatic data;
• input the set of symptomatic data and the set of repair or maintenance procedures into a generative diagnostic prediction machine learning model to generate one or more predicted error conditions of the vehicle, wherein the generative diagnostic prediction machine learning model is trained on a training set of symptomatic data and a training set of repair or maintenance procedures, and the set of predictive symptomatic data includes:
• (i) one or more predicted symptoms comprising at least one of (A) one or more predicted generated error codes from the vehicle or (B) one or more predicted symptomatic descriptions of the vehicle and (ii) predicted date data of when each predicted symptom occurred;
• and
• present, via an interactive display, at least a portion of the one or more predicted error conditions.