Method and system for providing artificial intelligence analytic (aia) services for performance prediction

Abstract

One embodiment of the present invention predicts a vehicular event relating to machinal performance using information obtained from interior and exterior sensors, vehicle onboard computer (“VOC”), and cloud data. The process of predication is able to activate interior and exterior sensors mounted on a vehicle operated by a driver for obtaining current data relating to external surroundings, interior settings, and internal mechanical conditions of the vehicle. After forwarding the current data to VOC to generate a current vehicle status representing real-time vehicle performance in accordance with the current data, retrieving a historical data associated with the vehicle including mechanical condition is retrieved. In one aspect, a normal condition signal is issued when the current vehicle status does not satisfy with the optimal condition based on the historical data. Alternatively, a race car condition is issued when the current vehicle status meets with the optimal condition.

1 - 20 . (canceled) 
     
     
         21 . A method, comprising:
 obtaining data from a plurality of sensors associated with a vehicle;   generating a current vehicle status representing a current vehicle performance based on the obtained data;   retrieving historical data associated with the vehicle;   issuing a normal condition signal in response to the current vehicle status failing to satisfy an optimal condition based on the historical data; and   in response to issuing the normal condition signal, employing a failure predictive artificial intelligence model to predict a failure in the vehicle and providing an indication of the failure.   
     
     
         22 . The method of  claim 21 , wherein employing the failure predictive artificial intelligence model to predict the failure in the vehicle comprises:
 assessing a likelihood failure rate and a failure time based on the obtained data and the historical data to predict the failure.   
     
     
         23 . The method of  claim 21 , further comprising:
 issuing a non-normal condition signal in response to the current vehicle status satisfying the optimal condition based on the historical data; and   in response to issuing the non-normal condition signal, employing the vehicle to provide direct feedback to a driver of the vehicle indicating skill of the driver.   
     
     
         24 . The method of  claim 21 , further comprising:
 sending a performance report to a third party indicating a current mechanical condition of the vehicle based on the current vehicle performance and the historical data.   
     
     
         25 . The method of  claim 21 , wherein obtaining the data from the plurality of sensors comprises:
 instructing one or more outward-facing cameras on the vehicle to record an environment outside the vehicle.   
     
     
         26 . The method of  claim 21 , wherein obtaining the data from the plurality of sensors comprises:
 instructing one or more inward-facing cameras on the vehicle to record an environment inside the vehicle.   
     
     
         27 . The method of  claim 21 , wherein obtaining the data from the plurality of sensors comprises:
 recording real-time data associated with vehicle performance, road conditions, traffic congestion, and weather conditions.   
     
     
         28 . The method of  claim 21 , wherein issuing the normal condition signal includes:
 determining that the current vehicle status fails to satisfy the optimal condition based on the current vehicle status failing meet performance requirements as manufactured based on the historical data.   
     
     
         29 . The method of  claim 21 , wherein issuing the normal condition signal includes:
 determining that the current vehicle status fails to satisfy the optimal condition based on the current vehicle status indicating signs of wear and tear.   
     
     
         30 . The method of  claim 21 , wherein employing the failure predictive artificial intelligence model comprises:
 employing the failure predictive artificial intelligence model to automatically schedule a maintenance appointment for the vehicle based on the failure, the obtained data, and the historical data.   
     
     
         31 . The method of  claim 21 , wherein employing the failure predictive artificial intelligence model comprises:
 employing the failure predictive artificial intelligence model to generate a suggested improvement for the vehicle based on the failure, the obtained data, and the historical data.   
     
     
         32 . A computing system, comprising:
 a memory that stores computer instructions; and   a processor that, when executing the computer instructions, causes the computing system to:
 collect data from a plurality of sensors on a vehicle; 
 generate a current vehicle status of the vehicle based on the collected data; 
 obtain historical data associated with the vehicle; 
 determine that the current vehicle status fails to satisfy an optimal condition based on the historical data; and 
 in response to determining that the current vehicle status fails to satisfy the optimal condition, employ a failure predictive artificial intelligence model that employs the collected data and the historical data to predict a failure in the vehicle, and provide an indication of the failure. 
   
     
     
         33 . The computing system of  claim 32 , wherein the processor, when executing the computer instructions to cause the computing system to employ the failure predictive artificial intelligence model, further causes the computing system to:
 predict the failure in the vehicle based on a likelihood failure rate and a failure time based on the collected data and the historical.   
     
     
         34 . The computing system of  claim 32 , wherein the processor, when executing the computer instructions to cause the computing system to determine that the current vehicle status fails to satisfy the optimal condition, further causes the computing system to:
 determine that the current vehicle status fails to satisfy the optimal condition based on the current vehicle status failing meet performance requirements as manufactured based on the historical data.   
     
     
         35 . The computing system of  claim 32 , wherein the processor, when executing the computer instructions to cause the computing system to determine that the current vehicle status fails to satisfy the optimal condition, further causes the computing system to:
 determine that the current vehicle status fails to satisfy the optimal condition based on the current vehicle status indicating signs of wear and tear.   
     
     
         36 . The computing system of  claim 32 , wherein the processor, when executing the computer instructions to cause the computing system to employ the failure predictive artificial intelligence model, further causes the computing system to:
 employ the failure predictive artificial intelligence model to automatically schedule a maintenance appointment for the vehicle based on the failure, the collected data, and the historical data.   
     
     
         37 . The computing system of  claim 32 , wherein the processor, when executing the computer instructions to cause the computing system to employ the failure predictive artificial intelligence model, further causes the computing system to:
 employ the failure predictive artificial intelligence model to generate a suggested improvement for the vehicle based on the failure, the collected data, and the historical data.   
     
     
         38 . A non-transitory computer-readable storage medium having stored thereon computing instructions that, when executed by a processor, cause the processor to perform actions, the actions comprising:
 instructing a plurality of sensors on a vehicle to collect data;   determining a current vehicle status of the vehicle based on the collected data;   determining that the current vehicle status fails to satisfy an optimal condition based on historical data associated with the vehicle; and   in response to determining that the current vehicle status fails to satisfy the optimal condition, utilizing the collected data and the historical data to predict a failure in the vehicle, and providing an indication of the failure.   
     
     
         39 . The non-transitory computer-readable storage medium of  claim 38 , wherein the computing instructions, when executed by the processor, cause the processor to perform further actions, the further actions comprising:
 employing a failure predictive artificial intelligence model to predict the failure in the vehicle based on a likelihood failure rate and a failure time based on the collected data and the historical data.   
     
     
         40 . The non-transitory computer-readable storage medium of  claim 38 , wherein the computing instructions, when executed by the processor, cause the processor to perform further actions, the further actions comprising:
 in response to determining that the current vehicle status satisfies the optimal condition, employing the vehicle to provide direct feedback to a driver of the vehicle indicating skill of the driver.