Training tuples including text and a question and answer corresponding to the text are input to a machine learning algorithm, such as a deep neural network. A Q&A model is obtained that outputs questions and answers given an input text. The training tuples may be obtained from standardized test such that the text is a question prompt and the questions and answers are based on the prompt. Raw text is input to the Q&A model to obtain second training tuples including a question and an answer. An NLU model is trained according to the second training tuples. The NLU model may then be installed on a consumer device, which will then use the model to respond to conversational queries and provide an appropriate response.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for training a query-response model for use in a vehicle, the method comprising, by a computer system: training a first model using a first plurality of tuples each including text, a question, and an answer; processing unstructured data using the first model to obtain a second plurality of tuples each including a question and an answer; and training a second model using the second plurality of tuples. 2. The method of claim 1, further comprising loading the second model onto a consumer computing device. 3. The method of claim 2, wherein the consumer computing device is an in-vehicle infotainment (IVI) system mounted in a vehicle. 4. The method of claim 3, further comprising: programming the IVI system to receive a query, input the query to the second model, and output a response according to the second model. 5. The method of claim 3, further comprising: programming the IVI system to input voice queries to the second model and output a response to the query according to the second model. 6. The method of claim 1, wherein the first model is a deep neural network (DNN) model. 7. The method of claim 1, wherein the second model is a deep neural network (DNN) model. 8. The method of claim 1, wherein processing the unstructured data using the first model comprises: pre-processing, by the computer system, the unstructured data to identify a feature set from within the unstructured data; and inputting, by the computer system, the feature set to the first model. 9. The method of claim 1, wherein the unstructured data comprises at least one of text and images. 10. The method of claim 1, wherein the first plurality of tuples are derived from test preparation materials for students. 11. A system for training a query-response model comprising: a first machine learning module including at least one processing device, the machine learning module programmed to: train a first model using a first plurality of tuples each including text, a question, and an answer; process unstructured data using the first model to obtain a second plurality of tuples each including a question and an answer; and a second machine learning module programmed to train a second model using the second plurality of tuples, the second model being a natural language understanding (NLU) model. 12. The system of claim 11, wherein the second machine learning module is further programmed to cause the one or more processors to load the second model onto a consumer computing device. 13. The system of claim 12, wherein the consumer computing device is an in-vehicle infotainment (IVI) system mounted in a vehicle. 14. The system of claim 13, wherein the second machine learning module is further programmed to program the IVI system to receive a query, input the query to the second model, and output a response according to the second model. 15. The system of claim 13 wherein the second machine learning module is further programmed to program the IVI system, to input voice queries to the second model and output a response to the query according to the second model. 16. The system of claim 11, wherein the first model is a deep neural network (DNN) model. 17. The system of claim 11, wherein the second model is a deep neural network (DNN) model. 18. The system of claim 11, wherein the first machine learning module is further programmed to process the unstructured data using the first model by: pre-processing the unstructured data to identify a feature set from within the unstructured data; and inputting the feature set to the first model. 19. The system of claim 11, wherein the unstructured data comprises at least one of text and images. 20. The system of claim 11, wherein the first machine learning module is further programmed to derive the first plurality of tuples from test preparation materials for students.