Abstract: Aspects of the disclosure relate to enhanced dynamic training response output generation control systems with enhanced dynamic training response output determinations. A computing platform may receive, from the user device and in response to an initial dynamic training interface, a training request input. The computing platform may send, to an NLU engine, the training request input and commands directing the NLU engine to perform natural language understanding and processing on the training request input to determine a natural language result output. Using the natural language result output, the computing platform may determine third party data sources that correspond to the natural language result output, and may request source data from the third party data sources. Using the source data and the natural language result output, the computing platform may generate a dynamic training response output, and may direct the user device to cause display of the dynamic training response output.

Abstract: Aspects of the disclosure relate to enhanced dynamic training response output generation control systems with enhanced dynamic training response output determinations. A computing platform may receive, from the user device and in response to an initial dynamic training interface, a training request input. The computing platform may send, to an NLU engine, the training request input and commands directing the NLU engine to perform natural language understanding and processing on the training request input to determine a natural language result output. Using the natural language result output, the computing platform may determine third party data sources that correspond to the natural language result output, and may request source data from the third party data sources. Using the source data and the natural language result output, the computing platform may generate a dynamic training response output, and may direct the user device to cause display of the dynamic training response output.

Abstract: Aspects of the disclosure relate to enhanced dynamic training response output generation control systems with enhanced dynamic training response output determinations. A computing platform may receive, from the user device and in response to an initial dynamic training interface, a training request input. The computing platform may send, to an NLU engine, the training request input and commands directing the NLU engine to perform natural language understanding and processing on the training request input to determine a natural language result output. Using the natural language result output, the computing platform may determine third party data sources that correspond to the natural language result output, and may request source data from the third party data sources. Using the source data and the natural language result output, the computing platform may generate a dynamic training response output, and may direct the user device to cause display of the dynamic training response output.

Abstract: Aspects of the disclosure relate to enhanced dynamic training response output generation control systems with enhanced dynamic training response output determinations. A computing platform may receive, from the user device and in response to an initial dynamic training interface, a training request input. The computing platform may send, to an NLU engine, the training request input and commands directing the NLU engine to perform natural language understanding and processing on the training request input to determine a natural language result output. Using the natural language result output, the computing platform may determine third party data sources that correspond to the natural language result output, and may request source data from the third party data sources. Using the source data and the natural language result output, the computing platform may generate a dynamic training response output, and may direct the user device to cause display of the dynamic training response output.

Abstract: Disclosed herein is a customized recommendation system for home assessment, in which the system comprises at least one or more databases, a computing device, and a device comprising hardware, including a processor and memory. The server device may receive a request for information on a home of a user from the computing device. The server device may identify publicly available information for the home and customer information comprising information about one or more features of the home and materials used in the home by parsing the one or more databases. By further analyzing the customer information and the publicly available information, the server device may determine one or more tips for the home and generate a customized recommendation including the one or more tips for the home. The server device may transmit the customized recommendation to the computing device associated with the user.

Abstract: Disclosed herein is a customized recommendation system for home assessment, in which the system comprises at least one or more databases, a computing device, and a device comprising hardware, including a processor and memory. The server device may receive a request for information on a home of a user from the computing device. The server device may identify publicly available information for the home and customer information comprising information about one or more features of the home and materials used in the home by parsing the one or more databases. By further analyzing the customer information and the publicly available information, the server device may determine one or more tips for the home and generate a customized recommendation including the one or more tips for the home. The server device may transmit the customized recommendation to the computing device associated with the user.

Abstract: Aspects of the disclosure relate to enhanced dynamic training response output generation control systems with enhanced dynamic training response output determinations. A computing platform may receive, from the user device and in response to an initial dynamic training interface, a training request input. The computing platform may send, to an NLU engine, the training request input and commands directing the NLU engine to perform natural language understanding and processing on the training request input to determine a natural language result output. Using the natural language result output, the computing platform may determine third party data sources that correspond to the natural language result output, and may request source data from the third party data sources. Using the source data and the natural language result output, the computing platform may generate a dynamic training response output, and may direct the user device to cause display of the dynamic training response output.

Abstract: Aspects of the disclosure relate to enhanced dynamic training response output generation control systems with enhanced dynamic training response output determinations. A computing platform may receive, from the user device and in response to an initial dynamic training interface, a training request input. The computing platform may send, to an NLU engine, the training request input and commands directing the NLU engine to perform natural language understanding and processing on the training request input to determine a natural language result output. Using the natural language result output, the computing platform may determine third party data sources that correspond to the natural language result output, and may request source data from the third party data sources. Using the source data and the natural language result output, the computing platform may generate a dynamic training response output, and may direct the user device to cause display of the dynamic training response output.

Abstract: Disclosed herein is a customized recommendation system for home assessment, in which the system comprises one or more databases, a first computing device, and a server computer comprising hardware, including a processor and memory. The server computer may receive a request for information on a home of the user from the first computing device. The server computer may identify publicly available information for the home and customer information comprising information about one or more features of the home and materials used in the home by parsing the one or more databases. By further analyzing the customer information and the publicly available information, the server computer may determine one or more tips for the home and generate a customized recommendation for the user including the one or more tips for the home. The server computer may then transmit the customized recommendation to the first computing device associated with the user.

Abstract: Aspects of the disclosure relate to enhanced dynamic training response output generation control systems with enhanced dynamic training response output determinations. A computing platform may receive, from the user device and in response to an initial dynamic training interface, a training request input. The computing platform may send, to an NLU engine, the training request input and commands directing the NLU engine to perform natural language understanding and processing on the training request input to determine a natural language result output. Using the natural language result output, the computing platform may determine third party data sources that correspond to the natural language result output, and may request source data from the third party data sources. Using the source data and the natural language result output, the computing platform may generate a dynamic training response output, and may direct the user device to cause display of the dynamic training response output.

Abstract: Disclosed herein is a customized recommendation system for home assessment, in which the system comprises one or more databases, a first computing device, and a server computer comprising hardware, including a processor and memory. The server computer may receive a request for information on a home of the user from the first computing device. The server computer may identify publicly available information for the home and customer information comprising information about one or more features of the home and materials used in the home by parsing the one or more databases. By further analyzing the customer information and the publicly available information, the server computer may determine one or more tips for the home and generate a customized recommendation for the user including the one or more tips for the home. The server computer may then transmit the customized recommendation to the first computing device associated with the user.