A system comprising: one or more processors; and one or more non-transitory computer-readable media configured to maintain executable instructions, which, when executed by the one or more processors, configure the one or more processors to perform operations comprising: receiving from a device , speech sound patterns corresponding to a voice input related to equipment ; determining an identity of a person associated with the device ; identifying the equipment related to the voice input ; using an emotion-recognition machine learning model customized for the identified person to determine whether an emotion classification associated with the voice input is classified as normal or non-normal; providing at least one of the received speech sound patterns or a text conversion of the speech sound patterns , along with an equipment history of the identified equipment and an indication of the emotion classification as input to one or more additional machine learning models to determine a response to the voice input ; composing a text instruction for responding to the voice input based on the determined response to the voice input ; converting the text instruction to an audio speech file; and sending the audio speech file to the device for presentation on the device . one or more processors; and one or more non-transitory computer-readable media configured to maintain executable instructions, which, when executed by the one or more processors, configure the one or more processors to perform operations comprising: receiving from a device , speech sound patterns corresponding to a voice input related to equipment ; determining an identity of a person associated with the device ; identifying the equipment related to the voice input ; using an emotion-recognition machine learning model customized for the identified person to determine whether an emotion classification associated with the voice input is classified as normal or non-normal; providing at least one of the received speech sound patterns or a text conversion of the speech sound patterns , along with an equipment history of the identified equipment and an indication of the emotion classification as input to one or more additional machine learning models to determine a response to the voice input ; composing a text instruction for responding to the voice input based on the determined response to the voice input ; converting the text instruction to an audio speech file; and sending the audio speech file to the device for presentation on the device . receiving from a device , speech sound patterns corresponding to a voice input related to equipment ; determining an identity of a person associated with the device ; identifying the equipment related to the voice input ; using an emotion-recognition machine learning model customized for the identified person to determine whether an emotion classification associated with the voice input is classified as normal or non-normal; providing at least one of the received speech sound patterns or a text conversion of the speech sound patterns , along with an equipment history of the identified equipment and an indication of the emotion classification as input to one or more additional machine learning models to determine a response to the voice input ; composing a text instruction for responding to the voice input based on the determined response to the voice input ; converting the text instruction to an audio speech file; and sending the audio speech file to the device for presentation on the device . <EOS>