A method for initiating a conversation with a user of a digital personal assistant performed by the digital personal assistant implemented on at least one mobile computing device which comprises a touch screen, the method comprising: obtaining one or more first signals related to an availability of the user of the digital personal assistant from one or more first sensors or first software applications, wherein the first signal/s comprise one or more of: calendar information associated with the user; daily habits information associated with the user; and information associated with a current activity of the user, and the current activity of the user is determined based on microphone input, application, input/output, network, and/or operating system activity on the computing device; obtaining one or more second signals related to a mental or emotional state of the user from one or more second sensors; determining whether a particular time is an appropriate time to attempt to initiate a conversation of the digital personal assistant with the user based at least on the first signal/s and the second signal/s; and in response to a determination that the particular time is an appropriate time to attempt to initiate the conversation of the digital personal assistant with the user, attempting to initiate the conversation of the digital personal assistant with the user, wherein the second signals comprise one or more of a speed at which a user taps or swipes the touch screen, a rate of errors in keystrokes or gestures, an amount of pressure applied by the user while using the touchscreen, and accelerometer, compass, and/or gyroscope output, wherein a machine learner is used to identify and weight user signals, including the second signals, for identifying the user's mental and/or emotional state, wherein the users signals and input concerning the user's mental and/or emotional state are provided as training data to the machine learner, wherein the user signals are collected as well as intermittent input concerning the user's own mental and/or emotional state. obtaining one or more first signals related to an availability of the user of the digital personal assistant from one or more first sensors or first software applications, wherein the first signal/s comprise one or more of: calendar information associated with the user; daily habits information associated with the user; and information associated with a current activity of the user, and the current activity of the user is determined based on microphone input, application, input/output, network, and/or operating system activity on the computing device; obtaining one or more second signals related to a mental or emotional state of the user from one or more second sensors; determining whether a particular time is an appropriate time to attempt to initiate a conversation of the digital personal assistant with the user based at least on the first signal/s and the second signal/s; and in response to a determination that the particular time is an appropriate time to attempt to initiate the conversation of the digital personal assistant with the user, attempting to initiate the conversation of the digital personal assistant with the user, wherein the second signals comprise one or more of a speed at which a user taps or swipes the touch screen, a rate of errors in keystrokes or gestures, an amount of pressure applied by the user while using the touchscreen, and accelerometer, compass, and/or gyroscope output, wherein a machine learner is used to identify and weight user signals, including the second signals, for identifying the user's mental and/or emotional state, wherein the users signals and input concerning the user's mental and/or emotional state are provided as training data to the machine learner, wherein the user signals are collected as well as intermittent input concerning the user's own mental and/or emotional state.