Determining Preferential Device Behavior

The computer implemented method of claim 1, wherein the one or more sensors of the mobile device are each selected from the group consisting of a location sensor, motion sensor, magnetometer, light sensor, proximity sensor, and camera sensor.

The computer implemented method of claim 1, wherein the mobile device user data further includes one or more of time of events, location of events taken by the user, installed applications on the mobile device, or use of the installed applications.

The computer-implemented method of claim 1, further comprising:

• determining, by the mobile device, a truth reference from any one or more of the mobile device user data and one or more of the plurality classes that associated with the mobile device, wherein the server incorporates the truth reference into the decision tree.

The computer-implemented method of claim 4, further comprising:

• Determining, by the mobile device, a truth reference from a priori knowledge.

The computer-implemented method of claim 1, wherein the adding or changing the information presented to a user of the mobile device includes one or more of presenting suggestions or changing a presentation language.

The computer-implemented method of claim 1, wherein the machine learning process is selected form the group consisting of supervised learning and unsupervised learning.

The computer-implemented method of claim 7, wherein the supervised learning is inferring a function from labeled training data.

The computer-implemented method of claim 7, wherein the supervised learning is inferring a function from labeled training data.

The computer-implemented method of claim 1, wherein the plurality of classes can relate to any one or more of work, interests and geographical regions derived from the crowd-sourced data.

The non-transitory machine-readable medium of claim 11, wherein the one or more sensors of the mobile device are each selected from the group consisting of a location sensor, motion sensor, magnetometer, light sensor, proximity sensor, and camera sensor.

The non-transitory machine-readable medium of claim 11, wherein the mobile device user data further includes one or more of time of events, location of events taken by the user, installed applications on the mobile device, or use of the installed applications.

The non-transitory machine-readable medium of claim 11, further comprising:

• determining a truth reference from any one or more of the mobile device user data and one or more of the plurality classes that associated with the mobile device, wherein the set of one or more server computers incorporates the truth reference into the decision tree.

The non-transitory machine-readable medium of claim 14, further comprising:

• determining a truth reference from a priori knowledge.

The non-transitory machine-readable medium of claim 11, wherein the adding or changing the information presented to a user of the mobile device includes one or more of presenting suggestions or changing a presentation language.

The non-transitory machine-readable medium of claim 11, wherein the machine learning process is selected form the group consisting of supervised learning and unsupervised learning.

The non-transitory machine-readable medium of claim 17, wherein the supervised learning is inferring a function from labeled training data.

The non-transitory machine-readable medium of claim 17, wherein the supervised learning is inferring a function from labeled training data.

The non-transitory machine-readable medium of claim 11, wherein the plurality of classes can relate to any one or more of work, interests and geographical regions derived from the crowd-sourced data.