Predicting the future state of a mobile device user
First Claim
1. A method comprising:
- by one or more computing devices, receiving first data indicating a current mobile-device usage by a user;
by one or more computing devices, accessing second data associated with past user states of the user, wherein each past user state corresponds to a time and indicates a temporal, spatial, or modal accessibility of the user;
by one or more computing devices, determining a future user state of the user at a future time, wherein;
the determination is based on a weighted set of predictor functions, wherein each predictor function comprises a machine-learned algorithm trained for a particular user state;
each predictor function calculates a probability of the particular user state based on the first data and the past user states of the second data, each of the past user states being weighted by a decay factor based on the corresponding time; and
one of the particular user states is selected as the future user state; and
by one or more computing devices, adapting the operation of a mobile device of the user at the future time based on the future user state, wherein adapting the operation comprises altering characteristics of third data sent to the mobile device of the user.
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Abstract
In one embodiment, a method includes receiving first data indicating a current usage of a mobile device by a user. The method includes accessing second data associated with past user states of the user, where each user state corresponds to a temporal, spatial, or modal accessibility of the user. A future user state of the user is determined based on a weighted set of predictor functions, each predictor function comprising a machine-learned algorithm for a particular user state. The probability of each user state is based on past user states of the second data, wherein each past user state is weighted by a decay factor based on the corresponding time. One of the user states is selected as the future user state, and the operation of the mobile device is adapted at a future time to account for that future user state.
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Citations
20 Claims
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1. A method comprising:
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by one or more computing devices, receiving first data indicating a current mobile-device usage by a user; by one or more computing devices, accessing second data associated with past user states of the user, wherein each past user state corresponds to a time and indicates a temporal, spatial, or modal accessibility of the user; by one or more computing devices, determining a future user state of the user at a future time, wherein; the determination is based on a weighted set of predictor functions, wherein each predictor function comprises a machine-learned algorithm trained for a particular user state; each predictor function calculates a probability of the particular user state based on the first data and the past user states of the second data, each of the past user states being weighted by a decay factor based on the corresponding time; and one of the particular user states is selected as the future user state; and by one or more computing devices, adapting the operation of a mobile device of the user at the future time based on the future user state, wherein adapting the operation comprises altering characteristics of third data sent to the mobile device of the user. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:
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receive first data indicating a current mobile-device usage by a user; access second data associated with past user states of the user, wherein each past user state corresponds to a time and indicates a temporal, spatial, or modal accessibility of the user; determine a future user state of the user at a future time, wherein; the determination is based on a weighted set of predictor functions, wherein each predictor function comprises a machine-learned algorithm trained for a particular user state; each predictor function calculates a probability of the particular user state based on the first data and the past user states of the second data, each of the past user states being weighted by a decay factor based on the corresponding time; and one of the particular user states is selected as the future user state; and adapt the operation of a mobile device of the user at the future time based on the future user state, wherein adapting the operation comprises altering characteristics of third data sent to the mobile device of the user. - View Dependent Claims (14, 15, 16, 17)
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18. A system comprising:
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one or more processors; and a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to; receive first data indicating a current mobile-device usage by a user; access second data associated with past user states of the user, wherein each past user state corresponds to a time and indicates a temporal, spatial, or modal accessibility of the user; determine a future user state of the user at a future time, wherein; the determination is based on a weighted set of predictor functions, wherein each predictor function comprises a machine-learned algorithm trained for a particular user state; each predictor function calculates a probability of the particular user state based on the first data and the past user states of the second data, each of the past user states being weighted by a decay factor based on the corresponding time; and one of the particular user states is selected as the future user state; and adapt the operation of a mobile device of the user at the future time based on the future user state, wherein adapting the operation comprises altering characteristics of third data sent to the mobile device of the user. - View Dependent Claims (19, 20)
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Specification