Personalized contextual suggestion engine
First Claim
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1. A method of generating user-specific contextual association rules, the method comprising:
- receiving signal information describing an event from a computing device associated with a user;
classifying the event as non-routine;
building a data vector for the event by assigning a value to a plurality of components of the data vector using information derived from the signal information, wherein a component of the data vector indicates that the event is non- routine;
generating an association rule for a first type of event using the data vector as an input to a machine learning algorithm that generates the association rule as an output, the association rule assigning a probability that the user is interested in a contextual suggestion given a specific non-routine user context;
storing the association rule;
receiving additional signal information from a device associated with the user;
determining a present context of the user from the additional signal information;
determining that the present context is the specific non-routine user context; and
triggering the contextual suggestion.
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Abstract
Aspects of the technology described herein provide a more efficient user interface by providing suggestions that are tailored to a specific user'"'"'s interests. The suggestions may be provided by a personal assistant or some other application running on a user'"'"'s computing device. A goal of the technology described herein is to provide relevant suggestions when the user can and actually wants to use them. The suggestions are designed to provide information or services the user wants to use.
22 Citations
21 Claims
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1. A method of generating user-specific contextual association rules, the method comprising:
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receiving signal information describing an event from a computing device associated with a user; classifying the event as non-routine; building a data vector for the event by assigning a value to a plurality of components of the data vector using information derived from the signal information, wherein a component of the data vector indicates that the event is non- routine; generating an association rule for a first type of event using the data vector as an input to a machine learning algorithm that generates the association rule as an output, the association rule assigning a probability that the user is interested in a contextual suggestion given a specific non-routine user context; storing the association rule; receiving additional signal information from a device associated with the user; determining a present context of the user from the additional signal information; determining that the present context is the specific non-routine user context; and triggering the contextual suggestion. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. One or more computer-readable storage media comprising instructions, which when executed by a computing device, cause the computing device to perform a method of generating user-specific contextual association rules, the method comprising:
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receiving signal information describing an event from a computing device associated with a user; classifying the event as non-routine; building a data vector for the event by assigning a value to a plurality of components of the data vector using information derived from the signal information, wherein a component of the data vector indicates that the event is non- routine; generating an association rule for a first type of event using the data vector as an input to a machine learning algorithm that generates the association rule as an output, the association rule assigning a probability that the user is interested in a contextual suggestion given a specific non-routine user context; storing the association rule; receiving additional signal information from a device associated with the user; determining a present context of the user from the additional signal information; determining that the present context is the specific non-routine user context; and triggering the contextual suggestion. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A method of generating user-specific contextual association rules, the method comprising:
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receiving signal information describing an event from a computing device associated with a user; classifying the event as non-routine; building a data vector for the event by assigning a value to a plurality of components of the data vector using information derived from the signal information, wherein a component of the data vector indicates that the event is non- routine; generating an association rule for a first type of event using the data vector as an input to a machine learning algorithm that generates the association rule as an output, the association rule assigning a probability that the user is interested in a contextual suggestion given a specific non-routine user context; storing the association rule; receiving additional signal information from a device associated with the user; triggering the contextual suggestion in response to determining the additional signal information matches the specific non-routine user context. - View Dependent Claims (18, 19, 20, 21)
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Specification