US 11,636,439 B2 · Techniques to apply machine learning to schedule events of interest

Abstract

Techniques to apply machine learning to schedule events of interest for a device user. As described herein, a typical device user is inundated with information suggesting activities and other things to do. Using these techniques, the information is categorized according to the activity and those activities that are most likely to be engaged in by the device user are recommended to the device user (via their device) as events of interest. If the device user selects an event of interest, the device is updated to reflect that selection. For instance, a calendar application may be updated to include an event description at the event's date and time. Other embodiments are described and claimed.

A device, comprising:

a processing circuit;
a computer memory storing information from a number of computer applications executed by the processing circuit;
and
logic stored in the computer memory and executed on the processing circuit, wherein the logic is operative to cause the processing circuit to:
interpret raw data from the number of computer applications using a natural language processing mechanism or text summarization techniques;
obtain user activity data and user availability data from the interpreted raw data;
store the user activity data and the user availability data in the computer memory as database information;
access, from the computer memory, the database information including records corresponding to user activity data, user availability data, and events of interest in a geographic area, wherein the database information is provided by the number of applications;
process the database information comprising the records corresponding to the events in the geographic area;
train a machine learning model having features based in part on user preference data in the records in the database information, the user preference data being based on user availability data and user activity data associated with various applications, the user activity data comprising historical events engaged in by a device user, each of the historic events comprising a historical activity attribute and a historical time attribute, the records comprising time attributes and activity attributes for events, and the features comprising a feature value for the user availability data and the user activity data from the user preference data and a likelihood that a user will engage in an event with a time attribute corresponding to at least one of the historical time attributes of the historical events;
apply the trained machine learning model to the accessed database information related to the user activity data, the user availability data and the events of interest in the geographic area;
identify records having events of interest, the records based on receipt of communications comprising information about future activities, based upon values provided by the trained machine learning model by selecting the events of interest that include time attributes that are at future times, each of the events of interest comprising attributes that map to a value exceeding a threshold value and to corresponding attributes of at least a portion of the user preference data, wherein the identifying further includes selecting the attributes that map to the value exceeding the threshold value correspond to a likelihood determined by the machine learning model that the device user will engage in the event of interest, and the likelihood related to the selected events of interest is based on determining at least one time attribute and at least one activity attribute of the selected events of interest exceeding the threshold value, and both of:
the event of interest having an activity attribute similar to a historical activity attribute of at least one of the historical events engaged in by the device user, the at least one of the historical events having a historical time attribute corresponding with the time attribute of the event of interest;
and
the event of interest having a time attribute similar to a historical time attribute of at least one of the historical events engaged in by the device user, the at least one of the historical events having a historical activity attribute corresponding with the activity attribute of the event of interest;
generate recommendation data for one or more events of interest in the geographic area in the identified records, generation of recommendation data for at least one of the one or more events of interest to cause the processing circuit to:
store a contact in a contacts portion for an event leader;
and
instruct a communication application to generate a communication directed towards the event leader;
display, on a display device of the device, the recommendation data and a selectable option for each event of the one or more events;
and
store one or more entries in a calendar application executed by the processing circuit based upon one or more user selections of one or more selectable options on the displayed recommendation data.

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Techniques to apply machine learning to schedule events of interest

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Techniques to apply machine learning to schedule events of interest

Life of this patent

Abstract

Claims17