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US 12,587,707 B2Granted
Session type classification for modeling
AssigneeDisney Enterprises, Inc.
InventorFabian Gallusser
Life of this patent
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Abstract
In some embodiments, a method receives data for sessions that is generated based on use of a service. The data for a session includes session characteristic values for a set of session characteristics that is associated with the session. The method classifies the sessions into a plurality of session types to generate feature values for the plurality of session types. The session characteristic values for the session are compared to a set of reference characteristics for respective session types in the plurality of sessions to perform the classifying. The feature values are analyzed for the plurality of session types using a model to generate a prediction for the service and the is outputted prediction.
Claims20
A method comprising:
- receiving, by a computing device, data for sessions that is generated based on use of a service;
- determining, by the computing device, a plurality of session characteristic values for a plurality of session characteristics based on a start of respective sessions and an end of respective sessions, wherein events that occur in between the start and the end are used to determine respective session characteristic values in the plurality of session characteristic values for the respective sessions, and wherein, for each session, events for the respective session are used to generate the plurality of session characteristic values for the respective session;
- classifying, by the computing device, the sessions into a plurality of session types, wherein the plurality of session characteristic values for the sessions is compared to a plurality of reference characteristic values for respective session types in the plurality of session types to perform the classifying, wherein at least two different session types are associated with different reference characteristic values;
- generating, by the computing device, feature values based on a count of occurrences for session types in the plurality of session types based on sessions being classified in respective session types, wherein an occurrence of the session is aggregated and increases a count for an updated feature value upon classifying the session in the session type and the updated feature value is the count;
- analyzing, by the computing device, the feature values for the plurality of session types using a model to generate a prediction for the service, wherein the count for the updated feature value is input into the model;
- and
- outputting, by the computing device, the prediction.
4 more elements
A non-transitory computer-readable storage medium having stored thereon computer executable instructions, which when executed by a computing device, cause the computing device to be operable for:
- receiving data for sessions that is generated based on use of a service;
- determining a plurality of session characteristic values for a plurality of session characteristics based on a start of respective sessions and an end of respective sessions, wherein events that occur in between the start and the end are used to determine respective session characteristic values in the plurality of session characteristic values for the respective sessions, and wherein, for each session, events for the respective session are used to generate the plurality of session characteristic values for the respective session;
- classifying the sessions into a plurality of session types, wherein the plurality of session characteristic values for the sessions is compared to a plurality of reference characteristic values for respective session types in the plurality of session types to perform the classifying, wherein at least two different session types are associated with different reference characteristic values;
- generating feature values based on a count of occurrences for session types in the plurality of session types based on sessions being classified in respective session types, wherein an occurrence of the session is aggregated and increases a count for an updated feature value upon classifying the session in the session type and the updated feature value is the count;
- analyzing the feature values for the plurality of session types using a model to generate a prediction for the service, wherein the count for the updated feature value is input into the model;
- and
- outputting the prediction.
4 more elements
An apparatus comprising:
- one or more computer processors;
- and
- a computer-readable storage medium comprising instructions for controlling the one or more computer processors to be operable for:
- receiving data for sessions that is generated based on use of a service;
- determining a plurality of session characteristic values for a plurality of session characteristics based on a start of respective sessions and an end of respective sessions, wherein events that occur in between the start and the end are used to determine respective session characteristic values in the plurality of session characteristic values for the respective sessions, and wherein, for each session, events for the respective session are used to generate the plurality of session characteristic values for the respective session;
- classifying the sessions into a plurality of session types, wherein the plurality of session characteristic values for the sessions is compared to a plurality of reference characteristic values for respective session types in the plurality of session types to perform the classifying, wherein at least two different session types are associated with different reference characteristic values;
- generating feature values based on a count of occurrences for session types in the plurality of session types based on sessions being classified in respective session types, wherein an occurrence of the session is aggregated and increases a count for an updated feature value upon classifying the session in the session type and the updated feature value is the count;
- analyzing the feature values for the plurality of session types using a model to generate a prediction for the service, wherein the count for the updated feature value is input into the model;
- and
- outputting the prediction.
7 more elements
Dependencies are inferred from the claim text.