US 12,744,785 B2 · Prioritizing operations over content objects of a content management system

Abstract

Content object operations over content objects of a content management system are prioritized to be performed immediately, or at a later time. The immediate scheduling of an operation is determined by policies, rules, and/or predictive model outcomes. The determination for later time scheduling is based on analysis of a history of events on content objects. If the content object operation is deemed to be at least potentially delayable to a later time, then a scheduling model is consulted to determine an urgency of performing the content object operation on the content object. The urgency value resulting from consulting the scheduling model is combined with then-current resource availability to determine a timeframe for performance of the content object operation on the content object relative to other entries in a continuously updated list of to-be-performed operations. The performance of the content object operation on the content object is initiated in due course.

Claims21

Dependent claims

A computer-implemented method for prioritizing operations over content objects of a content management system, the method comprising:

processing, by executing a continuously updated machine learning model, one or more history events of a content object of the content management system to predict a first timeframe at or by which the content object will be accessed in the future;
determining, by the continuously updated machine learning model, whether to schedule a content object operation at or by a first time or delay scheduling of the content object operation at or by a second time that is later in time than the first time;
when the continuously updated machine learning model selects scheduling the content object operation to be executed at or by the second time, executing a scheduling model of the continuously updated machine learning model to determine a second timeframe for executing the content object operation on the content object, the second timeframe being determined based at least in part on a scoring model used by the scheduling model;
updating a data structure in memory with an entry indicative of the second timeframe at or by which the content object operation is to be executed based at least in part on resource availability;
and
executing the content object operation according to the second timeframe that was determined by the scheduling model.

3 more elements

A system comprising:

a storage medium having stored thereon a sequence of instructions;
and
one or more processors that execute the sequence of instructions to cause the one or more processors to perform a set of acts, the set of acts comprising:
processing, by executing a continuously updated machine learning model, one or more history events of a content object of the content management system to predict a first timeframe at or by which the content object will be accessed in the future;
determining, by the continuously updated machine learning model, whether to schedule a content object operation at or by a first time or delay scheduling of the content object operation at or by a second time that is later in time than the first time;
when the continuously updated machine learning model selects scheduling the content object operation to be executed at or by the second time, executing a scheduling model of the continuously updated machine learning model to determine a second timeframe for executing the content object operation on the content object, the second timeframe being determined based at least in part on a scoring model used by the scheduling model;
updating a data structure in memory with an entry indicative of the second timeframe at or by which the content object operation is to be executed based at least in part on resource availability;
and
executing the content object operation according to the second timeframe that was determined by the scheduling model.

6 more elements

A computer program product embodied in a non-transitory computer readable medium and having stored thereon a sequence of instructions which, when executed by a processor, causes the processor to execute a set of acts, the set of acts comprising:

processing, by executing a continuously updated machine learning model, one or more history events of a content object of the content management system to predict a first timeframe at or by which the content object will be accessed in the future;
determining, by the continuously updated machine learning model, whether to schedule a content object operation at or by a first time or delay scheduling of the content object operation at or by a second time that is later in time than the first time;
when the continuously updated machine learning model selects scheduling the content object operation to be executed at or by the second time, executing a scheduling model of the continuously updated machine learning model to determine a second timeframe for executing the content object operation on the content object, the second timeframe being determined based at least in part on a scoring model used by the scheduling model;
and
updating a data structure in memory with an entry indicative of the second timeframe at or by which the content object operation is to be executed based at least in part on resource availability;
and
executing the content object operation according to the second timeframe that was determined by the scheduling model.

4 more elements

Dependencies are inferred from the claim text.

Prioritizing operations over content objects of a content management system

Life of this patent

Prioritizing operations over content objects of a content management system

Life of this patent

Abstract

Claims21