Back to results
US 2026/0073360 A1Published application
Machine-Based Selection or Modification of an Appointment Duration
AssigneeOracle International Corporation
InventorsJennifer Darmour, Orry Soegiono, Micah Lawler Sonderman
Life of this patent
Estimating the term…
Abstract
Techniques are disclosed that determine time allotments for appointments based on activities to be performed. A system determines a time allotment for an appointment by applying a machine learning model trained to calculate a duration for performing a set of activities involved in the appointment. The machine learning model may calculate the duration based on the set of activities, the timing of the appointment, and attributes of the provider.
Claims20
One or more non-transitory computer readable media comprising instructions which, when executed by one or more hardware processors, cause performance of operations comprising:
- generating a training data set for a machine learning (ML) model that estimates appointment duration based on a corresponding set of activities at least by:
- determining a first duration of a first prior appointment for completing a first combination of activities;
- including in a training data set the first combination of activities in association with the first duration;
- training the ML model based on the training data set;
- receiving a message regarding a particular appointment for a user;
- based on the message, determining a second combination of activities to be completed during the particular appointment;
- applying the ML model to the second combination of activities to determine a predicted duration for the particular appointment;
- generating a recommendation to select a first allotted time period for the particular appointment based on predicted duration determined by the ML model;
- subsequent to completion of the particular appointment, determining a detected duration of the particular appointment;
- and
- retraining the ML model based on the second combination of activities in association with the detected duration of the particular appointment.
8 more elements
A method comprising:
- generating a training data set for a machine learning (ML) model that estimates appointment duration based on a corresponding set of activities at least by:
- determining a first duration of a first prior appointment for completing a first combination of activities;
- including in the training data set the first combination of activities in association with the first duration;
- training the ML model based on the training data set;
- receiving a message regarding a particular appointment for a user;
- based on the message, determining a second combination of activities to be completed during the particular appointment;
- applying the ML model to the second combination of activities to determine a predicted duration for the particular appointment;
- generating a recommendation to select a first allotted time period for the particular appointment based on predicted duration determined by the ML model;
- subsequent to completion of the particular appointment, determining a detected duration of the particular appointment;
- and
- retraining the ML model based on the second combination of activities in association with the detected duration of the particular appointment.
8 more elements
A system comprising:
- at least one device including a hardware processor;
- the system being configured to perform operations comprising:
- generating a training data set for a machine learning (ML) model that estimates appointment duration based on a corresponding set of activities at least by:
- determining a first duration of a first prior appointment for completing a first combination of activities;
- including in a training data set the first combination of activities in association with the first duration;
- training the machine learning model based on the training data set;
- receiving a message regarding a particular appointment for a user;
- based on the message, determining a second combination of activities to be completed during the particular appointment;
- applying the ML model to the second combination of activities to determine a predicted duration for the particular appointment;
- generating a recommendation to select a first allotted time period for the particular appointment based on predicted duration determined by the ML model;
- subsequent to completion of the particular appointment, determining a detected duration of the particular appointment;
- and
- retraining the ML model based on the second combination of activities in association with the detected duration of the particular appointment.
10 more elements
Dependencies are inferred from the claim text.