公开(公告)号：US11823015B2

公开(公告)日：2023-11-21

申请号：US16247510

申请日：2019-01-14

Applicant: AUTODESK, INC.

Inventor： Tovi Grossman ,  Benjamin Lafreniere ,  Xu Wang

IPC: G06N5/04 ,  G06N20/00 ,  G06F16/2457 ,  G06F9/451 ,  G06F9/48 ,  G06F11/34 ,  G06F17/18 ,  G06N7/01

CPC classification number: G06N20/00 ,  G06F9/453 ,  G06F9/4806 ,  G06F9/4881 ,  G06F11/3452 ,  G06F16/24578 ,  G06F17/18 ,  G06N5/04 ,  G06N7/01

Abstract: In various embodiments, a pattern-based recommendation subsystem automatically recommends workflows for software-based tasks. In operation, the pattern-based recommendation subsystem computes an expected distribution of frequencies across command patterns based on different distributions of frequencies across the command patterns. The expected distribution of frequencies is associated with a target user, and each different distribution of frequencies is associated with a different user. The pattern-based recommendation subsystem then applies a set of commands associated with the target user to a trained machine-learning model to determine a target distribution of weights applied to a set of tasks. Subsequently, the pattern-based recommendation subsystem determines a training item based on the expected distribution of frequencies and the target distribution of weights. The pattern-based recommendation subsystem generates a recommendation that specifies the training item. Finally, the pattern-based recommendation subsystem transmits the recommendation to a user to assist the user in performing a particular task.

公开(公告)号：US11803771B2

公开(公告)日：2023-10-31

申请号：US16247507

申请日：2019-01-14

Applicant: AUTODESK, INC.

Inventor： Tovi Grossman ,  Benjamin Lafreniere ,  Xu Wang

IPC: G06N7/01 ,  G06N5/04 ,  G06N20/00 ,  G06F16/2457 ,  G06F9/451 ,  G06F9/48 ,  G06F11/34 ,  G06F17/18

CPC classification number: G06N20/00 ,  G06F9/453 ,  G06F9/4806 ,  G06F9/4881 ,  G06F11/3452 ,  G06F16/24578 ,  G06F17/18 ,  G06N5/04 ,  G06N7/01

Abstract: In various embodiments, a task-based recommendation subsystem automatically recommends workflows for software-based tasks based on a trained machine-learning model that maps different sets of commands to different distributions of weights applied to a set of tasks. In operation, the task-based recommendation subsystem applies a first set of commands associated with a target user to the trained machine-learning model to determine a target distribution of weights applied to the set of tasks. The task-based recommendation subsystem then performs processing operation(s) based on at least two different distributions of weights applied to the set of tasks and the target distribution to determine a training item. Subsequently, the task-based recommendation subsystem generates a recommendation that specifies the training item. Finally, the task-based recommendation subsystem transmits the recommendation to a user to assist the user in performing a particular task.