SYSTEMS AND METHODS FOR ANALYZING AND SEGMENTING AUTOMATION SEQUENCES
A system and method for segmenting or dividing a series of computer-based actions, for example into sentences, may provide a sequence of subsets of the series of actions to a neural network using a sliding window, and divide or segment the series actions into segments at points where the loss of the neural network is above a threshold. The dividing may include, for each of a sequence of computer-based actions within a sliding window determining if the sequence when provided to the neural network corresponds to a loss above or equal to a threshold, and if so, determining that an action in the sequence of actions within the sliding window should not be part of a segment or sentence being created.
1 . A method for segmenting a series of computer-based actions, comprising:
using a computer processor, providing a sequence of subsets of the series of computer-based actions to a neural network using a sliding window; and
dividing the series of computer-based actions into segments at points where the loss of the neural network is above a threshold.
2 . The method of claim 1 , wherein dividing the series of computer-based actions into segments at points where the loss of the neural network is above a threshold comprises:
for each of a sequence of computer-based actions within a sliding window determining if the sequence when provided to the neural network corresponds to a loss above or equal to a threshold; and
if the sequence when provided to the neural network corresponds to a loss above or equal to a threshold, determining that an action in the sequence of actions within the sliding window should not be part of a segment being created.
3 . The method of claim 2 , wherein determining that an action defined by the sliding window should not be part of a segment being created comprises removing the last action in the sequence of actions within a sliding window from a list.
4 . The method of claim 1 where the neural network is an autoencoder.
5 . The method of claim 1 where the threshold is set as a percentile of losses.
6 . The method of claim 1 , wherein the neural network is trained using the sequence of subsets.
7 . The method of claim 1 , comprising providing to a user a next suggested action.
8 . A system for segmenting a series of computer-based actions, comprising:
a memory; and
a processor configured to:
provide a sequence of subsets of the series of computer-based actions to a neural network using a sliding window; and
divide the series of computer-based actions into segments at points where the loss of the neural network is above a threshold.
9 . The system of claim 8 , wherein dividing the series of computer-based actions into segments at points where the loss of the neural network is above a threshold comprises:
for each of a sequence of computer-based actions within a sliding window determining if the sequence when provided to the neural network corresponds to a loss above or equal to a threshold; and
if the sequence when provided to the neural network corresponds to a loss above or equal to a threshold, determining that an action in the sequence of actions within the sliding window should not be part of a segment being created.
10 . The system of claim 9 , wherein determining that an action defined by the sliding window should not be part of a segment being created comprises removing the last action in the sequence of actions within a sliding window from a list.
11 . The system of claim 8 where the neural network is an autoencoder.
12 . The system of claim 8 where the threshold is set as a percentile of losses.
13 . The system of claim 8 , wherein the neural network is trained using the sequence of subsets.
14 . The system of claim 8 , wherein the processor is configured to provide to a user a next suggested action.
15 . A method for forming a series of computer-based actions into sentences, the method comprising:
using a computer processor, providing series of windows each comprising computer-based actions to a neural network; and
forming sentences of computer-based actions based on the loss of the windows when input to a neural network.
16 . The method of claim 15 , wherein forming sentences comprises:
for each window determining if the window when provided to the neural network corresponds to a loss above or equal to a threshold; and
if loss is above or equal to a threshold, determining that an action in the window should not be part of a sentence being created.
17 . The method of claim 16 , wherein determining that an action in the window should not be part of a sentence comprises removing the last action in a sequence of actions within the window from a list.
18 . The method of claim 15 where the neural network is an autoencoder.
19 . The method of claim 15 where the threshold is based on a percentile of losses.
20 . The method of claim 15 , wherein the neural network is trained using the sequence of subsets.