Written-modality prosody subsystem in a natural language understanding (nlu) framework

Patent metadata

Field	Value
Publication number	US-2019294676-A1
Application number	US-201916298764-A
Country	US
Kind code	A1
Filing date	Mar 11, 2019
Priority date	Mar 23, 2018
Publication date	Sep 26, 2019
Grant date	—

Abstract

Present embodiment include a prosody subsystem of a natural language understanding (NLU) framework that is designed to analyze collections of written messages for various prosodic cues to break down the collection into a suitable level of granularity (e.g., into episodes, sessions, segments, utterances, and/or intent segments) for consumption by other components of the NLU framework, enabling operation of the NLU framework. These prosodic cues may include, for example, source prosodic cues that are based on the author and the conversation channel associated with each message, temporal prosodic cues that are based on a respective time associated with each message, and/or written prosodic cues that are based on the content of each message. For example, to improve the domain specificity of the agent automation system, intent segments extracted by the prosody subsystem may be consumed by a training process for a ML-based structure subsystem of the NLU framework.

First claim

What is claimed is:

1 . An agent automation system, comprising:
a memory configured to store a conversation log and natural language understanding (NLU) framework including a prosody subsystem; and a processor configured to execute instructions of the NLU framework to cause the agent automation system to perform actions comprising:
processing, via the prosody subsystem, the conversation log based on prosodic cues to divide the conversation log into conversation channel groups, sessions, conversation segments, utterances, intent segments, or a combination thereof, consumed by another portion of the NLU framework.

2 . The system of claim 1 , wherein, to process the conversation log, the processor is configured to execute the instructions of the NLU framework to cause the agent automation system to perform actions comprising:
dividing the conversation log into the conversation channel groups, dividing the conversation channel groups into the sessions, dividing the sessions into the conversation segments, and dividing the conversation segments into the utterances based on metadata prosodic cues.

3 . The system of claim 2 , wherein the metadata prosodic cues comprise a temporal prosody cue that is based on a respective time associated with each message of the conversation log.

4 . The system of claim 3 , wherein the temporal prosody cue comprises a time gap between the respective times associated with each message of the conversation log.

5 . The system of claim 1 , wherein, to process the conversation log, the processor is configured to execute the instructions of the NLU framework to cause the agent automation system to perform actions comprising:
analyzing the utterances for written prosodic cues; and dividing the utterances into the intent segments based on the written prosodic cues.

6 . The system of claim 5 , wherein the written prosodic cues comprise punctuation, emojis, emphasis, or linguistic structure.

7 . The system of claim 1 , wherein the prosodic cues comprise an interrupt, a change in topic, a change in context, or a combination thereof.

8 . The system of claim 1 , wherein the processor is configured to execute the instructions of the NLU framework to cause the agent automation system to perform actions comprising:
providing the intent segments as inputs to a training process for a machine-learning (ML)-based parser of the NLU framework, wherein, within the training process, the intent segments are used to generate a plurality of utterance trees, and a model of the ML-based parser is updated in response to identifying a quorum within the plurality of utterance trees.

9 . The system of claim 1 , wherein the processor is configured to execute the instructions of the NLU framework to cause the agent automation system to perform actions comprising:
providing the utterances as inputs to a training process for a vocabulary subsystem of the NLU framework, wherein, within the training process, the utterances are used to generate a plurality of word vectors of a refined word vector distribution model that replaces a word vector distribution model of the vocabulary subsystem.

10 . The system of claim 1 , wherein the processor is configured to execute the instructions of the NLU framework to cause the agent automation system to perform actions comprising:
providing the intent segments as inputs to a semantic mining pipeline of the NLU framework, wherein, within the semantic mining pipeline, a plurality of word vectors are generated from the intent segments and clustered together to construct an intent/entity model of the NLU framework.

11 . The system of claim 1 , wherein the processor is configured to execute the instructions of the NLU framework to cause the agent automation system to perform actions comprising:
providing the sessions, the segments, or a combination thereof, as inputs to a persona of a reasoning agent/behavior engine (RA/BE) of the NLU framework, wherein RA/BE is configured to generate episodic context information based on each of the sessions, the segments, or the combination thereof.

12 . The system of claim 1 , wherein the processor is configured to execute the instructions of the NLU framework to cause the agent automation system to perform actions comprising:
receiving, from a persona of a RA/BE of the NLU framework, a new message that is part of a conversation between the persona and a user; providing a first indication to the persona of the RA/BE in response to determining that the conversation is a continuation of a previous episode of conversation between the persona and the user; and providing a second indication to the persona of the RA/BE in response to determining that the conversation is a new conversation episode.

13 . A method of operating a prosody subsystem of a natural language understanding (NLU) framework, comprising:
dividing a conversation log comprising plurality of messages into a plurality of conversation channel groups based on a first set of prosodic cues; dividing each of the plurality of conversation channel groups into a plurality of sessions based on a second set of prosodic cues; dividing each of the plurality of sessions into a plurality of conversation segments based on a third set of prosodic cues; dividing each of the plurality of conversation segments into a plurality of utterances based on a fourth set of prosodic cues; dividing each of the plurality of utterances into a plurality of intent segments based on a fifth set of prosodic cues; and providing the plurality of intent segments, the plurality of utterances, the plurality of conversation segments, or the plurality of sessions, or a combination thereof, as inputs to processes of the NLU framework.

14 . The method of claim 13 , wherein the first set of prosodic cues comprises metadata prosodic cues that are based on a conversation channel associated with each of the plurality of messages of the conversation log, and wherein the second of prosodic cues and third of prosodic cues comprise temporal cues that are based on a time associated with each of the plurality of messages of the conversation log.

15 . The method of claim 13 , wherein the fourth set of prosodic cues comprise metadata prosodic cues, and wherein each of the plurality of utterances corresponds to one of the plurality of messages of the conversation log.

16 . The method of claim 13 , wherein the fourth set of prosodic cues comprise metadata prosodic cues and written prosodic cues, wherein at least a portion of the plurality of utterances corresponds to more than one of the plurality of messages of the conversation log.

17 . The method of claim 13 , wherein the fifth set of prosodic cues comprise written prosodic cues, and wherein the written prosodic cues include punctuation, emojis, emphases, or linguistic structure.

18 . The method of claim 13 , wherein providing comprises:
providing the plurality of intent segments as inputs to a first training process of a machine-learning (ML)-based parser of the NLU framework, wherein, within the first training process, the plurality of intent segments is used to generate a plurality of utterance trees, and a model of the ML-based parser is updated in response to identifying a quorum within the plurality of utterance trees; providing the plurality of utterances as inputs to a second training process of a vocabulary subsystem of the NLU framework, wherein, within the second training process, the plurality of utterances is used to generate a plurality of word vectors of a refined word vecto

Assignees

• Servicenow Inc

Inventors

• Sapugay Edwin
• Madamala Anil Kumar
• Naboka Maxim
• Sunkara Srinivas Satyasai
• Santos Lewis Savio Landry
• Subbarao Murali B

Classifications

• G10L15/16

  using artificial neural networks · CPC title

• G10L15/1807

  using prosody or stress · CPC title

• G06N5/025

  Extracting rules from data · CPC title

• G06N20/00

  Machine learning · CPC title

• G06N3/006

  based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] · CPC title