公开(公告)号：US11205052B2

公开(公告)日：2021-12-21

申请号：US16552493

申请日：2019-08-27

Applicant: ServiceNow, Inc.

Inventor： Edwin Sapugay ,  Gopal Sarda

IPC: G06F40/30 ,  G06N3/08 ,  G06F40/205 ,  G06F40/253 ,  G06F40/295 ,  G06F40/211 ,  G06F40/284 ,  G06F40/216

Abstract: The present approaches are generally related to an agent automation framework that is capable of extracting meaning from user utterances, such as requests received by a virtual agent (e.g., a chat agent), and suitably responding to these user utterances. In certain aspects, the agent automation framework includes a NLU framework and an intent-entity model having defined intents and entities that are associated with sample utterances. The NLU framework may include a meaning extraction subsystem designed to generate meaning representations for the sample utterances of the intent-entity model to construct an understanding model, as well as generate meaning representations for a received user utterance to construct an utterance meaning model. The disclosed NLU framework may include a meaning search subsystem that is designed to search the meaning representations of the understanding model to locate matches for meaning representations of the utterance meaning model.

公开(公告)号：US20220019936A1

公开(公告)日：2022-01-20

申请号：US16931906

申请日：2020-07-17

Applicant: ServiceNow, Inc.

Inventor： Gopal Sarda ,  Sravan Ramachandran ,  Seganrasan Subramanian ,  Baskar Jayaraman

IPC: G06N20/00 ,  G06N7/00

Abstract: A specification of a desired target field for machine learning prediction and one or more tables storing machine learning training data are received. Within the one or more tables, eligible machine learning features for building a machine learning model to perform a prediction for the target field are identified. The eligible machine learning features are evaluated using a pipeline of different evaluations to successively filter out one or more of the eligible machine learning features to identify a set of recommended machine learning features among the eligible machine learning features. The set of recommended machine learning features is provided for use in building the machine learning model.

公开(公告)号：US20210004441A1

公开(公告)日：2021-01-07

申请号：US16552493

申请日：2019-08-27

Applicant: ServiceNow, Inc.

Inventor： Edwin Sapugay ,  Gopal Sarda

IPC: G06F17/27 ,  G06N3/08

Abstract: The present approaches are generally related to an agent automation framework that is capable of extracting meaning from user utterances, such as requests received by a virtual agent (e.g., a chat agent), and suitably responding to these user utterances. In certain aspects, the agent automation framework includes a NLU framework and an intent-entity model having defined intents and entities that are associated with sample utterances. The NLU framework may include a meaning extraction subsystem designed to generate meaning representations for the sample utterances of the intent-entity model to construct an understanding model, as well as generate meaning representations for a received user utterance to construct an utterance meaning model. The disclosed NLU framework may include a meaning search subsystem that is designed to search the meaning representations of the understanding model to locate matches for meaning representations of the utterance meaning model.

公开(公告)号：US11720756B2

公开(公告)日：2023-08-08

申请号：US17451405

申请日：2021-10-19

Applicant: ServiceNow, Inc.

Inventor： Edwin Sapugay ,  Gopal Sarda

IPC: G06F40/30 ,  G06N3/08 ,  G06F40/205 ,  G06F40/253 ,  G06F40/295 ,  G06F40/211 ,  G06F40/284 ,  G06F40/216

CPC classification number: G06F40/30 ,  G06F40/205 ,  G06F40/211 ,  G06F40/216 ,  G06F40/253 ,  G06F40/284 ,  G06F40/295 ,  G06N3/08

Abstract: The present approaches are generally related to an agent automation framework that is capable of extracting meaning from user utterances, such as requests received by a virtual agent (e.g., a chat agent), and suitably responding to these user utterances. In certain aspects, the agent automation framework includes a NLU framework and an intent-entity model having defined intents and entities that are associated with sample utterances. The NLU framework may include a meaning extraction subsystem designed to generate meaning representations for the sample utterances of the intent-entity model to construct an understanding model, as well as generate meaning representations for a received user utterance to construct an utterance meaning model. The disclosed NLU framework may include a meaning search subsystem that is designed to search the meaning representations of the understanding model to locate matches for meaning representations of the utterance meaning model.

公开(公告)号：US20220036012A1

公开(公告)日：2022-02-03

申请号：US17451405

申请日：2021-10-19

Applicant: ServiceNow, Inc.

Inventor： Edwin Sapugay ,  Gopal Sarda

IPC: G06F40/30 ,  G06N3/08 ,  G06F40/205 ,  G06F40/253 ,  G06F40/295 ,  G06F40/211 ,  G06F40/284 ,  G06F40/216

Abstract: The present approaches are generally related to an agent automation framework that is capable of extracting meaning from user utterances, such as requests received by a virtual agent (e.g., a chat agent), and suitably responding to these user utterances. In certain aspects, the agent automation framework includes a NLU framework and an intent-entity model having defined intents and entities that are associated with sample utterances. The NLU framework may include a meaning extraction subsystem designed to generate meaning representations for the sample utterances of the intent-entity model to construct an understanding model, as well as generate meaning representations for a received user utterance to construct an utterance meaning model. The disclosed NLU framework may include a meaning search subsystem that is designed to search the meaning representations of the understanding model to locate matches for meaning representations of the utterance meaning model.