公开(公告)号：US10713441B2

公开(公告)日：2020-07-14

申请号：US16238324

申请日：2019-01-02

Applicant: ServiceNow, Inc.

Inventor： Edwin Sapugay ,  Anil Kumar Madamala ,  Maxim Naboka ,  Srinivas SatyaSai Sunkara ,  Lewis Savio Landry Santos ,  Murali B. Subbarao

IPC: G06F40/30 ,  G06N20/00 ,  G10L15/19 ,  G10L15/22 ,  G06N5/02 ,  G06F40/205 ,  G06F40/211 ,  G10L15/18 ,  G10L15/16

Abstract: An agent automation system includes a memory configured to store a natural language understanding (NLU) framework and a processor configured to execute instructions of the NLU framework to cause the agent automation system to perform actions. These actions comprise: generating an annotated utterance tree of an utterance using a combination of rules-based and machine-learning (ML)-based components, wherein a structure of the annotated utterance tree represents a syntactic structure of the utterance, and wherein nodes of the annotated utterance tree include word vectors that represent semantic meanings of words of the utterance; and using the annotated utterance tree as a basis for intent/entity extraction of the utterance.

公开(公告)号：US20190295536A1

公开(公告)日：2019-09-26

申请号：US16238331

申请日：2019-01-02

Applicant: ServiceNow, Inc.

Inventor： Edwin Sapugay ,  Anil Kumar Madamala ,  Maxim Naboka ,  Srinivas SatyaSai Sunkara ,  Lewis Savio Landry Santos ,  Murali B. Subbarao

IPC: G10L15/18 ,  G06F17/27 ,  G10L15/22 ,  G06N20/00

Abstract: An agent automation system includes a memory configured to store a natural language understanding (NLU) framework, and a processor configured to perform actions, including: generating a meaning representation from an annotated utterance tree of an utterance, wherein a structure of the meaning representation indicates a syntactic structure of the utterance and one or more subtree vectors of the meaning representation indicate a semantic meaning of one or more intent subtrees of the meaning representation; searching the meaning representation of the utterance against an understanding model to extract intents/entities of the utterance based on the one or more subtree vectors of the meaning representation, wherein the understanding model includes a plurality of meaning representations derived from the intent/entity model; and providing the intents/entities of the utterance to a reasoning agent/behavior engine (RA/BE) of the agent automation system that performs one or more actions in response to the intents/entities of the utterance.

公开(公告)号：US20250148213A1

公开(公告)日：2025-05-08

申请号：US19012106

申请日：2025-01-07

Applicant: ServiceNow, Inc.

Inventor： Jonggun Park ,  Edwin Sapugay ,  Phani Bhushan Kumar Nivarthi ,  Masayo Iida ,  Sathwik Tejaswi Madhusudhan

IPC: G06F40/30 ,  G06F40/205 ,  G06F40/279 ,  G06N20/00

Abstract: A natural language understanding (NLU) framework includes a concept system that performs concept matching of user utterances. The concept system generates a concept cluster model from sample utterances of an intent-entity model, and then trains a machine learning (ML) concept model based on the concept cluster model. Once trained, the concept model receives semantic vectors representing potential concepts extracted from utterances, and provides concept indicators to an ensemble scoring system. These concept indicators include indications of which concepts of the concept model that matched to the potential concepts, which intents of the intent-entity model are related to these concepts, and concept-relationship scores indicating a strength and/or uniqueness of the relationship between each concept-intent combination. Based on these concept-related indicators, the ensemble scoring system may determine and apply an ensemble scoring adjustment when determining an ensemble artifact score for each of the artifacts extracted from an utterance.

公开(公告)号：US12175193B2

公开(公告)日：2024-12-24

申请号：US17579063

申请日：2022-01-19

Applicant: ServiceNow, Inc.

Inventor： Omer Anil Turkkan ,  Edwin Sapugay ,  Phani Bhushan Kumar Nivarthi

IPC: G06F40/289 ,  G06F40/205 ,  G06F40/279 ,  G06F40/284 ,  G06F40/30 ,  G06N20/00

Abstract: A natural language understanding (NLU) framework includes a lookup source system having one or more lookup sources. Each lookup source includes a respective source data representation that is compiled from respective source data. Once compiled, a user utterance can be submitted to the lookup source system, which generates segmentations of the user utterance. Each segmentation generally includes a collection of non-overlapping segments, and each segment generally describes how tokens of the user utterance can be grouped together and matched to the states of the source data representations. During lookup source inference, matches can be made to produced states or using fuzzy matchers that have corresponding of scoring adjustments. These scoring adjustments may be used by a segmentation scoring subsystem, potentially in combination with one or more additional segmentation scoring plugins, to score and rank the segmentations determined by the lookup source system for the user utterance.

公开(公告)号：US11507750B2

公开(公告)日：2022-11-22

申请号：US16931007

申请日：2020-07-16

Applicant: ServiceNow, Inc.

Inventor： Edwin Sapugay ,  Anil Kumar Madamala ,  Maxim Naboka ,  Srinivas SatyaSai Sunkara ,  Lewis Savio Landry Santos ,  Murali B. Subbarao

IPC: G06F40/30 ,  G06F16/28 ,  G06F16/2458 ,  G06N5/04 ,  G06F40/247 ,  G06F40/295 ,  G06N20/00

Abstract: An agent automation system includes a memory configured to store a corpus of utterances and a semantic mining framework and a processor configured to execute instructions of the semantic mining framework to cause the agent automation system to perform actions, wherein the actions include: detecting intents within the corpus of utterances; producing intent vectors for the intents within the corpus; calculating distances between the intent vectors; generating meaning clusters of intent vectors based on the distances; detecting stable ranges of cluster radius values for the meaning clusters; and generating an intent/entity model from the meaning clusters and the stable ranges of cluster radius values, wherein the agent automation system is configured to use the intent/entity model to classify intents in received natural language requests.

公开(公告)号：US11487945B2

公开(公告)日：2022-11-01

申请号：US16570506

申请日：2019-09-13

Applicant: ServiceNow, Inc.

Inventor： Edwin Sapugay ,  Jonggun Park ,  Anne Katharine Heaton-Dunlap

IPC: G06F40/30 ,  G06F40/295 ,  G06F40/253 ,  G06F40/205 ,  G06F16/2455 ,  G06F16/68

Abstract: Present embodiments include an agent automation framework having a similarity scoring subsystem that performs meaning representation similarity scoring to facilitate extraction of artifacts to address an utterance. The similarity scoring subsystem identifies a CCG form of an utterance-based meaning representation and queries a database to retrieve a comparison function list that enables quantifications of similarities between the meaning representation and candidates within a search space. The comparison functions enable the similarity scoring subsystem to perform computationally-cheapest and/or most efficient comparisons before other comparisons. The similarity scoring subsystem may determine an initial similarity score between the particular meaning representation and the candidates of the search space, then prune non-similar candidates from the search space. Selective search space pruning enables the similarity scoring subsystem to iteratively compare more data of the meaning representation to the search space via increasingly-complex comparison functions, while narrowing the search space to potentially-matching candidates.

公开(公告)号：US20220238103A1

公开(公告)日：2022-07-28

申请号：US17579052

申请日：2022-01-19

Applicant: ServiceNow, Inc.

Inventor： Sathwik Tejaswi Madhusudhan ,  Edwin Sapugay ,  Srinivas SatyaSai Sunkara

IPC: G10L15/18 ,  G10L15/16 ,  G10L15/30 ,  G10L15/06

Abstract: A natural language understanding (NLU) framework includes a domain-aware vector encoding (DAVE) framework. The DAVE framework enables a designer to create a DAVE system having a domain-agnostic semantic (DAS) model and a corresponding trained vector translator (VT) model. The DAVE system uses the DAS model to generate domain-agnostic semantic vectors for portions of a user utterance, and then uses the VT model to translate the domain-agnostic semantic vectors into a domain-aware semantic vectors to be used by a NLU system of the NLU framework during a meaning search operation. The VT model is also designed to provide predicted intent classifications for the portions the user utterance. Both the NLU system and the DAVE system of the NLU framework are highly configurable and refer to various NLU constraints during operation, including performance constraints and resource constraints provided by a designer or user of the NLU framework.

公开(公告)号：US20220237383A1

公开(公告)日：2022-07-28

申请号：US17579007

申请日：2022-01-19

Applicant: ServiceNow, Inc.

Inventor： Jonggun Park ,  Edwin Sapugay ,  Phani Bhushan Kumar Nivarthi ,  Masayo Iida ,  Sathwik Tejaswi Madhusudhan

IPC: G06F40/30 ,  G06F40/279 ,  G06N20/00

Abstract: A natural language understanding (NLU) framework includes an a concept system that performs concept matching of user utterances. The concept system generates a concept cluster model from sample utterances of an intent-entity model, and then trains a machine learning (ML) concept model based on the concept cluster model. Once trained, the concept model receives semantic vectors representing potential concepts extracted from utterances, and provides concept indicators to an ensemble scoring system. These concept indicators include indications of which concepts of the concept model that matched to the potential concepts, which intents of the intent-entity model are related to these concepts, and concept-relationship scores indicating a strength and/or uniqueness of the relationship between each concept-intent combination. Based on these concept-related indicators, the ensemble scoring system may determine and apply an ensemble scoring adjustment when determining an ensemble artifact score for each of the artifacts extracted from an utterance.

公开(公告)号：US20220229987A1

公开(公告)日：2022-07-21

申请号：US17579233

申请日：2022-01-19

Applicant: ServiceNow, Inc.

Inventor： Sagar Davasam Suryanarayan ,  Edwin Sapugay ,  Anil Kumar Madamala ,  Maxim Naboka ,  Vipulkumar Popat Mahadik ,  Edward Cheung

IPC: G06F40/284 ,  G06F40/253 ,  G06F40/30 ,  G06F40/166 ,  G06N20/00

Abstract: A natural language understanding (NLU) framework includes a lookup source framework, which enables a lookup source system to be defined having one or more lookup sources. The lookup source system can operate in a number of different manners to facilitate repository-aware inference of user utterances, for example, by facilitating vocabulary injection during compilation of an utterance meaning model and/or an understanding model. Additionally, the lookup source system can be leveraged to cleanse client-specific training data of sensitive values to generate generic training data that can be used to train the NLU framework of other clients. The lookup sources can be compiled in a synchronous or asynchronous manner, which enables lookup sources to be compiled in an on-demand basis from test source data. Additionally, understanding models that reference lookup sources can be periodically recompiled while leveraging the latest versions of the lookup sources for vocabulary injection.

公开(公告)号：US20210224485A1

公开(公告)日：2021-07-22

申请号：US17301092

申请日：2021-03-24

Applicant: ServiceNow, Inc.

Inventor： Edwin Sapugay ,  Anil Kumar Madamala ,  Maxim Naboka ,  Srinivas SatyaSai Sunkara ,  Lewis Savio Landry Santos ,  Murali B. Subbarao

IPC: G06F40/30 ,  G06N20/00 ,  G10L15/19 ,  G10L15/22 ,  G06N5/02 ,  G06F40/205 ,  G06F40/211

Abstract: An agent automation system includes a memory configured to store a natural language understanding (NLU) framework and a model, wherein the model includes at least one original meaning representation. The system includes a processor configured to execute instructions of the NLU framework to cause the agent automation system to perform actions including: performing rule-based generalization of the model to generate at least one generalized meaning representation of the model from the at least one original meaning representation of the model; performing rule-based refinement of the model to prune or modify the at least one generalized meaning representation of the model, or the at least one original meaning representation of the model, or a combination thereof; and after performing the rule-based generalization and the rule-based refinement of the model, using the model to extract intents/entities from a received user utterance