公开(公告)号：US12299391B2

公开(公告)日：2025-05-13

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 ,  G06F40/166 ,  G06F40/253 ,  G06F40/30 ,  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：US20220058343A1

公开(公告)日：2022-02-24

申请号：US17453446

申请日：2021-11-03

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: 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.

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：US20210004537A1

公开(公告)日：2021-01-07

申请号：US16749828

申请日：2020-01-22

Applicant: ServiceNow, Inc.

Inventor： Edwin Sapugay ,  Anil Kumar Madamala ,  Omer Anil Turkkan ,  Maxim Naboka

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

Abstract: The present disclosure is directed to an agent automation framework that is capable of extracting meaning from user utterances and suitably responding using a search-based natural language understanding (NLU) framework. The NLU framework includes a meaning extraction subsystem capable of detecting multiple alternative meaning representations for a given natural language utterance. Furthermore, the NLU framework includes a meaning search subsystem that enables elastic confidence thresholds (e.g., elastic beam-width meaning searches), forced diversity, and cognitive construction grammar (CCG)-based predictive scoring functions to provide an efficient and effective meaning search. As such, the disclosed meaning extraction subsystem and meaning search subsystem improve the performance, the domain specificity, the inference quality, and/or the efficiency of the NLU framework.

公开(公告)号：US20210004442A1

公开(公告)日：2021-01-07

申请号：US16570506

申请日：2019-09-13

Applicant: ServiceNow, Inc.

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

IPC: G06F17/27

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：US20200349325A1

公开(公告)日：2020-11-05

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

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：US20190294675A1

公开(公告)日：2019-09-26

申请号：US16239147

申请日：2019-01-03

Applicant: ServiceNow, Inc.

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

IPC: G06F17/27

Abstract: An agent automation system includes a memory configured to store a reasoning agent/behavior engine (RA/BE) including a first persona and a current context and a processor configured to execute instructions of the RA/BE to cause the first persona to perform actions comprising: receiving intents/entities of a first user utterance; recognizing a context overlay cue in the intents/entities of the first user utterance, wherein the context overlay cue defines a time period; updating the current context of the RA/BE by overlaying context information from at least one stored episode associated with the time period; and performing at least one action based on the intents/entities of the first user utterance and the current context of the RA/BE.

公开(公告)号：US11741309B2

公开(公告)日：2023-08-29

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 ,  G06N5/022 ,  G06F40/205 ,  G06F40/211 ,  G10L15/18 ,  G10L15/16

CPC classification number: G06F40/30 ,  G06F40/205 ,  G06F40/211 ,  G06N5/022 ,  G06N20/00 ,  G10L15/19 ,  G10L15/22 ,  G10L15/16 ,  G10L15/1807 ,  G10L15/1822 ,  G10L2015/223 ,  G10L2015/225

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.

公开(公告)号：US11556713B2

公开(公告)日：2023-01-17

申请号：US16749828

申请日：2020-01-22

Applicant: ServiceNow, Inc.

Inventor： Edwin Sapugay ,  Anil Kumar Madamala ,  Omer Anil Turkkan ,  Maxim Naboka

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

Abstract: The present disclosure is directed to an agent automation framework that is capable of extracting meaning from user utterances and suitably responding using a search-based natural language understanding (NLU) framework. The NLU framework includes a meaning extraction subsystem capable of detecting multiple alternative meaning representations for a given natural language utterance. Furthermore, the NLU framework includes a meaning search subsystem that enables elastic confidence thresholds (e.g., elastic beam-width meaning searches), forced diversity, and cognitive construction grammar (CCG)-based predictive scoring functions to provide an efficient and effective meaning search. As such, the disclosed meaning extraction subsystem and meaning search subsystem improve the performance, the domain specificity, the inference quality, and/or the efficiency of the NLU framework.

公开(公告)号：US20220245353A1

公开(公告)日：2022-08-04

申请号：US17579290

申请日：2022-01-19

Applicant: ServiceNow, Inc.

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

IPC: G06F40/30 ,  G06F40/40 ,  G06F40/295 ,  H04L51/02 ,  G06N20/20

Abstract: A natural language understanding (NLU) framework includes an ensemble scoring system that uses received indicators, along with a set of ensemble scoring weights and ensemble scoring rules, to determine a respective ensemble score for each artifact of the utterance identified during inference. The ensemble scoring rules enable boosting of the respective ensemble score of an extracted intent of an utterance in response to a sufficient or important entity associated with the intent also being extracted from the utterance. Based on one or more ensemble scoring rules, the ensemble scoring system may refer to an intent-entity model to determine sufficient or important entities associated with an extracted intent, and boost the respective ensemble artifact score of the intent when the ensemble scoring system determines, with a suitable confidence, that a sufficient entity or important entity of the intent was extracted by the NLU framework during inference of the user utterance.