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    DERIVING MULTIPLE MEANING REPRESENTATIONS FOR AN UTTERANCE IN A NATURAL LANGUAGE UNDERSTANDING (NLU) FRAMEWORK
    31.
    发明申请
    DERIVING MULTIPLE MEANING REPRESENTATIONS FOR AN UTTERANCE IN A NATURAL LANGUAGE UNDERSTANDING (NLU) FRAMEWORK 有权

    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.

    TEMPLATED RULE-BASED DATA AUGMENTATION FOR INTENT EXTRACTION
    32.
    发明申请
    TEMPLATED RULE-BASED DATA AUGMENTATION FOR INTENT EXTRACTION 审中-公开

    公开(公告)号：US20190295537A1

    公开(公告)日：2019-09-26

    申请号：US16239218

    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: G10L15/19 ,  G06N20/00

    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

    METHOD AND SYSTEM FOR AUTOMATED INTENT MINING, CLASSIFICATION AND DISPOSITION
    34.
    发明申请
    METHOD AND SYSTEM FOR AUTOMATED INTENT MINING, CLASSIFICATION AND DISPOSITION 审中-公开

    公开(公告)号：US20190294673A1

    公开(公告)日：2019-09-26

    申请号：US16179681

    申请日：2018-11-02

    Applicant: ServiceNow, Inc.

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

    IPC: G06F17/27 ,  G06F17/30 ,  G06N5/04

    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.

    Concept system for a natural language understanding (NLU) framework
    35.
    发明授权
    Concept system for a natural language understanding (NLU) framework 有权

    公开(公告)号：US12197869B2

    公开(公告)日：2025-01-14

    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 ,  G06F40/205

    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.

    Operational modeling and optimization system for a natural language understanding (NLU) framework
    37.
    发明授权
    Operational modeling and optimization system for a natural language understanding (NLU) framework 有权

    公开(公告)号：US12175196B2

    公开(公告)日：2024-12-24

    申请号：US17579044

    申请日：2022-01-19

    Applicant: ServiceNow, Inc.

    Inventor： Roshnee Sharma ,  Edwin Sapugay ,  Sathwik Tejaswi Madhusudhan ,  Anil Kumar Madamala ,  Hari Subramani ,  Jonggun Park ,  Srinivas Satyasai Sunkara

    IPC: G06F40/30 ,  G06F40/40 ,  H04L51/10

    Abstract: A natural language understanding (NLU) framework includes a modeling and optimization system that enables enhanced understanding and explainability to the operation of the NLU framework. The NLU framework includes a configuration vector storing settings of various components that may be applied during NLU inference of an utterance, such as which components should be activated or deactivated, as well as which numerical values (e.g., threshold values, coefficients, weight values) that are used by these components during operation. By using this configuration vector to systematically disable and adjust numerical parameters of the components of the NLU framework, and then determining the performance of the NLU framework in these configurations, the modeling and optimization system determines relationships between, as well as the relative importance of, the components of the NLU framework. The modeling and optimization system automatically determines or optimizes configurations for the NLU framework to accommodate various NLU performance and/or resource constraints.

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