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    ENSEMBLE SCORING SYSTEM FOR A NATURAL LANGUAGE UNDERSTANDING (NLU) FRAMEWORK
    42.
    发明申请
    ENSEMBLE SCORING SYSTEM FOR A NATURAL LANGUAGE UNDERSTANDING (NLU) FRAMEWORK 有权

    公开(公告)号：US20220245352A1

    公开(公告)日：2022-08-04

    申请号：US17579133

    申请日：2022-01-19

    Applicant: ServiceNow, Inc.

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

    IPC: G06F40/30 ,  G06F40/211 ,  G06F40/284 ,  G06N20/00

    Abstract: A natural language understanding (NLU) framework includes an ensemble scoring system designed to receive indicators determined by various systems of the NLU framework when inferencing a user utterance. The ensemble scoring system uses the received indicators, along with a set of ensemble scoring weights, to determine a respective ensemble score for each artifact of the utterance identified during inference. For example, segmentations provided by a lookup source system may be used to boost scores of intent and/or entities identified during a meaning search operation of a NLU system. The NLU framework may also include an ensemble scoring weight optimization subsystem that automatically determines optimized ensemble scoring weight values from labeled training data using an optimization plugin. Accordingly, the NLU framework enables these indicators to be suitably weighted and combined to provide a desired level of performance (e.g., computational resource consumption, precision, recall) of the NLU framework during operation.

    OPERATIONAL MODELING AND OPTIMIZATION SYSTEM FOR A NATURAL LANGUAGE UNDERSTANDING (NLU) FRAMEWORK
    43.
    发明申请
    OPERATIONAL MODELING AND OPTIMIZATION SYSTEM FOR A NATURAL LANGUAGE UNDERSTANDING (NLU) FRAMEWORK 有权

    公开(公告)号：US20220229994A1

    公开(公告)日：2022-07-21

    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.

    SYSTEM AND METHOD FOR LOOKUP SOURCE SEGMENTATION SCORING IN A NATURAL LANGUAGE UNDERSTANDING (NLU) FRAMEWORK
    44.
    发明申请
    SYSTEM AND METHOD FOR LOOKUP SOURCE SEGMENTATION SCORING IN A NATURAL LANGUAGE UNDERSTANDING (NLU) FRAMEWORK 有权

    公开(公告)号：US20220229990A1

    公开(公告)日：2022-07-21

    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 ,  G06F40/30 ,  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.

    DERIVING MULTIPLE MEANING REPRESENTATIONS FOR AN UTTERANCE IN A NATURAL LANGUAGE UNDERSTANDING (NLU) FRAMEWORK
    45.
    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：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.

    WRITTEN-MODALITY PROSODY SUBSYSTEM IN A NATURAL LANGUAGE UNDERSTANDING (NLU) FRAMEWORK
    50.
    发明申请
    WRITTEN-MODALITY PROSODY SUBSYSTEM IN A NATURAL LANGUAGE UNDERSTANDING (NLU) FRAMEWORK 审中-公开

    公开(公告)号：US20190294676A1

    公开(公告)日：2019-09-26

    申请号：US16298764

    申请日：2019-03-11

    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: 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.

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