Distributed server system for language understanding
First Claim
1. A language understanding system, the language understanding system comprising:
- a language understanding server, the language understanding server comprises;
at least one processor; and
memory encoding computer executable instructions that, when executed by the at least one processor, perform a method comprising;
retrieving training features from a plurality of feature extractors, wherein the plurality of feature extractors are each located on different feature servers, and wherein the language understanding server is separate from the different feature servers; and
estimating model parameters based on a training algorithm that utilizes the training features from the different feature servers to form a trained language understanding model;
receiving a natural language input from a client device;
sending the natural language input to the plurality of feature extractors in response to receiving the natural language input;
receiving potential features from the plurality of feature extractors after sending the natural language input to the plurality of feature extractors;
receiving and evaluating the potential features to determine input features for the natural language input;
determining a semantic meaning of the natural language input based on the input features; and
sending a response to the client device that includes the semantic meaning of the natural language input.
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Abstract
Systems and methods for training and using a natural language understanding system are provided. More specifically, the systems and methods train a natural language understanding system utilizing a distributed network of feature extractors on features servers. Further, the systems and methods for using the natural language understanding system utilize a distributed network of features extractor on features servers. Accordingly, the systems and methods provide for a more accurate natural langue understanding system, a more reliable natural langue understanding system, and a more efficient natural langue understanding system. Further, the systems and methods provide for natural language understanding systems with better development (including update ability), productivity, and scalability.
16 Citations
18 Claims
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1. A language understanding system, the language understanding system comprising:
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a language understanding server, the language understanding server comprises; at least one processor; and memory encoding computer executable instructions that, when executed by the at least one processor, perform a method comprising; retrieving training features from a plurality of feature extractors, wherein the plurality of feature extractors are each located on different feature servers, and wherein the language understanding server is separate from the different feature servers; and estimating model parameters based on a training algorithm that utilizes the training features from the different feature servers to form a trained language understanding model; receiving a natural language input from a client device; sending the natural language input to the plurality of feature extractors in response to receiving the natural language input; receiving potential features from the plurality of feature extractors after sending the natural language input to the plurality of feature extractors; receiving and evaluating the potential features to determine input features for the natural language input; determining a semantic meaning of the natural language input based on the input features; and sending a response to the client device that includes the semantic meaning of the natural language input. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method for training and using a natural language understanding system, the method comprising:
training a language understanding model of a language understanding system, the training comprises; receiving, at a language understanding server, training features from a plurality of feature extractors, wherein the plurality of feature extractors are each located on different feature servers, wherein the language understanding server is separate from the different feature servers; estimating model parameters based on a training algorithm that utilizes the training features from the different feature servers to form a trained language understanding model; receiving a natural language input from a client device; sending the natural language input to the plurality of feature extractors; receiving potential features for the natural language input from the plurality of feature extractors; evaluating the potential features utilizing the trained language understanding model to determine input features for the natural language input; and generating a response to the natural language input based on the input features. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15, 16)
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17. A system comprising:
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at least one processor; and a memory encoding computer executable instructions that, when executed by the at least one processor, cause the at least one processor to perform a method for language understanding, the method comprising; receiving a natural language input from a client device on a natural language server, sending the natural language input to a first feature extractor on a first server from the natural language server; sending the natural language input to a second feature extractor on a second server from the natural language server, sending the natural language input to a third feature extractor on a third server from the natural language server, wherein the first server, the second server, the third server, and the natural language server are different and separate from each other; receiving a first set of potential features for the natural language input from the first feature extractor by the natural language server; receiving a second set of potential features for the natural language input from the second feature extractor by the natural language server; receiving a third set of potential features for the natural language input from the third feature extractor by the natural language server; aggregating the first set of potential features, the second set of potential features, and the third set of potential features to form an aggregated set of potential features; evaluating the aggregated set of potential features utilizing a language understanding model trained with training features from the first feature extractor, the second feature extractor, and the third feature extractor; determining a user intent, a domain, and entities and associated confidence scores based on evaluating the aggregated set of potential features; and generating a response based on the user intent, the domain, and the entities and the associated confidence scores. - View Dependent Claims (18)
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Specification