Distributed server system for language understanding
First Claim
1. A language understanding system, the language understanding system comprising:
- a trained language understanding server, the trained language understanding server comprising;
at least one processor; and
memory encoding computer executable instructions that, when executed by the at least one processor, perform a method comprising;
receiving a natural language input from a client device;
sending the natural language input to a 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;
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 language understanding system, a more reliable natural language understanding system, and a more efficient natural language understanding system. Further, the systems and methods provide for natural language understanding systems with better development (including update ability), productivity, and scalability.
16 Citations
20 Claims
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1. A language understanding system, the language understanding system comprising:
a trained language understanding server, the trained language understanding server comprising; at least one processor; and memory encoding computer executable instructions that, when executed by the at least one processor, perform a method comprising; receiving a natural language input from a client device; sending the natural language input to a 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; 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, 8)
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9. A method for using a trained natural language understanding system, the method comprising:
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receiving a natural language input from a client device; sending the natural language input to a plurality of feature extractors, wherein the plurality of feature extractors are each located on different feature servers, receiving potential features for the natural language input from the plurality of feature extractors; evaluating the potential features utilizing a 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 (10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. 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, wherein the first server, the second 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; aggregating the first set of potential features and the second 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 and the second feature extractor; determining at least one of a user intent, a domain, or entities with associated confidence scores based on evaluating the aggregated set of potential features; and generating a response based on the at least one of the user intent, the domain, and the entities with the associated confidence scores. - View Dependent Claims (20)
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Specification