Tailored artificial intelligence
First Claim
1. A system that determines a classification by simulating a human user comprising;
- a translator that translates an input segment to an output segment and represents a frequency of words and a frequency of phrases in the output segment as an input vector;
a natural language processing engine that processes the input vector and generates a plurality of intents and a plurality of sub-entities;
a plurality of recognition engines hosting one or more evolutionary models that process the plurality of intents and the plurality of sub-entities to generate a second plurality of intents and a second plurality of sub-entities that represent a species classification;
a plurality of application engines that process the plurality of intents, the plurality of sub-entities, the second plurality of intents, and the second plurality of sub-entities, and render an identifying output to a graphical interface;
where the plurality of application engines automatically render a plurality of training vectors and a plurality of textual associations and associate the plurality of training vectors and the plurality of textual associations with a free-form input to the one or more evolutionary models when the identifying output is rejected; and
where the natural language processing engine selects an instance of an evolutionary model of the one or more evolutionary models as a result of a recognition of one or more predefined semantically relevant words and phrases that the natural language processing engine detected in the input vector; and
where the species classification ranks below a genus classification or a subgenus classification in a domain.
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Abstract
A system and method determine a classification by simulating a human user. The system and method translate an input segment such as speech into an output segment such as text and represents the frequency of words and phrases in the textual segment as an input vector. The system and method process the input vector and generate a plurality of intents and a plurality of sub-entities. The processing of multiple intents and sub-entities generates a second multiple of intents and sub-entities that represent a species classification. The system and method select an instance of an evolutionary model as a result of the recognition of one or more predefined semantically relevant words and phrases detected in the input vector.
11 Citations
30 Claims
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1. A system that determines a classification by simulating a human user comprising;
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a translator that translates an input segment to an output segment and represents a frequency of words and a frequency of phrases in the output segment as an input vector; a natural language processing engine that processes the input vector and generates a plurality of intents and a plurality of sub-entities; a plurality of recognition engines hosting one or more evolutionary models that process the plurality of intents and the plurality of sub-entities to generate a second plurality of intents and a second plurality of sub-entities that represent a species classification; a plurality of application engines that process the plurality of intents, the plurality of sub-entities, the second plurality of intents, and the second plurality of sub-entities, and render an identifying output to a graphical interface; where the plurality of application engines automatically render a plurality of training vectors and a plurality of textual associations and associate the plurality of training vectors and the plurality of textual associations with a free-form input to the one or more evolutionary models when the identifying output is rejected; and where the natural language processing engine selects an instance of an evolutionary model of the one or more evolutionary models as a result of a recognition of one or more predefined semantically relevant words and phrases that the natural language processing engine detected in the input vector; and where the species classification ranks below a genus classification or a subgenus classification in a domain. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method that determines a classification by simulating a human user comprising;
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translating an input segment by a translator to an output segment; representing a frequency of words and a frequency of phrases in the output segment as an input vector; processing by a natural language processing engine the input vector and generating a plurality of intents and a plurality of sub-entities; processing the plurality of intents and the plurality of sub-entities by a plurality of recognition engines hosting one or more evolutionary models; generating a second plurality of intents and a second plurality of sub-entities that represent a species classification; selecting an instance of an evolutionary model of the one or more evolutionary models as a result of a recognition of one or more predefined semantically relevant words and phrases that the natural language processing engine detected in the input vector; processing the plurality of intents, the plurality of sub-entities, the second plurality of intents, and the second plurality of sub-entities; rendering an identifying output to a graphical interface; rendering a plurality of training vectors and a plurality of textual associations and associating the plurality of training vectors and the plurality of textual associations with a free-form input to the one or more evolutionary models when the identifying output is rejected; training the one or more evolutionary models in response to receiving the plurality of training vectors and a plurality of textual associations until a fitness threshold is exceeded; and where the species classification ranks below a genus classification or a subgenus classification in a domain. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A non-transitory machine-readable medium encoded with machine-executable instructions, where execution of the machine-executable instructions is for:
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translating an input segment by a translator to an output segment; representing a frequency of words and a frequency of phrases in the output segment as an input vector; processing by a natural language processing engine the input vector and generating a plurality of intents and a plurality of sub-entities; processing a plurality of intents and a plurality of sub-entities by a plurality of recognition engines hosting one or more evolutionary models; generating a second plurality of intents and a second plurality of sub-entities that represent a species classification; selecting an instance of an evolutionary model of the one or more evolutionary models as a result of a recognition of one or more predefined semantically relevant words and phrases that the natural language processing engine detected in the input vector processing the plurality of intents, the plurality of sub-entities, the second plurality of intents, and the second plurality of sub-entities; rendering an identifying output to a graphical interface; rendering a plurality of training vectors and a plurality of textual associations and associating the plurality of training vectors and the plurality of textual associations with a free-form input to the one or more evolutionary models when the identifying output is rejected; and training the one or more evolutionary models in response to receiving the plurality of training vectors and a plurality of textual associations until a fitness threshold is exceeded; and where the species classification ranks below a genus classification or a subgenus classification in a domain. - View Dependent Claims (22, 23, 24, 25, 26, 27, 28, 29, 30)
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Specification