Verint Americas Inc.

Timothy James Hewitt

Joseph Wayne Dumoulin

Systems and methods are provided for dynamically adjusting a website of an entity using information that has been received, stored, gathered, and/or otherwise obtained about what people want to find on the entity's website. A website may be dynamically adjusted using trending information in response to determining that the usage of the monitored data source is greater than the baseline usage distribution or in response to determining that the usage of the monitored data source is not greater than the baseline usage distribution receiving NLP inputs of the user from the IVA and adjusting dynamic web content displayed on the website based on the NLP inputs.

Dynamic web content based on natural language processing (NLP) inputs

A non-ontological hierarchy for language models is based on established psycholinguistic and neuro-linguistic evidences. By using non-ontological hierarchies, a more natural understanding of user's inputs and intents improve toward a better potential for producing intelligent responses in a conversational situation.

Natural language processing with non-ontological hierarchy models

Systems and methods are provided for dynamically adjusting a website of an entity using information that has been received, stored, gathered, and/or otherwise obtained about what people want to find on the entity's website. A website may be dynamically adjusted using trending information. Dynamically adjusting may comprise generating and presenting links on the website to webpages in order of volume over a defined time threshold, or by variance over a standard volume over a longer established time frame. Dynamically adjusting the website may comprise providing an element on a website for what is most urgent to customers. A unit associated with a website may be monitored, and when the unit volume or activity starts to spike beyond normal expectations, then information about the unit is proactively and/or preemptively offered on the website, or a webpage of the website, without ever having a conversation with a user.

Experiential parsing (EP) is a technique for natural language parsing that falls into the category of dependency parsing. EP supports applications that derive meaning from chat language. An experiential language model parses chat data, and uses documented experiences with language without using automatic natural language processing (NLP) methods. A descriptive grammar is built at word level rather than a prescriptive grammar at phrase level. The experiential model is designed to understand that word 'A' associates with word 'B' by function &#x201c;C&#x201d;. The experiential model understands the relationship between words, independent of whether or not the overall phrase structure is grammatical. A high accuracy of producing the syntactic roles (such as main verb, direct object, etc.) is attained even when confronted with a variety of agrammatical inputs.

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Spokane Valley, WA, United States of America

Timothy James Hewitt