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Systems and methods for a two-tier machine learning model for generating conversational responses

專利號(hào)
US11616741B2
公開日期
2023-03-28
申請(qǐng)人
Capital One Services, LLC(US VA McLean)
發(fā)明人
Kunlaya Soiaporn; Victor Alvarez Miranda; Pamela Katali; Arturo Hernandez Zeledon; Rui Zhang; Kwan-Yuet Ho
IPC分類
H04L51/02; G06K9/62; G10L15/16; G06N20/20
技術(shù)領(lǐng)域
learning,model,intent,machine,user,may,tier,or,in,cluster
地域: VA VA McLean

摘要

Methods and systems are described for generating dynamic conversational responses using two-tier machine learning models. The dynamic conversational responses may be generated in real time and reflect the likely goals and/or intents of a user. The two-tier machine learning model may include a first tier that determines an intent cluster based on a feature input, and a second tier that determines a specific intent from the cluster.

說(shuō)明書

Methods and systems are described herein for generating dynamic conversational responses. Conversational responses include communications between a user and a system that may maintain a conversational tone, cadence, or speech pattern of a human during an interactive exchange between the user and the system. The interactive exchange may include the system responding to one or more user actions (which may include user inactions) and/or predicting responses prior to receiving a user action. In order to maintain the conversational interaction during the exchange, the system may advantageously generate responses that are both timely and pertinent (e.g., in a dynamic fashion). This requires the system to determine both quickly (i.e., in real-time or near real-time) and accurately the intent, goal, or motivation behind a user input. These user input or actions may take various forms including speech commands, textual inputs, responses to system queries, and/or other user actions (e.g., logging into a mobile application of the system). In each case, the system may aggregate information about the user action, information about the user, and/or other circumstances related to the user action (e.g., time of day, previous user actions, current account settings, etc.) in order to determine a likely intent of the user.

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