Robot dialogue system and method based on knowledge graph and RNN (Recurrent Neural Network)

A robot dialogue and neural network technology, applied in the field of robot dialogue systems, can solve problems such as poor experience, and achieve the effects of saving computing power, increasing autonomy, and improving controllability

Pending Publication Date: 2021-12-17
杭州景吾智能科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, the experience of the above scheme is poor

Method used

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  • Robot dialogue system and method based on knowledge graph and RNN (Recurrent Neural Network)
  • Robot dialogue system and method based on knowledge graph and RNN (Recurrent Neural Network)

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Embodiment Construction

[0034] The present invention will be described in detail below in conjunction with specific embodiments. The following examples will facilitate further understanding of the present invention in any form of techniques, but will not limit the invention in any form. It should be noted that several variations and improvements can also be made without departing from the concept of the present invention. These are all of the scope of protection of the present invention.

[0035] Such as figure 1 and figure 2 As shown, a robot dialogue system based on knowledge map and RNN neural networks, including the following modules: Robot module: used to convert the voice of the conversation into text, and transform the text into voice or running tasks; Module: Receive information transmitted by the robot module, performs databases and neural network related matchs, matching text answers and feeds back to robot modules; database modules: Different databases for maintaining different robots to achie...

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Abstract

The invention provides a robot dialogue system and method based on a knowledge graph and an RNN (Recurrent Neural Network). The system comprises a robot module which is used for converting the voice of a dialogue into a text, and converting the text into the voice or running a task; a server module which receives the information transmitted by the robot module, configures a database and a neural network, matches text answers and feeds back the text answers to the robot module; and a database module which is used for maintaining different databases of different robots. According to the method, different text matching schemes are utilized, the maximum matching accuracy of the input text is ensured, and correct and efficient answering is ensured. The service is deployed in the server, so that through the http request, the self-generated computing power of the machine is saved, meanwhile, the steps of machine deployment are simplified, and only interface calling is needed. According to the method, online of different machines can be supported at the same time, personalized answering of the different machines is achieved, self-defined questions and answers are supported, and autonomy is improved.

Description

Technical field [0001] The present invention relates to the field of robots, and in particular, to a robot dialogue system and method based on a knowledge map and an RNN neural network. Background technique [0002] Robot Dialogue will become more common application functions in the 21st century. But the existing human-machine dialogue is not intelligent, often answered with the situation. The answer is not accurate enough, and the customer's experience is poor. [0003] Patent Document is disclosed in the invention of CN110774285A discloses a method of performing a dialogue between humanoid robots and at least one user comprising the following steps repeatedly executed by a human robot: I) obtain multiple input signals from the corresponding sensor; II) Explanation The signal obtained to identify multiple events generated by the user, the event is selected from the group consisting of the following: Say at least words or sentences, tone, gestures, body posture, facial expression...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/332G06F16/33G06F16/36G06K9/62G06N3/04G06N3/08G10L15/26
CPCG06F16/3329G06F16/3343G06F16/367G06N3/04G06N3/08G10L15/26G06F18/22
Inventor 周广郭震
Owner 杭州景吾智能科技有限公司
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