Game anti-indulgence judgment system and method based on twin neural networks and GMM

A neural network and judgment method technology, applied in the field of machine learning, can solve problems such as hindering development and lack of game addiction judgment methods, and achieve the effects of reducing dependence, increasing training sample size, and strong universal applicability

Active Publication Date: 2019-01-15
XIAOVO TECH +1
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The lack of an accurate and intelligent game addiction determination method is also an imp

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  • Game anti-indulgence judgment system and method based on twin neural networks and GMM
  • Game anti-indulgence judgment system and method based on twin neural networks and GMM
  • Game anti-indulgence judgment system and method based on twin neural networks and GMM

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Embodiment Construction

[0035] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, in the case of no conflict, the following embodiments and features in the embodiments can be combined with each other.

[0036] see Figure 1 to Figure 6 . It should be noted that the structures, proportions, sizes, etc. shown in the drawings attached to this specification are only used to match the content disclosed in the specification, for those who are familiar with this technology to understand and read, a...

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Abstract

The invention provides a game anti-indulgence judgment system and method based on twin neural network and GMM. The method comprises the following steps of: utilizing twin neural network models to extract features of game data of game users, and carrying out difference learning training on features of different types of game users, so that user features of addicted game users and user features of non-addicted game users have different distances; the Gaussian mixture model is used to train the user characteristics of the game users and to learn the probability distribution of the user characteristics of the indulgent game users and the user characteristics of the non-indulgent game users. The trained twin neural network model is used to extract the feature vector of the game data of the userto be judged, and the likelihood of the feature vector of the game data of the user to be judged is calculated by using the trained Gaussian mixture model, and whether the user to be judged is a useraddicted to the game or not is determined according to the likelihood. The invention utilizes a model to judge whether a user is addicted to a game or not.

Description

technical field [0001] The present invention relates to the technical field of machine learning, in particular to a game anti-addiction judgment system and method based on twin neural network and GMM. Background technique [0002] The game anti-addiction system aims to solve the current situation that game users are addicted to online games. In order to effectively determine whether game users are addicted to games in the near future, measures such as reminders and game restrictions are used to change the bad game habits of some game users. [0003] In August 2005, the General Administration of Press and Publication issued the "Development Standards for Online Game Anti-addiction System", requiring seven large domestic online game operating companies to prepare for the development of an anti-addiction system. In March 2006, the General Administration of Press and Publication issued the "Notice on the Implementation of the Anti-addiction System of Online Games to Protect the ...

Claims

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Application Information

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IPC IPC(8): G06K9/62A63F13/75A63F13/79
CPCA63F13/75A63F13/79G06F18/2415G06F18/214
Inventor 骆源方品徐彬顾振兴
Owner XIAOVO TECH
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