Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Game anti-addiction judgment system and method based on twin neural network 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: 2022-01-07
XIAOVO TECH +1
View PDF9 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The lack of an accurate and intelligent game addiction determination method is also an important factor hindering many game manufacturers from developing game anti-addiction systems

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Game anti-addiction judgment system and method based on twin neural network and gmm
  • Game anti-addiction judgment system and method based on twin neural network and gmm
  • Game anti-addiction judgment system and method based on twin neural network and gmm

Examples

Experimental program
Comparison scheme
Effect test

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...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The present invention provides a game anti-addiction judgment system and method based on a twin neural network and GMM, the method includes: using the twin neural network model to extract features from game data of game users, and performing feature extraction on the features of different types of game users Differential learning and training, so that the user characteristics of addicted game users and the user characteristics of non-addicted game users have a different distance; use the Gaussian mixture model to learn and train the user characteristics of game users, and learn the user characteristics of addicted game users and non-addicted game users The probability distribution of the feature vector of the user features; use the trained twin neural network model to extract the feature vector of the game data of the user to be determined, and use the trained Gaussian mixture model to calculate the likelihood of the feature vector of the game data of the user to be determined , and determine whether the user to be determined is a user addicted to the game according to the likelihood. The present invention uses a model to determine whether the user is addicted to the game.

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

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
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
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com