System and application for smartphone restrictions and monitoring
The present invention relates to a system and method for monitoring and restricting smartphone usage among children, enhancing parental oversight and mental health support. The system includes internet-connected central servers executing monitoring software from non-transitory media, paired with specialized child devices and generic parental devices. These devices facilitate real-time and recorded monitoring of smartphone activities, including application usage, screen time, and internet access. The child devices are equipped with customized operating systems and software that prevent bypassing parental controls. Parental devices can access detailed analytics and live streams of child device activity. The system also incorporates machine learning algorithms to trigger mental health checks based on application usage patterns. These checks involve interactive mental health questionnaires, mindfulness exercises, and emergency interventions, providing timely support tailored to the child's activities and mental state. This invention offers a comprehensive solution to digital parenting challenges.
1 . A method for monitoring smartphone usage among children, comprising:
providing, via a child device provider, a specialized child device with a pre-installed customized operating system and monitoring software prior to delivery to a user, wherein the pre-installed customized operating system includes a custom library model configured to be loaded as a shared library file within the operating system of the child device, to create virtual system functions that mirror actual system shared library functions used by applications running on the child device, and to intercept and log activity data including inputs, outputs, and system calls associated with the applications;
transmitting, via the specialized child device, the activity data from the child device to a central server;
analyzing, via the central server, the activity data on the central server to identify usage patterns;
generating, via the central server, notifications based on the analyzed usage patterns;
sending, via the central server, the notifications to a parental device; and,
enabling, via the central server, the parental device to access and review the notifications and the activity data of the child device through a parental dashboard configured on the parental device.
2 . The method of claim 1 , wherein the transmitting data includes information selected from the group consisting of application usage data, call data, message data, and internet browsing data.
3 . The method of claim 1 , wherein the analyzing step includes the use of a machine learning algorithm to determine the usage patterns.
4 . The method of claim 1 , further comprising triggering a mental health check based on the analyzed usage patterns.
5 . The method of claim 4 , wherein the mental health check includes sending a questionnaire to the child device.
6 . The method of claim 5 , wherein the questionnaire is adapted based on the child's recent activity as determined by the machine learning algorithm.
7 . The method of claim 1 , further comprising the step of live streaming the display of the child device to the parental device.
8 . The method of claim 1 , wherein the notifications are based on predetermined criteria including at least one of: time spent on applications, inappropriate content access, and time of device usage.
9 . The method of claim 1 , further comprising a mental health professional device configured to receive specific alerts and data related to mental health checks from the central server.
10 . The method of claim 9 , wherein all streaming data is stored in a categorized database on the central server for non-real-time access by the parental or mental health professional device.
11 . The method of claim 1 , further comprising a mechanism for law enforcement first responders to access data and receive alerts from the central server when a predetermined danger threshold is exceeded.
12 . The method of claim 1 ,
wherein the custom library model file, is configured for detecting application launches and closures by monitoring the application's execution state, and logging these events along with associated data traffic and system call activities to provide detailed insights into application usage patterns.
13 . The method of claim 1 , further including a feature for detecting grooming patterns based on the stored data using a machine learning model, and alerting the parental device.
14 . The method of claim 13 , further comprising:
analyzing the logged data using a machine learning model configured to identify specific behavioral patterns and indicators of grooming;
applying natural language processing (NLP) techniques to the content within the communications and interactions logged from the child device to enhance detection accuracy;
automatically updating the machine learning model based on new data inputs to refine the pattern recognition process over time; and,
generating and transmitting alerts to the parental device when potential grooming behavior is detected, including detailed information on the context and nature of the detected activity to assist in timely and appropriate responses.