1. A system for monitoring and mitigating parental phubbing, comprising: a mobile device with a communication interface; a processor configured to track usage data of said mobile device; and a software application stored in the memory of said mobile device, wherein the software application is programmed to: analyse the tracked usage data to determine instances of parental phubbing; generate feedback based on the analysis, aimed at reducing instances of parental phubbing; and provide educational content on the potential impacts of parental phubbing on child's psychological health.

2. The system of claim 1, wherein the software application is further programmed to: implement a machine learning model to predict potential depressive or psychopathological effects on the child based on the frequency and duration of parental phubbing; alert the user of the potential risks and suggest ways to minimize them.

3. The system of claim 1, wherein the software application is further programmed to: detect and prevent deceptive digital behaviour from the child; and provide guidance on how to encourage honest digital communication.

4. The system of claim 1, wherein the software application further comprises a parental control module that allows the user to set restrictions on device usage based on predetermined criteria.

5. The system of claim 1, wherein the feedback generated includes visual cues, auditory cues, or a combination thereof, to alert the user of excessive mobile device usage.

6. A method for monitoring and mitigating parental phubbing, comprising: tracking usage data of a mobile device; analysing the tracked usage data to determine instances of parental phubbing; generating feedback based on the analysis, aimed at reducing instances of parental phubbing; and providing educational content on the potential impacts of parental phubbing on child's psychological health.

7. The method of claim 6, further comprising: implementing a machine learning model to predict potential depressive or psychopathological effects on the child based on the frequency and duration of parental phubbing; alerting the user of the potential risks and suggesting ways to minimize them.

8. The method of claim 6, further comprising: detecting and preventing deceptive digital behaviour from the child; providing guidance on how to encourage honest digital communication.

9. The method of claim 6, further comprising providing the user with recommendations for improving their interactions with the child, such as suggested activities, conversation topics, or time management strategies.

10. The method of claim 6, further comprising applying machine learning algorithms to analyse trends in device usage data and predict future instances of parental phubbing. SYSTEM FOR MONITORING AND MITIGATING PARENTAL PHUBBING Abstract The disclosed invention relates to a system and method for monitoring and mitigating parental phubbing, addressing the use of mobile devices. The system comprises a mobile device with a communication interface, a processor to track device usage data, and a software application stored in the device's memory. The software application analyses the tracked data to identify instances of parental phubbing, generating feedback to reduce such behaviour, and providing educational content on the potential psychological impacts on a child. The system further includes features such as implementing a machine learning model to predict depressive or psychopathological effects, alerting users of potential risks and suggesting ways to minimize them, detecting and preventing deceptive digital behaviour from the child, and offering guidance on promoting honest digital communication. Additionally, the system may provide recommendations for improving parent-child interactions and employ machine learning algorithms to analyse trends and predict future instances of parental phubbing. This system and method provide a comprehensive solution to address parental phubbing and its potential effects, promoting healthier digital habits and enhancing parent-child relationships. , C , Claims:Claims :

1. A system for monitoring and mitigating parental phubbing, comprising: a mobile device with a communication interface; a processor configured to track usage data of said mobile device; and a software application stored in the memory of said mobile device, wherein the software application is programmed to: analyse the tracked usage data to determine instances of parental phubbing; generate feedback based on the analysis, aimed at reducing instances of parental phubbing; and provide educational content on the potential impacts of parental phubbing on child's psychological health.

2. The system of claim 1, wherein the software application is further programmed to: implement a machine learning model to predict potential depressive or psychopathological effects on the child based on the frequency and duration of parental phubbing; alert the user of the potential risks and suggest ways to minimize them.

3. The system of claim 1, wherein the software application is further programmed to: detect and prevent deceptive digital behaviour from the child; and provide guidance on how to encourage honest digital communication.

4. The system of claim 1, wherein the software application further comprises a parental control module that allows the user to set restrictions on device usage based on predetermined criteria.

5. The system of claim 1, wherein the feedback generated includes visual cues, auditory cues, or a combination thereof, to alert the user of excessive mobile device usage.

6. A method for monitoring and mitigating parental phubbing, comprising: tracking usage data of a mobile device; analysing the tracked usage data to determine instances of parental phubbing; generating feedback based on the analysis, aimed at reducing instances of parental phubbing; and providing educational content on the potential impacts of parental phubbing on child's psychological health.

7. The method of claim 6, further comprising: implementing a machine learning model to predict potential depressive or psychopathological effects on the child based on the frequency and duration of parental phubbing; alerting the user of the potential risks and suggesting ways to minimize them.

8. The method of claim 6, further comprising: detecting and preventing deceptive digital behaviour from the child; providing guidance on how to encourage honest digital communication.

9. The method of claim 6, further comprising providing the user with recommendations for improving their interactions with the child, such as suggested activities, conversation topics, or time management strategies.

10. The method of claim 6, further comprising applying machine learning algorithms to analyse trends in device usage data and predict future instances of parental phubbing.

Description:SYSTEM FOR MONITORING AND MITIGATING PARENTAL PHUBBING
Field of the Invention
[0001] The present invention relates to the field of monitoring and mitigating parental phubbing, specifically addressing the usage of mobile devices and software applications to track and analyse parental phubbing behaviours, provide feedback to reduce such behaviours, and offer educational content on the potential psychological impacts of parental phubbing on a child's well-being.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Phubbing, a term derived from the word’s "phone" and "snubbing," describes the phenomenon of people prioritizing their smartphones or other digital devices over their immediate social environment. This modern-day behaviour has become commonplace, influencing interpersonal relationships and family dynamics across the globe. As digital technology pervades all aspects of life, its impact extends to parent-child interactions, with mounting evidence suggesting that phubbing can negatively affect children's psychological well-being.
[0004] Depression and other forms of psychopathology constitute significant mental health issues among children, and a myriad of factors can contribute to their manifestation. Recent investigations have delved into the potential connection between parental phubbing and children's mental health outcomes. Preliminary findings suggest a concerning correlation between these two variables: children exposed to consistent parental phubbing tend to exhibit elevated depressive symptoms and signs of psychopathology. This raises questions about the unintended consequences of the digital age on children's mental health.
[0005] Simultaneously, deceptive behaviour—both in physical and digital contexts—has become a pervasive problem among children. Specifically, digital deception, which involves the manipulation of information or engagement in misleading practices via technological platforms, has seen a marked increase. With the ubiquity of digital devices and platforms, children find themselves navigating online environments that are conducive to, and even encourage, deception.
[0006] Despite the acknowledgement of these individual issues, there is a paucity of research examining the relationship between parental phubbing and children's engagement in digital deception. This is a critical research gap as understanding the interconnections between these factors could provide a more comprehensive picture of the influences of parental digital behaviour on children's mental health and deceptive tendencies.
[0007] Research into these dynamics is not just an academic exercise, but a necessary undertaking that can have far-reaching implications for families and society. By elucidating the links between parental phubbing, children's mental health, and the propensity for digital deception, we can better equip parents, educators, and policymakers to mitigate the negative effects of these phenomena. The digital age has brought about unprecedented changes in our lives, but we must ensure that these transformations do not inadvertently compromise the psychological wellbeing and ethical growth of the next generation.

Summary
[0008] The present invention relates to the field of monitoring and mitigating parental phubbing, specifically addressing the usage of mobile devices and software applications to track and analyse parental phubbing behaviours, provide feedback to reduce such behaviours, and offer educational content on the potential psychological impacts of parental phubbing on a child's well-being.
[0009] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00010] The following paragraphs provide additional support for the claims of the subject application.
[00011] In an aspect the present invention relates to a system and method for monitoring and mitigating parental phubbing, which refers to the excessive use of mobile devices by parents that leads to inattentiveness and neglect of their children. The system comprises a mobile device with a communication interface, a processor for tracking usage data, and a software application stored in the device's memory.
[00012] In an embodiment, the software application is designed to analyse the tracked usage data and identify instances of parental phubbing. By considering factors such as duration of device usage, frequency of interaction, and types of applications used, the software can pinpoint when a parent is engaged in phubbing behaviour. This analysis forms the basis for generating feedback aimed at reducing instances of parental phubbing. The feedback can be in various forms, including visual or auditory cues, to alert the user of excessive mobile device usage.
[00013] Furthermore, the software application provides educational content on the potential impacts of parental phubbing on a child's psychological health. This content, accessible within the application, raises awareness among parents about the negative effects their phubbing behaviour can have on their children, such as depression, anxiety, or reduced social competence. By educating parents, the system promotes a better understanding of the consequences of parental phubbing and encourages behaviour change.
[00014] In an advanced embodiment, the software application incorporates a machine learning model. This model predicts potential depressive or psychopathological effects on the child based on the frequency and duration of parental phubbing. By analysing the tracked usage data and identifying patterns, the model can provide insights into the potential psychological impacts on the child. This predictive capability enables the system to alert the user of the potential risks and suggest ways to minimize them, fostering a more mindful and responsible use of mobile devices.
[00015] In an embodiment, the software application also includes features to detect and prevent deceptive digital behaviour from the child. By monitoring the child's digital activity, the system can identify patterns suggestive of deceptive behaviour, such as clearing browsing history or using specific applications associated with deceptive practices. The application provides guidance to the user on how to encourage honest digital communication, ensuring a safer and more trustworthy online environment for the child.
[00016] Additionally, the software application may incorporate a parental control module, allowing the user to set restrictions on device usage based on predetermined criteria. This feature empowers parents to establish boundaries and manage their own and their child's device usage effectively.
[00017] In an embodiment, the method for monitoring and mitigating parental phubbing involves tracking the usage data of a mobile device, analysing the data to identify instances of parental phubbing, generating feedback to reduce such behaviour, and providing educational content on the potential impacts of parental phubbing on a child's psychological health. The method may also include implementing a machine learning model to predict potential psychological effects, detecting and preventing deceptive digital behaviour from the child, providing guidance on honest digital communication, offering recommendations for improving parent-child interactions, and applying machine learning algorithms to analyse trends and predict future instances of parental phubbing.
[00018] In conclusion, the system and method for monitoring and mitigating parental phubbing provide a comprehensive solution to address this widespread issue. By utilizing mobile devices, tracking usage data, and employing a software application with various features and educational content, the system promotes awareness, behaviour change, and healthier digital habits among parents, leading to improved parent-child relationships and better psychological well-being for the child.
Brief Description of the Drawings
[00019] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00020] Fig. 1 illustrates a system for monitoring and mitigating parental phubbing, in accordance with an embodiment of the present disclosure.
[00021] Fig.2 illustrates a method 200 for monitoring and mitigating parental phubbing, in accordance with an embodiment of the present disclosure.
Detailed Description
[00022] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00023] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00024] Following below are more detailed descriptions of various concepts related to, and implementations of, methods, apparatuses, and systems of determining a credibility status of an image a person. The various concepts introduced above and discussed in greater detail below may be implemented in any of numerous ways.
[00025] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00026] The present invention relates to the field of monitoring and mitigating parental phubbing, specifically addressing the usage of mobile devices and software applications to track and analyse parental phubbing behaviours, provide feedback to reduce such behaviours, and offer educational content on the potential psychological impacts of parental phubbing on a child's well-being.
[00027] Referring now to a system 100 for monitoring and mitigating parental phubbing and components/elements thereof, in accordance with embodiment of present disclosure. This disclosure describes the system 100 for monitoring and mitigating parental phubbing, which is defined as the act of parents ignoring their children in favour of using their mobile devices. The system 100 comprises a mobile device 102, a processor 104 configured to track the usage data of the mobile device 102, and a software application 108 stored in the memory 106 of the mobile device 102. This system 100 aims to reduce instances of parental phubbing and provide educational content on the potential impacts of such behaviour on a child's psychological health.
[00028] In a specific embodiment, the mobile device, labelled as 102, is a central component of the system, designated as 100. The mobile device 102 includes a communication interface, a core feature that enables it to interact and exchange data with other devices or networks. This communication interface is a hybrid of hardware and software components, designed to facilitate various forms of communication. The communication interface can connect to cellular networks, allowing the device to send and receive data, make calls, and access internet services through cellular data. It can also connect to Wi-Fi networks for data transfer and internet connectivity. The Bluetooth capability allows it to pair with other Bluetooth-enabled devices for data sharing or control purposes. This versatile communication interface helps in gathering comprehensive usage data of the mobile device 102. By tracking the device's interactions across various networks and paired devices, the system can access a wide spectrum of information, enhancing its ability to accurately identify instances of parental phubbing. This, in turn, supports more precise and effective mitigation strategies, promoting healthier digital behaviours within the family.
[00029] In a specific embodiment, the mobile device 102 houses the processor, designated as 104, configured to track comprehensive usage data. The processor 104 is designed to record a wide range of information that can be instrumental in understanding user behaviour and identifying potential instances of parental phubbing. The processor 104 captures data such as the duration of device usage, providing insights into how long the device is actively used during a given period. It also tracks the frequency of device interaction, noting each time the user engages with the device. Additionally, the processor 104 records the types of applications used, offering a window into the user's interests or priorities. Moreover, the processor 104 tracks the time spent on each application. This helps distinguish between brief interactions, like checking a text message, and prolonged usage, such as watching a movie. Lastly, it records the time of day the device is used, shedding light on usage patterns related to the user's daily routine. Collectively, this data offers a detailed picture of the user's device interaction patterns, enabling the system 100 to accurately identify instances of parental phubbing and provide targeted, effective interventions.
[00030] In an embodiment, the system 100's core functionality is driven by a specialized software application, referenced as 108, stored within the memory 106 of the mobile device 102. This software application 108 is programmed to perform numerous critical tasks, including the analysis of the tracked usage data to determine instances of parental phubbing. The software application 108 employs a blend of algorithms and heuristics to interpret the collected data, considering factors like the time of day, duration of device usage, and the types of applications being used. These algorithms are designed to identify patterns of behaviour that may suggest parental phubbing. For instance, the software might consider typical family interaction hours, such as mealtimes or early evenings. If it detects that the user is engaging in prolonged use of social media applications during these periods, it might flag this behaviour as potential parental phubbing. By utilizing advanced data analysis techniques, the software application 108 provides a valuable tool for monitoring and identifying instances of parental phubbing. This capability allows the system to generate actionable insights and suggestions to mitigate this behaviour, ultimately promoting healthier digital habits and improved parent-child interaction.
[00031] In an embodiment, upon identification of such instances, the software application 108 generates feedback aimed at reducing parental phubbing. This feedback may take various forms, such as notifications, reminders, or even gamified challenges. For instance, a notification might gently remind the user, "You've been on your phone for X minutes. Maybe it's time for a break?" Alternatively, the application could propose a daily or weekly challenge, such as "Can you reduce your screen time by 10% this week?" The feedback is designed to be non-intrusive and supportive, encouraging rather than dictating behaviour change.
[00032] In addition to its analytical capabilities, the software application 108 also serves an educational role within the system 100. It provides insightful content about the potential impacts of parental phubbing on a child's psychological well-being. This feature is critically important, as it helps raise awareness among parents who might be oblivious to the potential negative effects their excessive device usage can have on their children. The educational content provided by the software application 108 can take various forms, catering to different learning preferences. This can include written articles that delve into research findings and expert opinions, engaging videos that share real-life stories or explain concepts visually, infographics that present data in an easily digestible format, or interactive quizzes that offer a more engaging, participatory learning experience. The topics covered by this educational content range from the link between parental phubbing and child depression, to anxiety issues, and even reduced social competence in children. By presenting this information in a user-friendly format, the software application 108 encourages parents to learn about these issues, helping them understand the importance of mindful device usage for their child's psychological health.
[00033] This system 100 is not limited to one device but can be implemented on multiple devices within a household. By doing so, it provides a comprehensive picture of the parental phubbing behaviour in the family and can tailor feedback and educational content accordingly.
[00034] In one embodiment of the system 100, a feature could be integrated that enables parents to designate specific "family time" periods. During these intervals, the usage of the mobile device 102 would be minimized to foster quality interaction among family members. The software application 108 would enforce these "family time" periods by implementing a variety of strategies. For example, it could block access to certain applications that are typically high-engagement or distracting, such as social media or video streaming apps. This ensures that during these periods, the temptation to engage in non-essential digital activities is greatly reduced. Additionally, the software application 108 could be programmed to send more frequent reminders or notifications to put the device away during "family time." These reminders could be simple push notifications or even visual or auditory cues that alert the user to their device usage. This embodiment underlines the system's flexibility and its commitment to promoting healthier device habits. By enabling parents to proactively schedule periods of reduced device usage, the system encourages regular, quality family interaction, thereby mitigating potential negative impacts of parental phubbing on the child's psychological wellbeing. In another embodiment, the system might include a feature that allows for the tracking and analysis of child behaviour as well, providing a more holistic view of the family dynamics. The software could track children's device usage, mood, and school performance, further enhancing its ability to provide relevant feedback and educational content.
[00035] In an embodiment, parental phubbing is indeed a multifaceted issue, involving a myriad of psychological, social, and technological factors. However, the system 100 provides a practical, supportive solution that aids parents in effectively managing their device usage. The system 100 doesn't pass judgment but instead provides constructive feedback and guidance. It operates from the premise that most parents are not intentionally neglecting their children but may simply be unaware of the extent of their device usage or its potential impacts. The system 100’s ability to track and analyse usage data allows parents to gain insights into their device habits, possibly illuminating patterns of usage they may not have previously noticed. By providing this objective data, the system 100 enables parents to become more aware of their behaviours. Moreover, the system 100 educates parents on the potential psychological impacts of excessive device usage on their children. It offers accessible, engaging content that covers a range of relevant topics, allowing parents to understand the potential consequences of parental phubbing. In essence, the system 100 offers a holistic approach to tackling the issue of parental phubbing, combining technological tracking with psychological education to support parents in fostering healthier digital habits for the benefit of their children's psychological wellbeing.
[00036] In an advanced embodiment, the system 100 harnesses the power of machine learning algorithms to enhance the accuracy of its analysis over time. As the system 100 gathers more data about the user's behaviour and the contextual nuances of device usage, the software application 108 can learn and improve its ability to differentiate instances of parental phubbing from other types of device usage. Machine learning algorithms excel at recognizing patterns and making predictions based on large datasets. In this case, the software application 108 can be trained on extensive usage data, allowing it to uncover subtle distinctions between different types of device interactions. For instance, through continuous learning, the application 108 could discern when a parent is using the device for work-related purposes during family time versus engaging in non-essential or distracting activities. This level of differentiation helps provide more accurate feedback and insights to the user, promoting a deeper understanding of their device usage behaviours. The machine learning algorithms employed by the system also adapt and evolve as they receive more data. By continuously updating their models, the algorithms become increasingly proficient at identifying patterns specific to each user's behaviour, context, and family dynamics. Through the incorporation of machine learning, the system 100 demonstrates its ability to leverage data-driven insights to improve its accuracy and understanding of parental phubbing behaviours. This enhances the system's effectiveness in providing tailored interventions and guidance, helping parents make more informed decisions regarding their device usage and fostering a healthier balance between digital engagement and family interactions.
[00037] Additionally, the system 100 incorporates a valuable feature that offers personalized suggestions for alternative activities tailored to both the parent's interests and the child's age and preferences. This feature recognizes that reducing parental phubbing requires providing appealing alternatives to screen time that capture the parent's attention while also engaging the child. By taking into account the parent's interests, such as hobbies or activities they enjoy, the system can recommend alternatives that align with their preferences. This ensures that the suggested activities are not only seen as distractions from device usage but also as genuinely enjoyable options for the parent. Furthermore, considering the child's age and preferences allows the system to propose activities that are age-appropriate and resonate with the child's interests. This personalization increases the likelihood of successful engagement and promotes meaningful interactions between the parent and child. The personalized suggestions may encompass a wide range of possibilities, such as outdoor activities, creative projects, games, or even shared hobbies. By providing engaging alternatives that cater to the unique characteristics of both the parent and child, the system encourages quality time and reduces the inclination for parental phubbing, fostering healthier and more fulfilling interactions within the family unit.
[00038] Furthermore, the software application 108 can incorporate a dashboard that provides users with a comprehensive overview of their device usage patterns and trends, the feedback they have received, their progress in reducing parental phubbing, and access to educational content. This dashboard serves as a centralized hub of information and insights for parents, facilitating a deeper understanding of their behaviours and the potential impact on their children's psychological health. Through graphical representations, the dashboard visualizes device usage patterns, such as daily, weekly, or monthly usage trends, allowing parents to easily comprehend and reflect on their device habits. These visualizations can help identify patterns of excessive usage or highlight improvements over time, promoting self-awareness and accountability. Additionally, the dashboard presents the feedback generated by the system, providing parents with a clear and organized summary of the recommendations, reminders, or alerts they have received. This allows them to track their progress and stay motivated in reducing parental phubbing.
[00039] In another embodiment, the system 100 could integrate with other smart devices in the household, such as smart speakers, TVs, or other IoT devices. These integrations could further enhance the system's ability to monitor and mitigate parental phubbing. For instance, a parent's interaction with a smart TV or smart speaker could also be considered in the analysis of parental phubbing.
[00040] Furthermore, the system 100 incorporates a feature that enables the sharing of feedback and educational content with other family members or caregivers. This inclusive functionality ensures that everyone involved in the child's upbringing is well-informed and aligned in their efforts to reduce parental phubbing. By extending the system's reach beyond individual users, it promotes a collective understanding of the issue and encourages a collaborative approach in creating a healthier digital environment for the child. Sharing feedback and educational content among family members or caregivers fosters a unified front in addressing parental phubbing, enhancing awareness and cooperation in working towards reducing its occurrence.
[00041] In another embodiment, the system 100 could be enhanced by pairing it with a wearable device capable of tracking physiological data, including heart rate or stress levels. This additional data would provide valuable insights into the effects of parental phubbing on the parent's stress levels and overall well-being. By monitoring physiological responses, the system 100 can gauge the impact of parental phubbing and measure the effectiveness of the reduction efforts. This information empowers parents to better understand the consequences of their device usage and make informed decisions to improve their well-being. The integration of wearable technology adds a deeper layer of self-awareness and enables the system 100 to offer personalized recommendations based on real-time physiological data, fostering healthier habits and ultimately improving the parent's overall quality of life.
[00042] In an aspect, to ensure the user's privacy and data security, all data collected and analysed by the system could be encrypted and stored securely. The user could also have control over what data is collected, how it's used, and who it's shared with.
[00043] Overall, this system 100 offers a comprehensive, user-friendly, and science-based tool for monitoring and mitigating parental phubbing. By raising awareness, providing actionable feedback, and offering educational content on the impacts of parental phubbing on a child's psychological health, it aims to improve family dynamics and contribute to the well-being of parents and children alike.
[00044] In this embodiment, the software application 108 is equipped with a machine learning model that enhances its functionality. The machine learning model is specifically trained to predict potential depressive or psychopathological effects on the child by examining the frequency and duration of parental phubbing instances. By leveraging advanced machine learning algorithms, the model analyses the tracked usage data, identifying patterns and correlations between parental phubbing behaviours and potential negative impacts on the child's mental health. This predictive capability enables the system to provide proactive insights and warnings, empowering parents to take preventive measures and reduce the risk of detrimental psychological effects on their children. By integrating machine learning, the software application 108 becomes a powerful tool that goes beyond simple analysis, offering personalized predictions and guidance to ensure the well-being of the child.
[00045] In an embodiment, when the machine learning model identifies a risk level that surpasses a pre-determined threshold, the machine learning model generates an alert for the user. This alert communicates the potential risks to the child's mental health identified by the model, such as increased likelihood of depressive symptoms or other psychopathological effects. Along with this alert, the software application 108 suggests ways to minimize these risks. For instance, it could recommend reducing screen time during specific hours, engaging in certain activities with the child, or seeking professional advice.
[00046] In this embodiment, the software application 108 incorporates a vital feature for detecting and preventing deceptive digital behaviour from the child. The software application 108 monitors the child's digital activity, with appropriate permissions, and employs algorithms to identify patterns that indicate potential deceptive behaviour. For instance, it may flag frequent clearing of browsing history, usage of applications associated with deceptive practices, or sudden shifts in online activity patterns. By actively monitoring these indicators, the software application 108 helps parents stay informed about their child's digital behaviour, enabling them to address any concerns and provide guidance on fostering honest and responsible digital communication. This feature promotes a safer and more trustworthy online environment for children while empowering parents to address deceptive behaviour effectively.
[00047] In this embodiment, when the software application 108 detects patterns suggestive of deceptive digital behaviour from the child, it goes beyond just identification and takes a proactive approach by providing guidance on fostering honest digital communication. The software application 108 offers recommendations to the user, such as initiating conversations with the child about internet safety and the importance of honesty in digital interactions. It also suggests setting clear expectations for online behaviour and provides examples of appropriate and responsible digital communication. By offering this guidance, the software application 108 empowers parents to address deceptive behaviour effectively and instil ethical digital practices in their children, creating a safer and more trustworthy digital environment for all parties involved.
[00048] In an embodiment, the software application 108 further comprises a parental control module. This parental control module allows the user to set restrictions on device usage based on predetermined criteria. For instance, the user might set limits on the usage duration of certain applications, block access to certain apps or websites, or restrict device usage during specified hours. These controls provide an additional layer of intervention to help reduce parental phubbing.
[00049] In an embodiment, the feedback generated by the software application 108 includes visual cues, auditory cues, or a combination thereof, to alert the user of excessive mobile device 102 usage. Visual cues might include on-screen notifications or color-coded alerts. Auditory cues could range from subtle beeps or chimes to spoken alerts. The type and intensity of these cues could be customized based on user preferences and the severity of the excessive usage identified. For example, prolonged and intense parental phubbing might trigger a more noticeable alert to effectively draw the user's attention.
[00050] In an embodiment, through these advanced features, the software application 108 aims to provide a comprehensive tool for monitoring, understanding, and mitigating parental phubbing and its potential impacts on the child's psychological health. It uses advanced technologies such as machine learning to enhance its effectiveness, and it provides user-friendly features to support parents in managing their device usage and fostering a healthier digital environment for their children.
[00051] In an aspect, meet Sarah, a working mother who is concerned about her habit of phubbing her

[00055] The software application 108 also offers educational content on the potential impacts of parental phubbing on a child's psychological health. It includes articles, videos, or interactive modules that highlight the negative consequences of phubbing and provide tips on fostering healthy parent-child relationships.
[00056] As Sarah continues using the system 100, she becomes more aware of her phubbing behaviour and its impact on her child. The feedback from the application acts as a gentle reminder to be more present and attentive during crucial moments. Additionally, the educational content helps her understand the long-term effects of parental phubbing, motivating her to make positive changes.
[00057] Over time, Sarah's usage patterns gradually shift, and she reduces instances of parental phubbing. She becomes more engaged during quality time with her child, strengthening their bond and creating a healthier family dynamic.
[00058] Through the system's tracking, analysis, feedback generation, and educational content, Sarah successfully mitigates parental phubbing, ultimately improving her relationship with her child and creating a more nurturing environment at home.
[00059] In an aspect, the system 100 determines the complex interplay between parental phubbing, emotional intelligence, and adolescent deceptive behaviour through the lens of three types of deception: a verbal deception, a non-verbal deception, and a digital deception.
[00060] In an aspect, the system 100 may identify the influence of verbal deception in children. When parents are more engaged with their devices than their children, the children might feel neglected or less valued. In turn, they might resort to verbal deception as a means of gaining attention or avoiding negative reactions. For instance, an adolescent might lie about their accomplishments to receive praise or recognition. However, high emotional intelligence can mitigate this effect. Children with high emotional intelligence can better understand and manage their emotions, helping them recognize the unproductive nature of such deceptive behaviour. They might instead seek healthier avenues to express their feelings and needs.
[00061] In an embodiment, the system 100 may identify the influence of non-verbal deception, such as faking emotions, which can also be a response to parental phubbing. For example, an adolescent might hide their feelings of sadness or neglect behind a mask of indifference or happiness. Once again, emotional intelligence plays a crucial role here. Children with higher emotional intelligence can better recognize and express their true feelings. They are less likely to resort to non-verbal deception, as they can communicate their emotional needs more effectively.
[00062] In an embodiment, the system 100 may identify the influence of digital deception, which involves dishonest behaviour on digital platforms, such as social media. Parental phubbing, with its inherent emphasis on digital device usage, can inadvertently model such behaviour. For instance, an adolescent might create a deceptive online persona to gain attention or approval that they feel they're not receiving from their preoccupied parents. However, emotional intelligence can provide a buffer against such behaviours. Children with high emotional intelligence may be more aware of the potential consequences of such deception and may be more inclined to behave authentically, both online and offline.
[00063] In all these cases, parental phubbing can potentially promote deceptive behaviour in children by reducing quality interactions and making children feel undervalued. Emotional intelligence can act as a protective factor, enabling children to manage their feelings of neglect and their responses to it more effectively. It is crucial to remember that each adolescent is unique, and these behaviours can also be influenced by other factors, such as personality traits, peer influence, and societal norms.
[00064] The disclosure describes a method 200 for monitoring and mitigating parental phubbing, aimed at reducing the negative psychological impacts on children. This method integrates several steps, including tracking usage data of a mobile device, analysing the data, generating feedback, and providing educational content. The step 202 of the method 200 involves tracking the usage data of a mobile device. This data includes various metrics such as the duration of device usage, frequency of device interaction, types of applications used, time spent on each application, and time of day when the device is used. The data is collected by a processor within the mobile device configured specifically for this purpose. This step is vital in providing raw data about the user's behaviour on the mobile device. The step 204 involves analysing the tracked usage data to determine instances of parental phubbing. This is accomplished by a software application stored in the mobile device's memory. The software application utilizes advanced algorithms to interpret the tracked data, looking for patterns or instances that suggest parental phubbing. For example, prolonged use of social media applications during typical family interaction hours could be identified as potential parental phubbing. At step 206, based on the analysis, the software application generates feedback aimed at reducing instances of parental phubbing. The feedback may be presented in various forms such as notifications, reminders, or even gamified challenges. For instance, the application might send a notification saying, "You've been on your phone for X minutes. Maybe it's time for a break?" or propose a challenge like "Can you reduce your screen time by 10% this week?" This feedback is designed to raise awareness of the user's behaviour and encourage changes that can benefit the child's psychological health. At step 208, the method 200 includes providing educational content on the potential impacts of parental phubbing on a child's psychological health. This educational content could be presented within the software application in the form of articles, videos, infographics, or interactive quizzes. It could cover topics such as the relationship between parental phubbing and child depression, anxiety, or reduced social competence.
[00065] The described method 200 presents an effective solution for monitoring and mitigating parental phubbing, combining data-driven insights, actionable feedback, and educational support. Leveraging the power of mobile technology and machine learning, this method enables parents to cultivate mindfulness regarding their device usage and its potential consequences on their children. By offering educational resources, parents gain valuable knowledge about the psychological effects of their behaviours, empowering them to make informed decisions and drive positive change. This method provides a comprehensive approach to address parental phubbing, fostering healthier digital habits and strengthening parent-child relationships.
[00066] In an enhanced embodiment, the method 200 benefits from the implementation of a machine learning model to predict potential depressive or psychopathological effects on the child resulting from parental phubbing. By training the model on the tracked usage data and establishing correlations with known patterns of depressive or psychopathological symptoms, the system gains predictive capabilities. This enables the system to provide valuable insights into the potential impact of parental phubbing on the child's mental health, empowering parents with proactive information. By leveraging machine learning algorithms, the method enhances its ability to anticipate and address potential negative consequences, facilitating early intervention and fostering a healthier psychological well-being for the child.
[00067] In an embodiment, when the machine learning model identifies potential risks based on parental phubbing, the method includes alerting the user about these risks. The alert can take the form of notifications or messages that inform the user about the predicted impact on the child's mental health. Alongside the alert, the system provides suggestions on how to minimize these risks. These suggestions may include practical recommendations such as reducing screen time, engaging in specific activities with the child, or seeking professional guidance to address the potential psychological effects. By promptly notifying the user and offering actionable guidance, the method empowers parents to take proactive measures in mitigating the risks associated with parental phubbing and safeguarding their child's mental well-being.
[00068] In another embodiment, the method 200 encompasses the detection and prevention of deceptive digital behaviour from the child. This is achieved by monitoring the child's digital activity (with appropriate permissions) and employing algorithms to identify patterns that indicate potential deceptive behaviour. Once such patterns are detected, the method provides guidance to the user on fostering honest digital communication with the child. This guidance may include strategies to initiate open discussions about internet ethics, setting clear expectations for online behaviour, and providing examples of appropriate and responsible digital communication. By addressing deceptive behaviour and promoting honesty, the method ensures a safer and more trustworthy online environment for the child, strengthening their digital literacy and fostering healthy digital interactions.
[00069] An additional step in the method 200 involves providing the user with recommendations for enhancing their interactions with the child. This includes suggesting activities that are suitable for the child's age and align with their interests, fostering meaningful engagement. Furthermore, the method recommends conversation topics that can facilitate stronger parent-child communication, promoting open dialogue and connection. Additionally, the method offers time management strategies to ensure dedicated quality time with the child, emphasizing the importance of balancing device usage and fostering genuine interactions. By providing these recommendations, the method supports parents in creating enriching experiences and strengthening their bond with the child, further enhancing their overall relationship and well-being.
[00070] In an alternative embodiment, the method 200 could involve applying machine learning algorithms to analyse trends in device usage data and predict future instances of parental phubbing. By recognizing patterns and trends in the data, the system can proactively alert the user of potential future instances of parental phubbing, enabling preventative measures.
[00071] Variations of those preferred embodiments may become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventors expect skilled artisans to employ such variations as appropriate, and the inventors intend for the invention to be practiced otherwise than as specifically described herein. Accordingly, this invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the invention unless otherwise indicated herein or otherwise clearly contradicted by context.
[00072] As used herein, the term “wireless communication network” or “network interface” refers to a network following any suitable wireless communication standards, such as LTE-Advanced (LTE-A), LTE, Wideband Code Division Multiple Access (WCDMA), High-Speed Packet Access (HSPA), and so on. Furthermore, the communications between network devices in the wireless communication network may be performed according to any suitable generation communication protocols, including, but not limited to, the first generation (1G), the second generation (2G), 2.5G, 2.75G, the third generation (3G), the fourth generation (4G), 4.5G, the fifth generation (5G) communication protocols, and/or any other protocols either currently known or to be developed in the future.
[00073] As used herein, the term “network device” refers to a device in a wireless communication network via which a terminal device accesses the network and receives services therefrom. The network device may refer to a base station (BS) or an access point (AP), for example, a node B (Node or NB), an evolved NodeB (eNodeB or eNB), a Remote Radio Unit (RRU), a radio header (RH), a remote radio head (RRH), a relay, a low power node such as a femto, a pico, and so forth, depending on the applied terminology and technology. The “network device” or “terminal device” or “computing device” may represent any suitable device (or group of devices) capable, configured, arranged, and/or operable to enable and/or provide a terminal device access to the wireless communication network or to provide some service to a terminal device that has accessed the wireless communication network. The terminal device may include, but not limited to, a mobile phone, a cellular phone, a smart phone, a tablet, a wearable device, a personal digital assistant (PDA), portable computers, image capture terminal devices such as digital cameras, gaming terminal devices, music storage and playback appliances, wearable terminal devices, vehicle-mounted wireless terminal devices and the like. In the following description, the terms “terminal device”, “terminal”, “user equipment”, “computing device”, “network device” and “UE” may be used interchangeably.
[00074] Processing device may be provided by one or more processors such as a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00075] In addition, the present disclosure may also provide a memory containing the computer program as mentioned above, which includes machine-readable media and machine-readable transmission media. The machine-readable media may also be called computer-readable media, and may include machine-readable storage media, for example, magnetic disks, magnetic tape, optical disks, phase change memory, or an electronic memory terminal device like a random access memory (RAM), read only memory (ROM), flash memory devices, CD-ROM, DVD, Blue-ray disc and the like. The machine-readable transmission media may also be called a carrier, and may include, for example, electrical, optical, radio, acoustical or other form of propagated signals—such as carrier waves, infrared signals, and the like.
[00076] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
[00077] All references to “a/an/the element, apparatus, component, means, step, etc.” are to be interpreted as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated. The discussion above and below in respect of any of the aspects of the present disclosure is also in applicable parts relevant to any other aspect of the present disclosure.
[00078] The wordings such as “include”, “including”, “comprise” and “comprising” do not exclude elements or steps which are present but not listed in the description and the claims.
[00079] It also shall be noted that as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. This invention can be achieved by means of hardware including several different elements or by means of a suitably programmed computer. In the unit claims that list several means, several ones among these means can be specifically embodied in the same hardware item. The use of such words as first, second, third does not represent any order, which can be simply explained as names.
[00080] Various techniques may be described herein in the general context of software, hardware elements, or program modules. Generally, such modules include routines, programs, objects, elements, components, data structures, and so forth that perform particular tasks or implement particular abstract data types. The terms “module,” “functionality,” and “component” as used herein generally represent software, firmware, hardware, or a combination thereof. The features of the techniques described herein are platform-independent, meaning that the techniques may be implemented on a variety of commercial computing platforms having a variety of processors.

Claims
I/We Claim:
1. A system for monitoring and mitigating parental phubbing, comprising:
a mobile device with a communication interface;
a processor configured to track usage data of said mobile device; and
a software application stored in the memory of said mobile device, wherein the software application is programmed to:
analyse the tracked usage data to determine instances of parental phubbing;
generate feedback based on the analysis, aimed at reducing instances of parental phubbing; and
provide educational content on the potential impacts of parental phubbing on child's psychological health.
2. The system of claim 1, wherein the software application is further programmed to:
implement a machine learning model to predict potential depressive or psychopathological effects on the child based on the frequency and duration of parental phubbing;
alert the user of the potential risks and suggest ways to minimize them.
3. The system of claim 1, wherein the software application is further programmed to:
detect and prevent deceptive digital behaviour from the child; and
provide guidance on how to encourage honest digital communication.
4. The system of claim 1, wherein the software application further comprises a parental control module that allows the user to set restrictions on device usage based on predetermined criteria.
5. The system of claim 1, wherein the feedback generated includes visual cues, auditory cues, or a combination thereof, to alert the user of excessive mobile device usage.
6. A method for monitoring and mitigating parental phubbing, comprising:
tracking usage data of a mobile device;
analysing the tracked usage data to determine instances of parental phubbing;
generating feedback based on the analysis, aimed at reducing instances of parental phubbing; and
providing educational content on the potential impacts of parental phubbing on child's psychological health.
7. The method of claim 6, further comprising:
implementing a machine learning model to predict potential depressive or psychopathological effects on the child based on the frequency and duration of parental phubbing;
alerting the user of the potential risks and suggesting ways to minimize them.
8. The method of claim 6, further comprising:
detecting and preventing deceptive digital behaviour from the child;
providing guidance on how to encourage honest digital communication.
9. The method of claim 6, further comprising providing the user with recommendations for improving their interactions with the child, such as suggested activities, conversation topics, or time management strategies.
10. The method of claim 6, further comprising applying machine learning algorithms to analyse trends in device usage data and predict future instances of parental phubbing.

SYSTEM FOR MONITORING AND MITIGATING PARENTAL PHUBBING
Abstract
The disclosed invention relates to a system and method for monitoring and mitigating parental phubbing, addressing the use of mobile devices. The system comprises a mobile device with a communication interface, a processor to track device usage data, and a software application stored in the device's memory. The software application analyses the tracked data to identify instances of parental phubbing, generating feedback to reduce such behaviour, and providing educational content on the potential psychological impacts on a child. The system further includes features such as implementing a machine learning model to predict depressive or psychopathological effects, alerting users of potential risks and suggesting ways to minimize them, detecting and preventing deceptive digital behaviour from the child, and offering guidance on promoting honest digital communication. Additionally, the system may provide recommendations for improving parent-child interactions and employ machine learning algorithms to analyse trends and predict future instances of parental phubbing. This system and method provide a comprehensive solution to address parental phubbing and its potential effects, promoting healthier digital habits and enhancing parent-child relationships. , C , Claims:Claims
I/We Claim:
1. A system for monitoring and mitigating parental phubbing, comprising:
a mobile device with a communication interface;
a processor configured to track usage data of said mobile device; and
a software application stored in the memory of said mobile device, wherein the software application is programmed to:
analyse the tracked usage data to determine instances of parental phubbing;
generate feedback based on the analysis, aimed at reducing instances of parental phubbing; and
provide educational content on the potential impacts of parental phubbing on child's psychological health.
2. The system of claim 1, wherein the software application is further programmed to:
implement a machine learning model to predict potential depressive or psychopathological effects on the child based on the frequency and duration of parental phubbing;
alert the user of the potential risks and suggest ways to minimize them.
3. The system of claim 1, wherein the software application is further programmed to:
detect and prevent deceptive digital behaviour from the child; and
provide guidance on how to encourage honest digital communication.
4. The system of claim 1, wherein the software application further comprises a parental control module that allows the user to set restrictions on device usage based on predetermined criteria.
5. The system of claim 1, wherein the feedback generated includes visual cues, auditory cues, or a combination thereof, to alert the user of excessive mobile device usage.
6. A method for monitoring and mitigating parental phubbing, comprising:
tracking usage data of a mobile device;
analysing the tracked usage data to determine instances of parental phubbing;
generating feedback based on the analysis, aimed at reducing instances of parental phubbing; and
providing educational content on the potential impacts of parental phubbing on child's psychological health.
7. The method of claim 6, further comprising:
implementing a machine learning model to predict potential depressive or psychopathological effects on the child based on the frequency and duration of parental phubbing;
alerting the user of the potential risks and suggesting ways to minimize them.
8. The method of claim 6, further comprising:
detecting and preventing deceptive digital behaviour from the child;
providing guidance on how to encourage honest digital communication.
9. The method of claim 6, further comprising providing the user with recommendations for improving their interactions with the child, such as suggested activities, conversation topics, or time management strategies.
10. The method of claim 6, further comprising applying machine learning algorithms to analyse trends in device usage data and predict future instances of parental phubbing.