Life path guidance system method and apparatus

Abstract

A system for detecting and recommending life path advice to users, the system comprising: a sensor apparatus comprising a plurality of sensors configured to measure physiological parameters to assess a user need; a computing machine in communication with the sensor apparatus, wherein the computing machine is configured to: receive, from the sensor apparatus, sensor data indicating the measured physiological parameters; analyze the sensor data to detect parameters for corrective advice within the permissions of the user; determine a life path guidance based on the analysis; and generate a user interface comprising display data indicating the recommended steps to accomplish the life path guidance for display via a display device.

Description

LIFE PATH GUIDANCE SYSTEM METHOD AND APPARATUSCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of and priority to U.S. Provisional Application No. 63 / 667,483 filed July 3, 2024, the disclosure of which is incorporated by reference herein in its entirety.BACKGROUND

[0002] Growing up without strong role models or critical guidance can make the journey to becoming a responsible adult feel like navigating a complex maze without a map. Many disadvantaged or otherwise limited individuals face a myriad of life-altering decisions about education, relationships, careers, and personal values that they will make in a vacuum. The absence of trusted knowledge and mentors to offer advice and support can lead those individuals to feelings of uncertainty, self-doubt, and the fear of making costly mistakes. Without a clear framework for making informed choices, building individual and community capacities, taking responsible action, and links to the community support mechanisms, these individuals may struggle to prioritize their goals, actualize their potential, weigh the long-term consequences of their actions, and develop the self-discipline necessary to overcome obstacles. Consequently, they many times fail where others less disadvantaged succeed. These individuals must learn to rely on their own judgment and resourcefulness to establish a sense of direction and purpose in life. The lack of positive influences during this formative period can lead to a prolonged process of trial and error, as young adults strive to identify their passions, build healthy relationships, and cultivate the skills needed to thrive in an increasingly complex world.BRIEF SUMMARY

[0003] In an effort to support disadvantaged or otherwise limited individuals making life choices without proven life maturation indicators, strong role models or guidance or even understanding what sorts of community support mechanisms that are available to them, a promising solution emerges through the integration of wearable sensors, measured and calibrated human lived experiences and artificial intelligence (Al) technologies. By leveraging these tools, we can create personalized Life Guidance Machines (LGM) that provide invaluable assistance to young individuals, adults, and families navigating the complexities oflife and work affirmatively to achieve self-actualization. Self-actualization is a concept of human growth defined in psychology, often associated with humanistic psychology, which refers to the process of realizing and fulfilling one's full potential. It is the highest level of psychological development in Abraham Maslow's hierarchy of needs.

[0004] According to Maslow (1943, 1954), self-actualization by an individual is achieved when that individual is able to satisfy their lower-level needs (physiological, safety, love and belonging, and esteem) and can focus on personal growth, creativity, and fulfillment. Selfactualization is seen as an ongoing process rather than a final destination. It involves a person's continuous effort to become the best version of themselves and to realize their full potential in various aspects of life, such as personal relationships, career, and personal growth. The Life Guidance Machine uses a suite of sensors to approximate measurement of these needs. Wearable sensors, such as smartwatches or discreet body-worn devices, can collect data on the user's physical and emotional well-being, including heart rate, sleep patterns, and stress levels. The LGM can also poll and request information from the individual and respond to questions from the individual. The Life Guidance Machine can also poll and request information from the individual and respond to questions from the individual. This information is then processed and aligned by Al algorithms to pre-set life maturation indicators and capacity-building resources that analyze the data to gain insights into the individual's needs, challenges and development requirements.

[0005] The Life Guidance Machine(LGM), powered by various forms of Al comprising Generative Al, natural language processing, human lived experience feedback (inputs & outcomes), and machine learning, engages in meaningful conversations with the disadvantaged individual, offering tailored development advice, emotional support, and encouragement. By accessing vast databases of information on various topics, such as education, mental health, and career development, the Life Guidance Machine provides well-informed guidance to help the individual make sound decisions and take responsible and timely action. Additionally, the Al system can identify potential issues and proactively suggest coping strategies or connect the user with relevant support services in their community, such as counseling, mentorship programs, or skill-building workshops. The LGM assists individuals with creating custom pathways that lead to individual desired outcomes within a balanced ecosystem.

[0006] To further enhance the effectiveness of this solution, the Life Guidance Machine can adapt its communication style and recommendations based on the disadvantaged individual'sunique environmental indicators, personality, development preferences, learning style, and goals, creating a highly personalized experience. The wearable sensors continuously monitor the individual's progress and emotional state, allowing the Al to adjust its approach and offer timely interventions when needed. By harnessing the power of human experience, Al, and wearable technology, we can bridge the gap left by the absence of traditional role models and empower disadvantaged or limited individuals with the knowledge, skills, and support they need to navigate the path to responsible adulthood with greater confidence and resilience.BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0007] To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced.

[0008] FIG. 1 illustrates an aspect of the solution subject matter in accordance with one variant that using the dimensions of wellness within a unique development ecosystem.

[0009] FIG. 2 illustrates an aspect of the subject matter in accordance with one architectural variant of the solution reference as human development infrastructure (HDI™).

[0010] FIG. 3 illustrates the data acquisition process from an active user through contactbased sensors, non-contact sensors, and conversational inputs.

[0011] FIG. 4 illustrates the networked computing environment architecture including application servers, client devices, databases, and third-party system integrations.

[0012] FIG. 5 illustrates the machine learning program training process using training data to create trained models for detecting user conditions and generating recommendations.

[0013] FIG. 6 illustrates the data platform architecture showing the flow from data sources through processing, analytics, and reporting components.

[0014] FIG. 7 illustrates the hardware components of the solution machine including processors, memory, I / O components, and communication systems.

[0015] FIG. 8 illustrates the multiprocessor environment with specialized components for sensor execution, community management, and crisis response.

[0016] FIG. 9 illustrates the software architecture stack including operating system, libraries, frameworks, and applications that support the solution.DETAILED DESCRIPTION

[0017] General architectural goals of the Life Guidance Machine and Systems

[0018] System Architecture:

[0019] The SaaS platform for this solution is built on a scalable, cloud-based infrastructure (e.g., AWS, Azure, Google Cloud) to handle high traffic and data processing. Further defining this solution as Software as a Life Service (SaaLS) is an extension of the Software as a Service (SaaS) model, where software and technology are integrated into various aspects of daily life to enhance convenience, efficiency, and overall quality of life. In this scenario, software and Al would play a central role in managing, optimizing, and personalizing a wide range of life services. The term computing machine

[0020] Approximation of Life Guidance Quality of Life measurements using machine processes and sensors

[0021] The LGM uses Dimensions of Wellness as an approximation of life aspects and actualization of the clients. Some potential aspects of Software as a Life Service could comprise:Health and Wellness: o Personalized health monitoring and recommendations based on real-time data from wearables and smart home devices o Al-powered virtual health assistants for medical advice, appointment scheduling, and medication management o Predictive analytics for early disease detection and preventive care o Personalized development coaching and capacity-building resources for human growth and maturationHome and Energy Management: o Intelligent home automation systems that optimize energy consumption, comfort, and security o Predictive maintenance for home appliances and systems o Al-powered personal assistants for managing household tasks and errands Education and Learning: o Personalized learning platforms that adapt to individual learning styles, learning stages (grade levels, etc.), and preferences o Intelligent tutoring systems that provide real-time feedback and guidanceo Lifelong learning and skill development recommendations based on career goals, life maturation stages, and market trendsTransportation and Mobility: o Autonomous vehicle networks integrated with smart city infrastructure o Intelligent traffic management and route optimization o Seamless multimodal transportation planning and booking based on wellness and health development ecosystem intelligencePersonal Finance and Wealth Management: o Al-powered financial advisors that provide personalized investment strategies and financial planning o Automated expense tracking, budgeting, and savings optimization o Predictive financial risk assessment and fraud detectionSocial Interaction and Relationships: o Al-assisted matchmaking and relationship management o Personalized recommendations for social activities and events o Emotion recognition and sentiment analysis for improved communication and empathyCareer and Professional Development: o Intelligent career guidance and job matching based on skills, preferences, and market demand o Personalized learning and development plans for career growthData Alignment Service - Aligns individual goals, decisions, pathways, and actions to evidence-informed human development requirements.User Feedback Service - A direct way for users to communicate their life experiences, preferences, progress, and needs. Results can be qualitative or quantitative data, and can comprise opinions, thoughts, suggestions, and proven outcomes.

[0022] Al-powered networking and mentorship platformsThe solution variant here, follows a microservices architecture, with loosely coupled services communicating through APIs.

[0023] Components of the Solution Variant comprise:User Management Service: Handles user registration, authentication, and profile management.Data Ingestion Service: Collects data from connected devices and apps via secure APIs and data streaming protocols (e.g., MQTT, WebSockets).Data Processing Service: Cleanses, transforms, and analyzes incoming data using stream processing frameworks (e.g., Apache Spark, Flink).Machine Learning Service: Trains and deploys ML models for pattern recognition, anomaly detection, and recommendation generation. Pattern recognition is aligned to human development standards and requirements based on gender, culture, etc.Recommendation Engine: Generates personalized recommendations based on user data, ML insights, and predefined rules.Notification Service: Sends real-time alerts, notifications, and recommendations to users via mobile apps, SMS, email, and push notifications.Analytics and Reporting Service: Provides dashboards, reports, and data visualization tools for users to track their progress and insights.Personalize Pathway Mapping Services: Generates trusted pathway recommendations based on proven evidence-based outcomes. Personalized pathways are informed by previous actualization models aligned to geographical profiles, personal profiles, and goal profiles with all users deidentified.

[0024] Data Collection and Integration:Mobile Apps: Native iOS and Android apps collect user data (with consent) such as location, activity, social interactions, and app usage patterns. SDKs and libraries (e.g., Firebase, AWS Amplify) are used for efficient data collection and syncing with the cloud backend. Mobile apps will assist users with aligning all data collection, aggregation, and integration functions to human development needs and requirements. For example, it will inform the users if they’ve spent enough time in the sun to receive vitamin D benefits.loT Devices: Sensors and devices (e.g., smart home devices, environmental sensors) stream real-time data to the platform using MQTT or HTTP protocols. Secure authentication and encryption mechanisms (e.g., OAuth, SSL / TLS) ensure data privacy and integrity.Wearables: Smartwatches, fitness trackers, and other wearables collect user health and activity data using Bluetooth Low Energy (BLE) and sync with the mobile apps or directly with the cloud platform. Sensors will allow personalized mapping, and alignment supports to help users analyze and covert data into actionable outcomes.The specialized cluster of processors, memory, servers, networks and code required to accomplish the present solution will be generally referred to as a “Computing Machine” or “Life Guidance Machine”Automated Capacity-Building Resources: Custom algorithms will drive data collection and integration based on human development life cycles and life stages (age groups). Capacitybuilding frameworks will be development from human lived experience and maturation indicators and will provide proactive coaching and development support to better ready users for future life development requirements.

[0025] Data Processing and Analytics:• Streaming data is ingested and processed in real-time using Apache Kafka or AWS Kinesis. Data is transformed, enriched, and stored in a data lake (e.g., AWS S3, Google Cloud Storage) for further analysis.• Batch processing jobs (e.g., using Apache Spark, AWS EMR) periodically analyze historical data to identify patterns, trends, and correlations.• Machine learning models (e.g., clustering, classification, recommendation algorithms) are trained on the processed data to generate insights and recommendations. Frameworks like TensorFlow, PyTorch, or cloud ML services (e.g., AWS SageMaker, Google Al Platform) are used for model development and deployment.• Life positioning data and pathways are aggregated to produce new guidance models that positively impact life outcomes, while learning from lived experiences that negatively influence human behavior.

[0026] User Recommendation and Interaction:• The recommendation engine combines real-time user data, historical patterns, human development best-practices, and ML-generated insights to provide timely and relevant recommendations .• Rule-based and Al-driven recommendations are generated based on predefined criteria (e.g., location, time, user preferences) and dynamic factors (e.g., current activity, social context). The rule-based method encompasses a framework that assists individuals with balancing life decisions and actions within an ecological model for holistic development.• Recommendations are pushed to users via mobile app notifications, SMS, email, or through in-app messaging and chat interfaces.

[0027] Interactive features like chatbots, voice assistants, or gamification elements can be integrated to engage users and guide them towards positive actions.

[0028] Security and Privacy:• Security measures are implemented, including encryption of data in transit and at rest, secure authentication and authorization protocols (e.g., OAuth, JWT), and regular security audits and penetration testing.• Compliance with relevant data protection regulations (e.g., GDPR, HIPAA) is ensured through proper data handling, consent management, and privacy controls.• Access to sensitive user data is strictly controlled and monitored, with granular permissions and audit trails.• Individuals will be deidentified throughout the life guidance system.• Database clusters will be accessed via encryption keys to align data to individual pathways.

[0029] Real Time User Sensors and their Algorithms

[0030] To discern what a person might be doing at a point in time without user input and offer real-time advice, a combination of wearable sensors is necessary to capture various physiological, environmental, and contextual data. The types of sensors comprise:Motion sensors: o Accelerometer: Measures linear acceleration and can detect movement, orientation, and vibration. o Gyroscope: Measures angular velocity and helps determine orientation and rotation. o Magnetometer: Measures the Earth's magnetic field to determine compass direction. These sensors can help identify activities such as walking, running, sitting, or lying down.Location sensors: o GPS: Provides outdoor location data using satellite signals. o Indoor positioning systems (IPS): Use technologies like Wi-Fi, Bluetooth beacons, or RFID to determine indoor location. Location data can offer context about the user's environment and help provide location-specific advice.Physiological sensors: o Heart rate sensor: Measures heart rate and can help assess physical activity intensity and stress levels. o Electrodermal activity (EDA) sensor: Measures skin conductance, which can indicate emotional arousal and stress. o Skin temperature sensor: Monitors skin surface temperature, which can be affected by physical activity, emotions, and environment. o Electrocardiogram (ECG) sensor: Records the electrical activity of the heart, providing insights into heart health and stress.Environmental sensors: o Temperature sensor: Measures ambient temperature to provide context about the user's environment. o Humidity sensor: Measures air moisture levels, which can impact comfort and health. o Barometric pressure sensor: Measures atmospheric pressure, which can help determine altitude and weather changes. o Air quality sensor: Detects pollutants, particulate matter, and volatile organic compounds (VOCs) in the air.Audio sensors: o Microphone: Captures ambient sounds and can be used for voice recognition or detecting specific audio cues. Audio data can help identify the user's environment (e.g., busy streets, quiet room) and social interactions.Optical sensors: o Photodiode: Detects light intensity and can be used for ambient light sensing. o Pulse oximeter: Measures blood oxygen saturation (SpO2) and can provide insights into respiratory health. o Camera: Captures images or videos of the user's surroundings for visual context.

[0031] By combining data from these sensors, a wearable device or a network of devices can build an understanding of the user's activities, physiological state, and environment. This information can then be processed using machine learning algorithms to identify patterns, detect specific situations, and generate personalized advice or recommendations in real-time.

[0032] For example, if the sensors detect that the user is sitting for prolonged periods, has an elevated heart rate, and is in a warm, humid environment, the system could infer that the user might be experiencing discomfort or stress and suggest taking a break, performing stretches, or moving to a cooler location.

[0033] FIG. 1 represents a set of Dimensions of Wellness 102 that the current solution tracks to emulate aspects of self-actualization. The eight dimensions of wellness is a holistic approach to health and well-being that encompasses various aspects of an individual's life. These dimensions are interconnected and contribute to overall wellness. The eight dimensions are defined as the following:1. Physical wellness: Taking care of your body through regular exercise, proper nutrition, sufficient sleep, and preventive medical care.2. Emotional wellness: Being able to understand, express, and manage your emotions in a healthy way, as well as being able to cope with stress and maintain positive relationships.3. Intellectual wellness: Engaging in mentally stimulating activities, exploring new ideas, and expanding your knowledge and skills through learning and creative pursuits.4. Social wellness: Developing and maintaining meaningful relationships with others, building a support network, and contributing to your community.5. Spiritual wellness: Finding purpose and meaning in life, exploring personal values and beliefs, and engaging in practices that align with your spiritual or philosophical worldview.6. Occupational wellness: Achieving satisfaction and fulfillment through your work or chosen career path, maintaining a healthy work-life balance, and managing work-related stress.7. Financial wellness: Managing your financial resources effectively, making informed financial decisions, and setting and working towards financial goals.8. Environmental wellness: Recognizing the impact of your surroundings on your wellbeing and taking steps to create a healthy and sustainable environment, both personally and globally.

[0034] To achieve these dimensions, the solution takes input from both sensors, stored development intelligence, and queries to the user. They would approximate these dimensions with these methodologies. Examples of Data acquisition that would allow these approximations:

[0035] Physical wellness is assessed as follows:• Sensors: Fitness trackers, smartwatches, sleep monitors, smart scales• User questions: "How many minutes of exercise do you get per week?", "How many servings of fruits and vegetables do you consume daily?", "How many hours of sleep do you get per night?"• Other measurable inferences: Activity levels, heart rate variability, sleep quality, BMI, nutrient intake

[0036] Emotional wellness is assessed as follows:• Sensors: Mood tracking apps, heart rate monitors, skin conductance sensors• User questions: "On a scale of 1-10, how would you rate your stress levels today?", "How often do you experience feelings of sadness or anxiety?", "Do you have someone to talk to when you're feeling overwhelmed?"• Other measurable inferences: Mood stability, emotional resilience, stress levels, heart rate, skin conductance

[0037] Intellectual wellness is assessed as follows:• Sensors: Brain training apps, reading tracking apps, online course progress trackers• User questions: "How many books have you read in the past month?", "Have you learned any new skills recently?", "Do you engage in mentally stimulating activities like puzzles or brain teasers?"• Other measurable inferences: Cognitive function, learning progress, time spent on intellectually engaging activities

[0038] Social wellness is assessed as follows:• Sensors: Social media analytics, communication tracking apps, GPS location data• User questions: "How many social interactions do you have per week?", "Do you feel supported by your friends and family?", "How often do you participate in community events or volunteer work?"• Other measurable inferences: Social network size, communication frequency, time spent with others, community engagement

[0039] Spiritual wellness is assessed as follows:• Sensors: Meditation apps, journaling apps, GPS location data (visits to places of worship or spiritual significance)• User questions: "Do you engage in any spiritual or mindfulness practices?", "How often do you reflect on your personal values and beliefs?", "Do you feel a sense of purpose or meaning in your life?"• Other measurable inferences: Time spent on spiritual practices, frequency of selfreflection, visits to places of spiritual significance

[0040] Occupational wellness is assessed as follows:• Sensors: Productivity tracking apps, time management tools, work-life balance monitors• User questions: "On a scale of 1-10, how satisfied are you with your current job or career path?", "Do you feel you have a healthy work-life balance?", "How often do you experience work-related stress?"• Other measurable inferences: workplace satisfaction, work-life balance, productivity levels, time spent on work-related activities

[0041] Financial wellness is assessed as follows:• Sensors: Financial tracking apps, budgeting tools, investment portfolio monitors• User questions: "Do you have a personal budget?", "How often do you review your financial goals?", "Do you feel in control of your financial situation?"• Other measurable inferences: Spending habits, saving rates, debt levels, investment performance, financial literacy

[0042] Environmental wellness is assessed as follows:• Sensors: Air quality monitors, water quality testers, smart home energy monitors• User questions: "Do you take steps to reduce your environmental impact?", "How often do you spend time in nature?", "Do you feel your living and working spaces are healthy and supportive of your well-being?"• Other measurable inferences: Personal carbon footprint, personal energy consumption, time spent in nature, exposure to environmental toxins

[0043] After all of these individual dimensions are measured, the weighted dimensions are rolled up into a unified wellness metric. That metric is calculated by rolling up the values for the eight dimensions can be combined using a multi-dimensional wellness assessment model. Here's an approach:1. Standardization and Normalization of the Data: Ensure that all data inputs are standardized and normalized to a common scale (e.g., 0-100) to allow for easy comparison and aggregation across different dimensions.2. Dimensional Value Weighting: Assign appropriate weights to each dimension based on their relative importance to overall wellness. These weights may vary depending on individual preferences, cultural factors, or expert recommendations. For example, physical and emotional wellness will generally be given higher weights compared to other dimensions as they have a direct impact on the other factors.3. User Scoring: Calculate scores for each dimension using the standardized data inputs. This can be done by averaging the scores from relevant sensors, user questions, and measurable inferences within each dimension. For instance, the physical wellness score in one variant of the solution is calculated from an average of the scores from fitness trackers, sleep monitors, and user-reported exercise and nutrition habits as identified by user or credit card purchases.4. Aggregation: Combine the weighted scores across all eight dimensions to generate an overall wellness score. This can be done using a weighted average formula:Overall Wellness Score = (wl * Physical Score) + (w2 * Emotional Score) + (w3 * Intellectual Score) + (w4 * Social Score) + (w5 * Spiritual Score) + (w6 * Occupational Score) + (w7 * Financial Score) + (w8 * Environmental Score) o Where wl, w2, .., w8 are the weights assigned to each dimension.5. Visualization: Present the overall wellness score and the individual dimension scores in a visually appealing and easy-to-understand format, such as a radar chart or a bar graph. This allows users to quickly identify areas of strength and areas that need improvement.6. Recommendations: Based on the individual dimension scores and the overall wellness score, provide personal recommendations and resources to help Users improve their wellbeing. For example, if someone scores low on physical wellness, the system could suggest specific exercises, nutrition plans, or sleep hygiene tips.7. Tracking: Regularly reassess the User's wellness scores over time to track progress and adjust recommendations as needed. This can help Users stay motivated and engaged in their wellness journey, while also providing proactive strategies for a sustained quality life.8. Machine Eearning: As more data is collected from a larger population of users, machine learning algorithms can be employed to identify patterns, correlations, and predictive factors that influence overall wellness.

[0044] The eight dimensions of wellness 102 form the foundational framework for the Life Guidance Machine's assessment algorithms. Each dimension is weighted according to evidence-based psychological research, with physical wellness (for example of a solution variant, comprising 20% of the overall score) and emotional wellness (for example of a solution variant, comprising 18% of the overall score) receiving the highest weightings due to their cascading effects on other dimensions. The system employs a dynamic weighting algorithm that adjusts these percentages based on the user's age, life stage, and cultural background.

[0045] The interconnection matrix between the calculated dimensions is represented through correlation coefficients stored in the system's database. For example, occupational wellness directly correlates with financial wellness, while spiritual wellness shows a strong correlation with emotional wellness. These correlations enable the system to predict improvements in secondary dimensions when primary dimensions are addressed through targeted interventions.

[0046] Real-time dimension scoring occurs through a combination of sensor data and user input validation. The Solution employs machine learning models trained on longitudinal wellness data from a large statistically significant set of study participants to establish baseline expectations for each demographic group. Anomaly detection algorithms identify when a user's dimensional scores deviate significantly from their personal baseline or peer group averages, triggering personalized intervention recommendations.

[0047] Fig. 2 illustrates a variant of the Solution that comprises a User 202, a Contact based Sensor Array 204, a Non-Contact Sensor Array 206, a Sensor Quality Processing 208, a Life Guidance Machine 210, a Stored Goals and Parameters Data 212, a Unique User Parameters Data 214, a Functional Services Databases 216, a Correlation and Display Computing Module 218, an API to 3rd Pty systems 220, and a Cloud Computing Instance 222.

[0048] The Life Guidance Machine 210 is a Al machine that comprises a neural network coupled with generative Al and NLP(Natural Language Processing) supporting tools to allow question answering functionality coupled with the Contact based Sensor Arrays 204 input and Non Contact Sensor Array 206 inputs to approximate a human advisor who has access to extensive data to provide counseling resources. The Life Guidance Machine 210 simulates “conversations” using the Conversation Interface 224 with the User 202 by the followingprocesses: During a conversation, the Life Guidance Machine processes a conversation as follows:1. The user provides input, such as asking a question or sharing a concern.2. NLP algorithms process the user's input, breaking it down into smaller components and analyzing its meaning, intent, and alignment to best practice wellness / development standards.3. The machine's knowledge base and generative Al models work together to formulate a relevant and helpful response based on the user's input, maturation index, and wellness score, and the context of the conversation.4. The machine generates a human-like response using natural language generation techniques, considering factors like grammar, syntax, and tone.5. The response is delivered to the user, providing information, advice, or support.6. The user's feedback and the machine's performance are recorded and used to further suggest outcomes as well as to further train and improve the machine's language models and knowledge base through machine learning.

[0049] This iterative conversation process allows the Life Guidance Machine to engage in meaningful conversations, provide personalized guidance, analyze decisions / actions / processes, and continuously improve its performance based on user interactions. The combination of NLP, generative Al, and machine learning enables the machine to understand, generate, and adapt its language to effectively assist users in various situations.

[0050] To initiate the use of the Life Guidance Machine 210 a User 202 will follow this process for a User 202 experiencing a mixed set of daily environments:

[0051] During the initial setup, the person's chosen pathway, demographic information and relevant personal history are entered or downloaded into the user profile database, creating a new user profile.

[0052] The wearable device or other client device 408 collects data from the person's body and environment as they go about their daily activities.

[0053] The Sensor Quality processing 208 receives, validates and digitizes the raw sensor data from the wearable device.

[0054] The Life Guidance Machine 210 also analyzes the received data for usability and quality using the data quality assessment algorithms.

[0055] If the data is deemed not useful, the Life Guidance Machine 210 creates instructions for the user on how to obtain better measurements, and the solution requests a re-measurement.

[0056] Once the data quality is satisfactory, the Life Guidance Machine 210 processes the digitized data using machine learning algorithms, comparing it against the known data parameters in the reference database and the user's historical data from their profile.

[0057] The personalized learning algorithms in the Cloud Computing Instance 222 analyze the user's current data in the context of their historical patterns and trends, identifying any significant deviations or anomalies.

[0058] The adaptive threshold generator in the Cloud Computing Instance 222 adjusts the expected parameters for the user based on their personalized data, enabling more precise anomaly detection.

[0059] 9. If the collected data falls outside the user-specific expected parameters, the alert system in the Life Guidance Machine 210 notifies the user and suggests appropriate queries, actions or interventions based on the context of the anomaly (e.g., stress, work, exercise, sleep).

[0060] 10. The Life Guidance Machine 210 generates a report containing the relevant diagnostic parameters, taking into account the user's personalized data and trends converting them into conversational outputs to interact with the User 202 via the Correlation and Display Computing Module 218 which uses an API to 3rd Pty systems 220 to speak to the User 202 via their devices.

[0061] 11. The user- specific recommendation engine in the Cloud Computing Instance 222 generates tailored recommendations and action plans based on the user's unique data patterns and personal history.

[0062] 12. The Correlation and Display Computing Module 218 pushes the report and personalized recommendations to the User 202 who can interpret the results and make informed decisions about adjusting their activities, environment, or seeking professional advice if needed.

[0063] The Life Guidance Machine 210 stores the User 202 latest data, results, generated user feedback, alerts, and personalized recommendations in the user profile database, updating their historical record.Fig. 3 illustrate how data is acquired from an Active User 302. As previously discussed, the measurements of Users 202 parameters can occur using both Contact based Sensor Array 204and / or a Non-Contact Sensor Array 206. In the instance of an Active User 302, which for the purposes of this specification is a user who is actively receiving or producing information for the Life Machine ingestion. Data acquisition will be variable in the sense that data may be coming from one sensor type at a time, some combinations of sensor types at other time, and sometimes including Active User Conversations 312. Ideally for Active User 302 having all formats; Contact based Sensor Array 204 measurement, a Non-Contact Sensor measurement 312 are beneficial for Sensor Quality processing 208. Active User Conversation 316 is used for new data, or to assist in sensor disambiguation from the Contact Sensor Measurement 314 or the Non-Contact Sensor measurements 312 or alternatively Active User 302 inputs and Life Guidance Machine 210 outputs.

[0064] By combining data from these various contacting sensors, a picture of an individual's daily activities, health status, and overall well-being can be obtained. This information can be used to provide personalized recommendations, detect potential issues early on, and enable timely interventions when necessary. Inside of a household, real-time monitoring using contacting sensors in a household setting can help improve the quality of life for individuals, particularly those with additional needs while also providing active guidance for them. In the context of real-time monitoring of an individual as they go about their day, various types of non-contacting and contacting sensors can be used to gather data and provide insights into their activities, health, and well-being. These sensors can be worn by the individual or integrated into their household environment for seamless and continuous monitoring. Here are some examples of sensors that may be used in this scenario:1. Infrared sensors: With the individual's consent, infrared sensors can be placed in various locations around the home or in public spaces to detect the presence and movement of the individual without physical contact. These sensors could be used to monitor the individual's activity levels, detect falls, or trigger automated systems such as lighting or temperature control based on their presence.2. Video cameras with Al-based activity recognition: If the individual agrees, video cameras equipped with artificial intelligence algorithms can be installed in their home or in public areas to monitor their activities and behaviors. These Al systems can analyze the video feed to detect specific actions, such as falls, unusual behaviors, or daily routines, and provide alerts or personalized recommendations based on the individual's needs and preferences.3. Sound sensors: With the individual's permission, sound sensors can be placed in various locations to detect and analyze ambient sounds, such as voice commands, coughing, or crying. These sensors could be used to monitor the individual's well-being, detect potential health issues, or trigger automated assistance systems based on their specific needs.4. Ultrasonic sensors: If the individual consents, ultrasonic sensors can be used to detect the presence and movement of the individual without physical contact. These sensors emit high-frequency sound waves and measure the time it takes for the waves to bounce back, allowing them to detect objects or people in the vicinity. They could be used to monitor the individual's location, detect falls, or trigger automated systems based on their presence.5. Lidar sensors: With the individual's agreement, lidar (light detection and ranging) sensors can be used to create detailed 3D maps of the individual's environment and track their movements within that space. These sensors emit laser beams and measure the time it takes for the light to reflect off objects, providing accurate distance and position information. They could be used to monitor the individual's activity levels, detect falls, or navigate automated assistance systems.6. Radio-frequency identification (RFID) tags: If the individual agrees, RFID tags can be attached to objects or worn by the individual to track their location and interactions with tagged items. These tags use radio waves to communicate with RFID readers, allowing for non-contact tracking of the individual's movements and activities. This technology could be used to monitor the individual's daily routines, medication adherence, or use of specific objects in their environment.7. Wi-Fi and Bluetooth-based tracking: With the individual's consent, existing Wi-Fi and Bluetooth infrastructure can be used to track the location and movement of the individual within a given space. By measuring the signal strength and time-of-flight of Wi-Fi and Bluetooth signals emitted by the individual's devices, their position can be estimated without the need for additional sensors. This technology could be used to monitor the individual's activity levels, detect wandering behaviors, or trigger location-based services.8. Millimeter-wave sensors: If the individual permits, millimeter-wave sensors can be used to detect the presence, movement, and vital signs of the individual from a distance. These sensors emit high-frequency radio waves that can penetrate clothing and reflect off the individual's body, allowing for non-contact monitoring of their respiratory rate, heart rate, and movement patterns. This technology could be used to monitor the individual's health status, detect falls, or trigger emergency alerts based on their vital signs.

[0065] The benefits of using Contact based Sensor Array 204 and Non-Contact Sensor Array 206 in a variant of this solution allows the merger of multiple sensor inputs for the User 202 as they go about their day.

[0066] The Contact-based Sensor Array 204 further comprises multiple sensor modalities using selectably integrated sensors built into wearable devices and smart home infrastructure. Primary sensors in this solution variant further comprise: electrocardiogram (ECG) sensors for heart rhythm analysis, galvanic skin response (GSR) sensors for stress detection, accelerometers and gyroscopes for activity tracking, oximeter sensors and skin temperature sensors for circadian rhythm monitoring. Each sensor feeds data to the Sensor Quality Processing module 208 at selectable data rates and wireless frequencies to optimize data transfer.

[0067] In a variant of the solution, the Life Guidance Machine 210 employs a hybrid neural network architecture combining transformer-based language models for natural language processing with convolutional neural networks for sensor data pattern recognition. The system maintains separate processing pipelines for immediate response generation (low latency and simple computation) and deep analytical processing (moderate latency for complex correlations or calculations). The machine's knowledge base comprises a corpora of evidence-based intervention strategies that mapped to specific combinations of dimensional wellness scores and life circumstances mapped to the individual and the individual’s circumstances.

[0068] Any data flow through the solution incorporates strict privacy protocols(e.g. End-to- end encryption and federated learning.) User data remains on local edge computing devices whenever possible, with only aggregated, anonymized insights shared with the central Cloud Computing Instance 222. The API to 3rd Party Systems 220 employs authentication (for example OAuth 2.0) and implements additional counter measures (e.g. rate limiting) to prevent unauthorized access while enabling secure integration with the data systems of subscribed healthcare providers, educational institutions, and community service organizations.

[0069] FIG. 4 is a diagrammatic representation of a networked computing environment 400 in which some examples of the present solution may be implemented or deployed.

[0070] One or more application servers 406 provide server-side functionality via a network 404 to a networked user device, in the form of a client device 408 that is accessed by a user 430. A web client 412 (e.g., a browser) and a programmatic client 410 (e.g., an “app”) are hosted and execute on the web client 412.

[0071] An Application Program Interface (API) server 420 and a web server 422 provide respective programmatic and web interfaces to application servers 406. A specific application server 418 hosts an Algorithm Processor 424, which comprises components, modules and / or applications.

[0072] The web client 412 communicates with the Algorithm Processor 424 via the web interface supported by the web server 420. Similarly, the programmatic client 410 communicates with the Algorithm Processor 424 via the programmatic interface provided by the Application Program Interface (API) application, Application Program Interface (API) server 420. The third-party application 416 may, for example, be a Community organization or entity's third-party system registering support or education to a local program for the Active User 302.

[0073] The application server 418 is shown to be communicatively coupled to database servers 426 that facilitates access to an information storage repository or databases 428. In an example variant, the databases 428 comprises storage devices that store information to be published and / or processed by the Algorithm Processor 424

[0074] Additionally, a third-party application 416 executing on a third-party server 414, is shown as having programmatic access to the Client device 408 and the application servers 406 via the programmatic interface provided by the Application Program Interface (API) server 420. For example, the third-party application 416, using information retrieved from the Application Server 418, may support one or more features or functions on a website hosted by the third party.

[0075] Another example of a solution variant comprises the networked computing environment 400 implementing a mesh network topology where multiple client devices 408 can serve as data collection points and processing nodes. This distributed architecture system benefits comprise enhanced system resilience by reducing the single points of failure. Another variant of the solution uses edge computing capabilities located nearer to each client device 408 to enhance real-time processing of sensor data, reducing solution bandwidth requirements and improving response times for time-critical interventions.

[0076] Another variant of the solution uses the Algorithm Processor 424 hosted on Application Server 418 to implement a multi-stage data processing pipeline: data ingestion and validation (Stage 1 ), feature extraction and normalization (Stage 2), pattern recognition and anomaly detection (Stage 3), recommendation generation (Stage 4), and outcome tracking(Stage 5). Each stage employs different machine learning models optimized for specific tasks, with a meshing data methodology to combine the multiple model outputs for improved prediction for the individual user.

[0077] Third-party application 416 integration enables the system to interface with existing community resources and service providers. An example variant of the solution would use an API framework that supports both REST and GraphQL interfaces, allowing flexible data exchange formats. Integration examples using this API framework further comprises connection to local healthcare systems for appointment scheduling, educational platforms for learning resource recommendations, and emergency services for crisis intervention protocols.

[0078] FIG. 5 illustrates training and use of a machine-learning program 502 by the present solution according to some examples of the solutions. In some other examples of the solutions, machine-learning programs (MLPs), also referred to as machine-learning algorithms or tools, are used to perform operations associated with searches, such as job searches. In this variant of the solution, Training data 506 is used in a Machine-Learning Program Training 508 to create Trained Machine-Learning Programs 512 to execute various components of the Life Guidance Machine 210. The Training Data 506 further comprises a multi-modal dataset comprising longitudinal user interactions, validated psychological assessments, sensor measurements, and outcome tracking across diverse demographic populations. The Features 504 of the Machine Learning Program are Content 516 modules, Concepts Modules 518, Historical Data modules 522, Attribute modules 520 , User Data 524 modules. These modules represent trained data sets tailored to fine tune the training required for each Active User. The Content 516 module is a data corpus of content for a specific area of general knowledge (e.g. education opportunities, financial planning.) The Content 516 modules comprise specialized knowledge repositories comprising curated, evidence-based information across multiple life domains. In a variant of the solution the content module contains structured knowledge graphs with entities, relationships, and attributes relevant to specific life areas. The education opportunities content module comprises detailed information on educational pathways from K-12 through higher education, vocational training, professional certifications, and lifelong learning opportunities.

[0079] Educational content encompasses admission requirements, prerequisite courses, expected outcomes, career prospects, and financial implications for each educational path. The system maintains real-time connections to educational institution databases, scholarship databases, and employment statistics to provide current and accurate guidance. Content istagged with metadata including difficulty level, time requirements, geographic availability, and cultural relevance to enable personalized filtering and recommendation.

[0080] Financial planning content modules comprise information on budgeting strategies, investment options, debt management, insurance products, retirement planning, and financial goal setting. Content is regularly updated to reflect current market conditions, regulatory changes, and economic trends. The system integrates with financial data providers to offer realtime market information and personalized financial projections based on user circumstances.

[0081] Healthcare content modules encompass preventive care guidelines, mental health resources, chronic disease management strategies, and healthcare navigation assistance. Content is sourced from peer-reviewed medical literature, professional medical organizations, and validated health promotion programs. The system maintains connections to local healthcare providers and insurance networks to provide location-specific guidance.

[0082] Career development content comprises job market analysis, skill requirement trends, professional networking strategies, resume optimization techniques, and interview preparation resources. The system integrates with job posting platforms, professional social networks, and industry databases to provide real-time career opportunity identification and skills gap analysis.

[0083] The Concepts Modules 518 comprise a semantic understanding framework that capture abstract relationships between life experiences, goals, and interventions. These modules utilize knowledge representation techniques including ontologies, semantic networks, and conceptual graphs to model complex interactions between different life domains. The concept modules enable the system to understand nuanced relationships such as how career satisfaction influences family relationships or how financial stress affects physical health.

[0084] The Concepts module 518 further comprises natural language understanding capabilities that can interpret user communications about abstract concepts like "feeling stuck," "seeking purpose," or "work-life balance." Using a semantic embedding techniques map user expression to standardized psychological constructs and intervention frameworks. The system can recognize when users describe similar concepts using different terminology and provide consistent guidance regardless of communication style.

[0085] Concept modules further comprise relationship models that emulate family dynamics, social networks, and community connections. These relationship modules allows the solution to provide guidance on relationship building, conflict resolution, and social support networkdevelopment. Additionally, a cultural concept modeling ensures that guidance respects different cultural values and social norms while promoting healthy relationship patterns.

[0086] A variant of the concept module 518 further comprises goal conceptualization. Goal conceptualization would be used by the solution to help Active Users 302 translate abstract aspirations into concrete, measurable objectives. Another variant of the solution concepts module 518 further comprise the ability to decompose complex life goals into a set of steps, identify a set of potential obstacles, and suggest at least a single evidence-based strategies for goal achievement. A further variant of the solution comprise a Temporal Concept module. This variant of the module understands how goals and priorities change across different life stages and major life transitions.

[0087] Attributes modules 520 comprises demographic and other human attribute classifications further comprising anti-bias attribute filtering. Attribute Modules 520 further comprises demographic and psychographic classification systems that enable personalized guidance while implementing anti-bias filtering mechanisms. These modules recognize that effective life guidance must account for individual differences in background, circumstances, and personal characteristics while avoiding harmful stereotypes and discriminatory assumptions.

[0088] A variant of the solution further comprises an Attribute Module 520 that uses demographic attribute classification comprises age, gender, ethnicity, socioeconomic status, educational background, geographic location, family structure, and employment status. Each attribute is treated as a multidimensional construct rather than a simple category, recognizing the complexity and fluidity of each Active Users 302 identity.

[0089] Historical Data Modules 522 comprise the historical records of user interactions, interventions, and outcomes to support the Life Guidance Machine’s learning and improvement algorithms. These Historical Data Modules 522 implement data management systems that track each Active User 302 progress across multiple dimensions over extended time periods while maintaining individual privacy protections through encryption and access control mechanisms.

[0090] A variant of the solution uses a historical data architecture using time- series databases. Data compression algorithms reduce storage requirements while maintaining query performance for pattern analysis.

[0091] The Historical Module 522 uses pattern recognition algorithms analyze historical data to identify successful intervention strategies for users with similar characteristics andcircumstances. The Solution and its variants can recognize when users are experiencing situations similar to previous cases and recommend interventions that have proven effective. Anomaly detection capabilities identify unusual patterns that may indicate emerging issues requiring attention.

[0092] User Data 524 Modules implement personal data management systems that integrate sensor measurements, interaction histories, explicit user inputs, and inferred characteristics into unified user profiles related to an Active User 302.

[0093] In the solution’s real-time user data integration combines multiple data streams including wearable sensor measurements (heart rate, activity levels, sleep patterns), smartphone usage patterns, location data, social interaction patterns, and environmental factors. By using data fusion algorithms combine multiple sensor modalities to improve measurement accuracy and provide behavioral understanding. Sensor data quality assessment algorithms identify and filter unreliable measurements while maintaining data completeness.

[0094] The Machine Leaning Program 502 once trained is deployed and starts to receive New Data 510. This New Data 510 is classified and stored to be ready for updating the Machine Learning in either a batch cycle or a continuous learning format based on the needs of the variant of the solution.USE OF MACHINE LEARNING ALGORITHMS TO IMPROVE VARIOUS DETECTED DEVELOPMENT REQUIREMENTS / PROBLEMS EXPERIENCED BY A USER

[0095] When the solution variant uses an AT system, the Al System is designed to mirror the advice given by from a Counselor to an Active User 302, it incorporate algorithms and techniques that enable it to provide guidance, support, and information in a manner that emulates a responsible and caring counseling figure. The algorithmic approaches and Al techniques comprise:1. Natural Language Processing (NLP): NLP algorithms can be used to analyze and understand the User's input, whether it is text-based or spoken. This allows the Al to interpret the User's questions, concerns, or requests and provide relevant and appropriate responses. NLP techniques, such as sentiment analysis and intent recognition, can help the Al gauge the User's emotional state and tailor its advice accordingly.2. Knowledge Representation and Reasoning: To provide accurate and reliable advice, the Al system should have access to a knowledge base that covers a wide range of topicsrelevant to Users, such as education, health, relationships, and personal development. This knowledge can be represented using various techniques, such as ontologies, semantic networks, or knowledge graphs. Reasoning algorithms can then be applied to this knowledge base to draw inferences, connect related concepts, and generate appropriate advice based on the User's specific situation.3. Personalization and Adaptation: The Al system should be designed to learn and adapt to the individual User's needs, preferences, and context. This can be achieved through the use of machine learning algorithms, such as reinforcement learning or collaborative filtering, which allow the Al to continuously improve its understanding of the User based on their interactions and feedback. By personalizing its advice and recommendations, the Al can build a more effective and trustworthy relationship with the User.4. Empathy and Emotional Intelligence: To mirror the supportive and understanding nature of a counselor figure, the Al should be capable of recognizing and responding to the User's emotions in an empathetic manner. This can be achieved through the use of affective computing techniques, such as emotion recognition from facial expressions, voice tone analysis, or text sentiment analysis. By acknowledging and validating the User's feelings, the Al can provide more meaningful and comforting advice.5. Explainable Al and Transparency: It is crucial that the Al system's decision-making process is transparent and explainable to the User. This means that the Al should be able to provide clear and understandable explanations for its advice, citing relevant information sources and reasoning steps. Techniques such as rule-based systems, decision trees, or attention mechanisms can be used to generate human-interpretable explanations, helping to build trust and credibility in the Al's advice.6. Ethical and Safety Considerations: The Al system should be designed with strong ethical principles and safety measures in place. This comprises incorporating algorithms that can detect and flag potentially harmful or inappropriate content, such as explicit language, hate speech, or content that promotes risky behaviors. The Al should also have clear boundaries and limitations, knowing when to refer the User to human experts or authorities for more serious issues, such as mental health concerns or legal matters.7. Dialogue Management and Conversation Strategy: To engage the User in meaningful and productive conversations, the Al should employ dialogue management techniques that can guide the conversation flow, maintain context, and provide timely and relevant responses. This may involve using techniques such as slot filling, intent classification, andresponse generation to create a natural and coherent conversation experience. The Al should also have strategies for handling various conversation scenarios, such as providing encouragement, offering constructive criticism, or gently challenging the User's assumptions when appropriate.8. Gamification and Interactive Learning: To keep the User engaged and motivated, the Al could incorporate gamification elements and interactive learning techniques into its advicegiving process. This may involve using challenges, quizzes, or role-playing scenarios to help the User practice problem-solving skills, develop critical thinking, or explore different perspectives on a given issue. By making the learning process more enjoyable and hands- on, the Al can help the User internalize and apply the advice more effectively.

[0096] Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed. Machine learning explores the study and construction of algorithms, also referred to herein as tools that may learn from existing data and make predictions about new data. Such machine-learning tools operate by building a model from example training data in order to make data-driven predictions or decisions expressed as outputs or assessments (e.g., assessment). Although example of the solutions are presented with respect to a few machine-learning tools, the principles presented herein may be applied to other machine-learning tools.

[0097] In some example of the solutions, different machine-learning tools may be used. For example, Logistic Regression (LR), Naive-Bayes, Random Forest (RF), neural networks (NN), matrix factorization, and Support Vector Machines (SVM) tools may be used for classifying or scoring clients and their cohorts.

[0098] Two common types of problems in machine learning arc classification problems and regression problems. Classification problems, also referred to as categorization problems, aim at classifying items into one of several category values (for example, is this object an apple or an orange?). Regression algorithms aim at quantifying some items (for example, by providing a value that is a real number).

[0099] The machine-learning algorithms use features 504 for analyzing the data to generate a Detected Condition 514. Each of the features 504 is an individual measurable property of a phenomenon being observed. The concept of a feature is related to that of an explanatory variable used in statistical techniques such as linear regression. Choosing informative, discriminating, and independent features is important for the effective operation of the MLP inpattern recognition, classification, and regression. Features may be of different types, such as numeric features, strings, and graphs.

[0100] When the 512 is used to perform an assessment, new training new data 510 is provided as an input to the trained machine-learning program 412, and the trained machine-learning program 412 generates the Detected Condition 514 as output.

[0101] FIG. 6 is another example of a platform variant of the present solution. The solution comprises a Data Source 602, a Human Modified Data 604, a SQL Service 606, a Data Lake / Blob 608, a Data Platform 610, an Analytics and Machine Learning platform 612, a Reporting Platform 614, and a Machine Data Sources 616 and any other Data Source 602 that is a hybrid. The primary origin of raw new data as the Data Source 602 is gathered from any measured set of sensors that generates or collects information. This could range from web applications, sensors, user interactions, to traditional databases. This raw data, in its unprocessed form, is the foundational building block for all subsequent operations and interactions. This could range from web applications, sensors, user interactions, to traditional databases. This raw data, in its unprocessed form, is the foundational building block for all subsequent operations and interactions.

[0102] Human Modified Data 604 represents altered data collected from the source, where there might be instances where human intervention is needed for rectifications, additions, or modifications. The Human Modified Data 604 represents this manually altered data. Human modifications comprise data cleaning operations, annotation of sensor readings for training purposes, manual labeling of user behavior patterns, and expert corrections to automated classifications. The platform maintains audit trails of all human modifications to ensure traceability and compliance with data governance requirements.

[0103] SQL Service 606 is a pivotal technology in the solution’s data ecosystem, the SQL Service 606 offers structured querying capabilities to retrieve, manipulate, and manage data. Whether it is fetching data from the original source or accessing human-modified data, the SQL service ensures that data can be accessed in a structured, efficient, and reliable manner. Its interaction with the Human Modified Data 604 also guarantees that any manual modifications are query able and integrated into the overall data flow.

[0104] Data Lake I Blob 608 is a data solution providing a repository for storing large amounts of raw data in its native format, be it structured, semi-structured, or unstructured. By directly interacting with the SQL Service 606, it ensures that data, irrespective of its source,can be stored and accessed without constraints, he Data Lake / Blob 608 is that solution, providing a repository for storing vast amounts of raw data in its native format, be it structured, semi-structured, or unstructured. By directly interacting with the SQL Service 606, it ensures that data, irrespective of its source, can be stored and accessed. The Data Lake / Blob 608 implements hierarchical storage management, automatic data tiering based on access patterns, and intelligent data lifecycle policies to optimize storage costs while maintaining query performance for the Life Guidance Machine's analytical requirements.

[0105] Data Platform 610 acts as a hub that orchestrates the movement, transformation, and storage of data. It interacts with the Data Lake / Blob 608 to fetch data, utilizes the SQL Service 606 to query and transform the data, and ensures that the Human Modified Data 604 is integrated. The platform comprises the infrastructure and tools required to handle, process, and route data to various other components. An example of Data Platform 610 comprises Azure Synapse Analytics, formerly known as Azure SQL Data Warehouse, which is an integrated analytics service provided by Microsoft Azure. The Data Platform 610 implements data orchestration workflows, real-time stream processing capabilities, and automated data quality monitoring to ensure reliable data delivery for life guidance decision-making processes.

[0106] Analytics and Machine Learning platform 612 represents the analytics computing processor for the platform. By extracting data from the Data Platform 610, the Analytics and Machine Learning platform 612 applies algorithms, statistical models, and machine learning techniques to find insights, make statistical predictions, and support decision-making processes. Its interaction with Machine Data Sources 616 ensures that machine-generated data can also be used for analytical purposes, enriching the overall analysis. An example of this platform comprises Databricks, which is a cloud-based platform for big data analytics and machine learning. The platform implements specialized algorithms for wellness dimension scoring, intervention effectiveness prediction, crisis detection, and personalized recommendation generation tailored to individual user profiles and life circumstances.

[0107] Reporting Platform 614: After analyzing data, the insights drawn need to be presented in a comprehensible manner for stakeholders. The Reporting Platform 614 does that function by generating reports for the users of the Solution. By getting data from the Analytics and Machine Learning platform 612, the Reporting Platform 614 generates visualizations, dashboards, and reports that condense large amounts of information into formats that will drive the understanding of its Active Users 302 and other Users.

[0108] Machine Data Sources 616 is a large data component of the present solution, much of the data will arise from Machine Data Sources from sensors. The Machine Data Sources 616 represent this data, encompassing logs, sensor readings, and automation outputs, represent the expanding universe of loT and automation-generated data that characterizes today's technological landscape. In the present solution, much of the data from Machine Data Sources derived from various sensors integrated throughout the user's environment. The Machine Data Sources 616 encompass this data, including sensor logs, real-time sensor readings, and automation outputs from smart home devices, wearable technology, and environmental monitoring systems.

[0109] FIG. 7 is a diagrammatic representation of a variant of the solution machine 702 implementing the current solution within which instructions 712 (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine 702 and its Processors 710, Processor 714 to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions 712 may cause the machine 702 to execute any one or more of the methods described herein. The instructions 712 transform the general, non-programmed machine 702 into a particular machine 702 programmed to carry out the described and illustrated functions in the manner described. The machine 702 may operate as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine 702 may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine 702 may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a PDA, a cellular telephone, a smart phone, a mobile device, a wearable device, other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions 712, sequentially or otherwise, that specify actions to be taken by the machine 702. Further, while only a single machine 702 is illustrated, the term “machine” shall also be taken to comprise a collection of machines that individually or jointly execute the instructions 712 to perform any one or more of the methodologies of this solution as discussed herein.

[0110] The machine 702 may comprise processors 706, memory 708, and I / O components 704, which may be configured to communicate with each other via a bus 742. In an example of the solution, the processors 706 (e.g., a Central Processing Unit (CPU), a Reduced InstructionSet Computing (RISC) Processor, a Complex Instruction Set Computing (CISC) Processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an ASIC, a Radio- Frequency Integrated Circuit (RFIC), another Processor, or any suitable combination thereof) may comprise, for example, a Processor 710 and a Processor 714 that execute the instructions 712. The term “Processor” is intended to comprise multi-core processors that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. Although FIG. 7 shows multiple processors 706, the machine 702 may comprise a single Processor with a single core, a single Processor with multiple cores (e.g., a multi-core Processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof.

[0111] The memory 708 comprises a main memory 716, a static memory 718, and a storage unit 720, both accessible to the processors 706 via the bus 742. The main memory 716, the static memory 718, and storage unit 720 store the instructions 712 embodying any one or more of the methodologies or functions described herein. The instructions 712 may also reside, completely or partially, within the main memory 716, within the static memory 718, within machine-readable medium 722 within the storage unit 720 within at least one of the processors 706 (e.g., within the Processor's cache memory), or any suitable combination thereof, during execution thereof by the machine 902.

[0112] The I / O components 704 may comprise a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I / O components 704 that are comprised in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones may comprise a touch input device or other such input mechanisms, while a headless server machine will likely not comprise such a touch input device. It will be appreciated that the I / O components 704 may comprise many other components that are not shown in FIG. 7. In various example of the solutions, the I / O components 704 may comprise output components 728 and input components 730. The output components 728 may comprise visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input components 730 may comprise alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, aphoto-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and / or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like.

[0113] In further example of the solutions, the I / O components 704 may comprise biometric components 732, motion components 734, environmental components 736, or position components 738, among a wide array of other components. For example, the biometric components 732 of this solution comprise components to detect expressions related to emotional or physical distress (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye-tracking), measure bio-signals indicative of physical conditions (e.g., blood pressure, heart rate, body temperature, perspiration, levels of carbon dioxide / other chemicals in blood work, or brain waves), identify a personal individual characteristics related to an Active User 302 (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram-based identification), and the like.

[0114] Communication may be implemented using a wide variety of technologies. The I / O components 704 further comprise communication components 740 operable to couple the machine 702 to a network 724 or devices 726 via respective coupling or connections. For example, the communication components 740 may comprise a network interface Component or another suitable device to interface with the network 724. In further examples, the communication components 740 may comprise wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices 726 may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB).

[0115] Moreover, the communication components 740 may detect identifiers or comprise components operable to detect identifiers. For example, the communication components 740 may comprise Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect onedimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode,PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components 740, such as location via Internet Protocol (IP) geolocation, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth.

[0116] The various memories (e.g., main memory 716, static memory 718, and / or memory of the processors 706) and / or storage unit 720 may store one or more sets of instructions and data structures (e.g., software) embodying or used by any one or more of the methodologies or functions described herein. These instructions (e.g., the instructions 712), when executed by processors 706, cause various operations to implement the disclosed examples of the solutions.

[0117] The instructions 712 may be transmitted or received over the network 724, using a transmission medium, via a network interface device (e.g., a network interface component comprised in the communication position components 738) and using any one of several well- known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions 710 may be transmitted or received using a transmission medium via a coupling (e.g., a peer-to- peer coupling) to the devices 726.

[0118] As previously mentioned, this solution utilizes machine learning methodology to create a training solution for the data processed by each user and each client processed using this solution. By continuously measuring results of assessment outcomes and comparing it to the scoring algorithm, the present solution improves the predicted clinical and consumer diagnosis.

[0119] Turning now to FIG. 8, a diagrammatic representation of a multiprocessor processing environment 802 of the present solution is shown, which comprises the Processor 808, the Processor 810, and a Processor 804 (e.g., a GPU, CPU or combination thereof).

[0120] The Processor 804 is shown to be coupled to a power source 806, and to comprise (either permanently configured or temporarily instantiated) modules, namely in this example of a solution variant, a Sensor execution component 812, a Community execution component 814 and a Crisis Management Component 816. The Crisis Management Component 816 operationally controls, Sensor Quality processing 208 and manages the Contact based Sensor Array 204 and the Non Contact Sensor Array 206 sensing parameters on the User 202, the Community execution component 814 operationally manages Consumer side data for the benefit of the Active User, and the Sensor execution component 812 operationally manages data reporting to 3rd party systems like Functional Services Databases 216 and other ClinicalPlatforms. As illustrated, the Processor 804 is communicatively coupled to both the Processor 810 and Processor 808, and receives commands from the Processor 804, as well as commands from the Processor 808. The Sensor execution component 812 would be responsible for processing and analyzing data from various sensors connected to the Life Guidance Machine. This component is designed as a dedicated processor chip optimized for sensor data processing and real-time analysis. The chip has specialized circuitry and algorithms to handle diverse sensor inputs, such as visual, audio, and biometric data. It performs tasks like image and video processing, speech recognition, and pattern detection to extract meaningful information from the sensor data. The Sensor execution component 812 would work in conjunction with the machine's main processor to provide real-time insights and trigger appropriate actions based on the analyzed sensor data. For example, if the sensors detect a fall or abnormal vital signs, the Sensor execution component 812 would immediately process this information and alert the main system to initiate emergency protocols.

[0121] The Community execution component 814 would be responsible for managing the Life Guidance Machine's interactions and communications with the user's community and support network. This component would be implemented as a dedicated processor chip optimized for communication, data sharing, and collaboration. The chip would have built-in features for secure communication protocols, data encryption, and authentication to ensure the privacy and security of sensitive information. It would facilitate seamless integration with various communication channels, such as messaging platforms, social networks, and teleconferencing systems. The Community execution component 814 it would enable the Life Guidance Machine to connect with the user's designated family members, caregivers, healthcare providers, and emergency contacts. It would handle tasks like sending alerts, sharing updates, and coordinating care efforts among the community members involved in the user's well-being.

[0122] The Crisis Management Component 816 would be a critical processor chip designed to handle emergency situations and crisis scenarios. This component would have dedicated algorithms and protocols to detect and respond to potential crises, such as medical emergencies, falls, or emotional distress. It would continuously monitor data from the Sensor execution component 812 and analyze patterns and anomalies that may indicate a crisis situation. The Crisis Management Component 816 would have predefined response mechanisms and decisionmaking capabilities to initiate appropriate actions in case of an emergency. For example, if a severe medical emergency is detected, the Crisis Management Component 816 wouldimmediately trigger an alert to emergency services, providing them with the user's location, vital signs, and relevant medical information.

[0123] It would also notify the user's designated emergency contacts and provide them with real-time updates on the situation. The Crisis Management Component 816 would have failsafe mechanisms and redundancy measures to ensure reliable operation even in the face of system failures or network disruptions.

[0124] The following example is a non-limiting manner of enabling this solution. The solution also contemplates using other analogous hardware or software implementation than those explicitly mentioned.TECHNOLOGY AND INFRASTRUCTURE

[0125] This example solution is delivered over the Internet using virtual machines (VMs) for web and database servers, (e.g., AZURE web apps to host web applications, and AZURE blob storage for content storage. The solution deploys dynamically generated application features as well as derived or published content via various servers. The web applications are developed with any modern app development environment (e.g., Customer-facing solutions are developed in .NET Core, MVC Framework, C #, Angular, and SQL formats using Web 2.0 functionality standards.DATA CENTERS

[0126] An example of the data center implementation uses 3rdparty data centers like those provided by GOOGLE, AMAZON, MICROSOFT vendors that comply with stringent data privacy requirement from the jurisdiction served by the platform.SERVERS AND CONTENT HOSTING TECHNOLOGY

[0127] The solution hosts on INTEL or AMD processors using cloud-hosted server hardware on using various software formats for virtual machines, (e.g., Windows Server 2018.) Content, especially Video and other dynamic content is hosted by various media services (e.g. AZURE MEDIA SERVICE and BRIGHT COVE). All servers are set up in a high-availability fashion to ensure ultimate up-time, an active server takes in all the traffic and a warm backup server is on stand-by with continuous synchronization. This allows the solution to react to a server going down at any one point in time, the second server automatically kicks in without any manualintervention. Disaster recovery is also in place to ensure automatic failover in case of a data center outage.SOLUTION LOAD BALANCING FOR THE APPLICATIONS AND DATA

[0128] In this example of the solution, use of a 3rdparty (e.g., AZURE) hardware-based load balancing distributes end-user connections across a plurality of distributed servers. This enables greater resiliency, a balanced server load, with enhanced fault tolerance on the various applications deployed by this solution.CONTENT DELIVERY NETWORK

[0129] Content is held in a resilient blob storage that automatically replicates data to help guard against unexpected hardware failures. Content storage is triple-redundant with an option of geo-constrained redundant storage by jurisdictions that limit transfer of privacy related data. Content used in this context refers to content of all types and data of all types related to User Advisory (e.g., educational literature, medical data and records, other global data and records)DATA BACK-UP AND SECURITY

[0130] The solution uses multiple levels of backups to ensure data resiliencies. Full backups of data are created daily with transactional hourly backups. Copies of the backups are also physically or virtually sent to off-site facilities to ensure the highest level of protection and resilience for customer data.

[0131] FIG. 9 is a solution block diagram 902 illustrating a software architecture 806 representative of the current solution, which can be installed on any one or more of the devices described herein. The software architecture 906 is supported by hardware such as a machine 904 that comprises processors 922, memory 928, and I / O components 940. In this example, the software architecture 906 can be conceptualized as a stack of layers, where each layer provides a particular functionality. The software architecture 906 comprises layers such as an operating system kernel 916, libraries 912, frameworks 910, and applications 908. Operationally, the applications 908 invoke API calls 952 through the software stack and receive messages 954 in response to the API calls 952.

[0132] The operating system 914 manages hardware resources and provides common services. The operating system 914 comprises, for example, a kernel 916, services 918, and drivers 924. The operating system 914 acts as an abstraction layer between the hardware and the othersoftware layers. For example, the kernel 916 provides memory management, Processor management (e.g., scheduling), component management, networking, and security settings, among other functionalities. The services 918 can provide other common services for the other software layers. The drivers 924 are responsible for controlling or interfacing with the underlying hardware. For instance, the drivers 924 can comprise display drivers, camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), WLFI® drivers, audio drivers, power management drivers, and so forth.

[0133] The libraries 912 provide a low-level common infrastructure used by the applications 908. The libraries 912 can comprise system libraries 920 (e.g., C standard library) that provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries 912 can comprise API libraries 926 such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as Moving Picture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC), Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group (JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries (e.g., an OpenGL framework used to render in two dimensions (2D) and three dimensions (3D) in a graphic content on a display), database libraries (e.g., SQLite to provide various relational database functions), web libraries (e.g., WebKit to provide web browsing functionality), and the like. The libraries 912 can also comprise a wide variety of other libraries 930 to provide many other APIs to the applications 908.

[0134] The frameworks 910 provide a high-level common infrastructure that is used by the applications 908. For example, the frameworks 910 provide various graphical user interface (GUI) functions, high-level resource management, and high-level location services. The frameworks 910 can provide a broad spectrum of other APIs that can be used by the applications 908, some of which may be specific to a particular operating system or platform.

[0135] In an example of the solution, the applications 908 comprise a Measuring App 936, a contacts application 932, a browser application 934 for recommended treatment or educational content, a location application 944 to capture where treatment is occurring, a media application 946, a messaging application 948, , and a broad assortment of other applications such as a third-party application 942. The applications 908 are programs that execute functions definedin the programs. Various programming languages can be employed to create one or more of the applications 908, structured in a variety of manners, such as object-oriented programming languages (e.g., Objective-C, Java, or C++) or procedural programming languages (e.g., C or assembly language). In a specific example, the third-party application 942 (e.g., an application developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of the particular platform) may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or another mobile operating system. In this example, the third-party application 942 can invoke the API provided by the operating system 914 to facilitate functionality described herein.

[0136] Teen Examples of Life Path Guidance

[0137] Teen Example 1: Pursuing a Career in Medicine

[0138] Tina, a 16-year-old girl from a low-income family, dreams of becoming a doctor but lacks guidance and resources. The Life Guidance Machine, equipped with Al and various sensors, helps Tina navigate her path to a medical career. The Life Guidance Machine engages Tina in conversations about her interests, strengths, and challenges. Through natural language processing, it interprets her responses and provides tailored advice. To the extent it is permitted, the machine accesses Tina's school records as well as other psychometric testing to assess her capacity and learning potential. The machine then suggests online resources for learning about the medical field, such as educational videos, articles, and forums where she can connect with mentors. The machine also provides sample entrance examinations for Tina to understand and practice with. The Life Guidance Machine also helps Tina create a study plan to excel in her high school science and math courses that are essential skills. It recommends study techniques, provides practice questions, and offers feedback and reinforcement for Tina on her progress using Life Guidance Machine learning algorithms that analyze her performance. To support Tina's mental well-being, the Life Guidance Machine uses non-contact sensors to monitor her stress levels and sleep patterns. It offers relaxation techniques and encourages her to prioritize self-care. When Tina expresses doubt or frustration, the Al system provides emotional support and motivation. The Life Guidance Machine also assists Tina in finding scholarship opportunities and guides her through the college application process. It helps her craft a compelling personal statement and connects her with mentors in the medical field for advice and inspiration. Throughout Tina's educational journey, the Life Guidance Machine adapts its guidance based on her progress and changing needs. It celebrates her successes andhelps her learn from setbacks. By the time Tina graduates from high school, she feels confident and prepared to pursue her dream of becoming a doctor, thanks to the support and guidance provided by the Life Guidance Machine.

[0139] Teen Example 2: Overcoming Social Anxiety / Autism Assessment

[0140] Ethan, a 14-year-old boy, struggles with social anxiety, making it difficult for him to form friendships and participate in class. The Life Guidance Machine helps Ethan develop social skills and build confidence while also providing assessment measurements if permitted for Ethan who is also suspected of being on the Autism Spectrum. The Life Guidance Machine engages Ethan in conversations about his feelings and experiences and listens to his responses. Using natural language processing, it identifies patterns in his responses that indicate social anxiety, as well as patterns of expected responses that would suggest an Autism diagnosis. It provides information about social anxiety disorder and reassures Ethan that he is not alone. Alternatively, if Ethan is being assessed for Autism, the machine may interact via API with a professional care giver giving Ethan treatment for Autism to assess the effectiveness of his treatment. The Life Guidance Machine suggests coping strategies for managing anxiety, such as deep breathing exercises and positive self-talk. It also recommends gradual exposure to social situations, starting with small, manageable steps like saying hello to a classmate or asking a question in class. Using machine learning algorithms, the Life Guidance Machine analyzes Ethan's progress both in reported and measured sensor readings like quickened heart rate or breathing and adjusts its recommendations or real-time recommendations accordingly. It provides positive reinforcement for his efforts and encourages him to celebrate his successes, no matter how small. The machine also helps Ethan identify his interests and connects him with online communities where he can interact with like-minded individuals. It suggests conversation starters and guides him through social interactions, helping him build confidence in his abilities. To support Ethan's overall well-being, the Life Guidance Machine uses contact sensors to monitor his physical responses to anxiety, such as increased heart rate or sweating. It offers relaxation techniques and encourages him to engage in activities that promote mental health, such as exercise and creative pursuits. As Ethan continues to work with the Life Guidance Machine, he gradually becomes more comfortable in social situations. He starts participating more in class, make friends, and find others that share his interest. If permitted other individuals may be connected if the machine finds similar interests or The machine'sguidance and support have helped Ethan develop the skills and confidence needed to overcome his social anxiety and pursue his goals.

[0141] Teen Example 3: Discovering a Passion for Programming

[0142] Maya, a 15-year-old girl, feels unsure about her future and lacks direction. The Life Guidance Machine helps Maya explore her interests and discover a passion for programming. The Life Guidance Machine engages Maya in conversations about her hobbies, favorite subjects, and career aspirations. Using natural language processing, it identifies her strong problem-solving skills and interest in technology. The Life Guidance Machine suggests that Maya explore programming as a potential career path. It provides resources for learning programming languages, such as online tutorials, coding challenges, and beginner-friendly projects. As Maya begins to learn programming, the machine uses machine learning algorithms to analyze her code and provide feedback on her progress. It offers suggestions for improving her code and encourages her to experiment with different approaches. The Life Guidance Machine also connects Maya with online communities of programmers where she can ask questions, collaborate on projects, and learn from more experienced developers. It suggests hackathons and coding competitions where she can put her skills to the test and network with other aspiring programmers. To support Maya's learning, the Life Guidance Machine recommends books, podcasts, and videos related to programming and technology. It also encourages her to explore the real-world applications of programming, such as developing mobile apps or creating websites for local businesses. As Maya's skills grow, the machine helps her identify potential career paths in programming, such as software development, web design, or data analysis. It provides information about the education and experience required for these careers and suggests steps Maya can take to prepare herself. In addition, the machine provide test and suggests others for Maya to study with complete preparation. Through the guidance, feedback, and support of the Life Guidance Machine, Maya discovers a passion for programming and develops a clear sense of direction for her future. She feels excited and motivated to pursue a career in technology, knowing that she has the skills and resources to succeed.

[0143] Teen Example 4: Coping with Family Conflict

[0144] Jayden, a 13-year-old boy, lives in a household with frequent family conflicts. The Life Guidance Machine helps Jayden cope with the stress and emotional turmoil caused by his home environment. The machine engages Jayden in conversations about his family situation, using natural language processing to identify the sources of conflict and the impact on his wellbeing. It provides a safe space for Jayden to express his feelings and validates his experiences. The Life Guidance Machine offers coping strategies for dealing with family conflict, such as deep breathing exercises, mindfulness techniques, and journaling. It encourages Jayden to focus on the aspects of his life that he can control, such as his schoolwork and friendships. To help Jayden build resilience, the machine suggests activities that promote self-care and emotional well-being, such as exercise, creative pursuits, and spending time in nature. It also provides information about healthy communication skills and conflict resolution techniques that Jayden can use when interacting with his family members. The Life Guidance Machine uses noncontact sensors to monitor Jayden's stress levels and sleep patterns. When it detects signs of elevated stress or sleep disturbances, it offers relaxation techniques and encourages Jayden to reach out for support from trusted adults or professionals. The machine also helps Jayden identify his strengths and builds his self-esteem. It provides positive reinforcement for his accomplishments and encourages him to set goals for himself, both academically and personally. As Jayden continues to work with the Life Guidance Machine, he develops a greater sense of self-awareness and emotional regulation. He learns to prioritize his own wellbeing and starts to build a support system outside of his family, including friends, teachers, and counselors. Although the Life Guidance Machine cannot change Jayden's family situation, it provides him with the tools and support needed to cope with the challenges he faces at home. Jayden feels more resilient and better equipped to navigate the difficulties in his life, knowing that he has a constant source of guidance and support in the Life Guidance Machine.

[0145] Adult Examples of Life Path Guidance

[0146] Adult Example 1: Accessing Mental Health Support

[0147] Sarah, a 35-year-old single mother, has been struggling with depression and anxiety. The Life Guidance Machine helps Sarah identify and access mental health resources in her community. Through natural language processing and other Al processes, the machine engages Sarah in conversations about her emotional well-being. It provides a safe space for her to express her feelings and validates her experiences. The machine then uses its various databasesto identify mental health services available in Sarah's area, comprising low-cost counseling, support groups, and community mental health centers. The Life Guidance Machine helps Sarah navigate the process of accessing these services, providing step-by-step guidance on how to schedule appointments, apply for financial assistance, and prepare for her first counseling session. It also offers coping strategies and relaxation techniques that Sarah can use in her daily life to manage her symptoms. To the extent the service are permitted, via API, the machine schedules and reminds Sarah to attend her appointments. As Sarah begins to engage with the mental health resources in her community, the Life Guidance Machine continues to provide support and encouragement. It helps her track her progress, celebrates her successes, and adjusts its recommendations based on her changing needs.

[0148] Adult Example 2: Finding Employment Assistance

[0149] John, a 45 -year-old recently laid-off factory worker, is struggling to find new employment. The Life Guidance Machine helps John identify and access job training and placement services in his community. Additionally, if necessary, the machine also helps John find legal assistance if the facts of his layoff or termination would indicate a bias or an actionable set of facts. In this case if there are enough individuals using similar Life Guidance Machines and if they are so permitted to share limited details to allow detection of discriminatory behavior on the part of the employer. Through conversations with John, the machine learns about his work experience, skills, and career goals. It then searches its database to identify local resources that can help John in his job search, such as career counseling services, job fairs, and vocational training programs. The Life Guidance Machine provides John with information about these resources, including eligibility requirements, application processes, and potential benefits. It helps him create or edit his strong resume and cover letter and offers tips for networking and interviewing. As John begins to engage with the employment assistance programs in his community, the machine continues to provide guidance and support. It helps him track his job applications, prepare for interviews, and negotiate job offers. When John faces setbacks or rejections, the machine offers encouragement and helps him maintain a positive outlook.

[0150] Adult Example 3: Securing Housing Assistance

[0151] Adeline, a 55-year-old woman, is at risk of losing her home due to financial hardship. The Life Guidance Machine helps Adeline identify and access housing assistance programs in her community. Through conversations with Adeline, the machine learns about her financial situation and housing needs that are particular to her life path. It then searches its database to identify local resources that can help Adeline maintain stable housing, such as rental assistance programs, housing counseling services, and affordable housing options. The Life Guidance Machine provides Adeline with information about these resources, including eligibility requirements, application processes, and potential benefits. It helps her gather the necessary documentation and complete the application forms. As Adeline begins to engage with the housing assistance programs in her community, the machine continues to provide guidance and support. It helps her communicate with housing authorities and landlords and offers advice on budgeting and financial management to help her maintain stable housing in the long-term.

[0152] Adult Example 4: Accessing Healthcare Services

[0153] Ezra, a 50-year-old man with a chronic health condition, has been struggling to afford his medical care. The Life Guidance Machine helps Ezra identify and access healthcare resources in his community. Through conversations with Ezra, the machine learns about his health condition and financial situation. It then searches its database to identify local resources that can help Ezra access affordable healthcare, such as community health clinics, financial assistance programs, prescription assistance programs, and health insurance navigators. The Life Guidance Machine provides Ezra with information about these resources, including eligibility requirements, application processes, and potential benefits. It helps him gather the necessary documentation and complete the application forms. As Ezra begins to engage with the healthcare resources in his community, the machine continues to provide guidance and support. It helps him communicate with healthcare providers, track his appointments and medications, and offers advice on managing his chronic condition through lifestyle changes and self-care practices. Through the support of the Life Guidance Machine, Ezra is able to access the healthcare services he needs to manage his condition and improve his overall health and well-being. He feels more in control of his health and better equipped to navigate the complex healthcare system, knowing that he has a constant source of guidance and support in the Life Guidance Machine.

[0154] Crisis Management Examples of the Life Path Guidance;

[0155] Crisis Management Example 1: Domestic Violence Situation

[0156] Li, a 35-ycar-old woman, is experiencing domestic violence at the hands of her partner. The Life Guidance Machine detects signs of distress during conversations with Li and identifies the need for immediate intervention. Using NLP, the machine analyzes Li's responses and recognizes keywords and phrases associated with domestic violence on the part of Li or her partner. If Li is assessed safe, it validates Li"s experiences and reassures her that help is available. If not, the machine calls 911. The Life Guidance Machine's knowledge base comprises information on local domestic violence support services and emergency response protocols. It provides Li with contact information for local domestic violence hotlines, shelters, and legal aid services even to the point of dialing the service if so permitted. The machine also offers guidance on creating a safety plan and preparing to leave the abusive situation. The LGM with Li's permission, listens and records her partner's verbal statements or threats, preserving a legal record for law enforcement. With Li's consent, the Life Guidance Machine contacts emergency services, providing them with Li"s location and details of the situation. It stays on the line with Li, offering support and guidance until help arrives. The machine also follows up with Li in the days and weeks after the intervention, with the permission or integration of a care plan API providing resources for long-term support and recovery.

[0157] Crisis Management Example 2: Suicide Prevention

[0158] Sean, a 45-year-old man, is experiencing severe depression and suicidal thoughts. The Life Guidance Machine detects signs of suicidal ideation during a conversation and takes immediate action to ensure Sean's safety. Using NLP and machine learning, the Life Guidance Machine analyzes Sean's language patterns and identifies phrases and sentiment associated with suicidal thoughts. It expresses concern for Sean's well-being and assesses the immediacy of the risk. The machine's knowledge base comprises information on suicide prevention strategies and crisis intervention resources. It provides Sean with contact information for suicide prevention hotlines and local mental health services. The Life Guidance Machine also guides Sean through crisis management techniques, such as creating a safety plan and identifying personal support systems. If the risk is deemed high, the Life Guidance Machine contacts emergency serviceswith Sean's consent, providing them with his location and relevant information. It stays on the line with Sean, offering support and engaging him in conversation until help arrives. The machine follows up with Sean to ensure he receives ongoing mental health support and treatment.[01591 Crisis Management Example 3: Medical Emergency

[0160] Margaret, a 70-year-old woman living alone, experiences severe chest pain and difficulty breathing during a conversation or in sensor contact with the Life Guidance Machine. In response to worn sensors or voice or sound responses, the machine recognizes the symptoms of a potential heart attack and takes immediate action. Using NLP and machine learning, the Life Guidance Machine analyzes Margaret's description or sensor readings of her symptoms and identifies the need for urgent medical attention. It calmly instructs Margaret to sit down and relax while it contacts emergency services. The machine provides the emergency dispatcher with Margaret's location, age, and medical history, as well as details of her current symptoms. It stays on the line with Margaret, offering reassurance and monitoring her condition until the ambulance arrives. To the extent that Margaret has caregivers, it alerts them as well to her condition as well as to her needs if they are able to respond with an AED or similar home device. The Life Guidance Machine also contacts Margaret's designated emergency contacts, informing them of the situation and providing updates on her status. It remains in contact with Margaret's family and healthcare providers in the days following the incident, offering support and assistance with post-hospitalization care coordination.

[0161] Crisis Management Example 4: Natural Disaster Response

[0162] During a severe hurricane, the Life Guidance Machine detects that Enrico, a 55-year- old man, is in an area under mandatory evacuation orders. The machine initiates a conversation with Enrico to ensure his safety and provide guidance. Using NLP and generative Al, the Life Guidance Machine creates personalized evacuation instructions based on Enrico's location, mobility needs, and available transportation options and if activated shares his location with emergency responders or family member or others that Enrico designates. It provides Enrico with real-time updates on the hurricane's path, road closures, and open evacuation shelters. The machine's knowledge base comprises information on emergency preparedness, includingessential supplies and documents to bring during an evacuation. It guides Enrico through the process of securing his home and gathering necessary items. It also can advise Enrico if he lacks sufficient time to secure his home in the event of a fast-moving situation, like a tornado or other fast moving natural emergencies. If Enrico is unable to evacuate safely on his own, the Life Guidance Machine contacts emergency services to request assistance and provide its location as a beacon. It provides responders with Enrico's location, medical needs, and any other relevant information to facilitate a swift and appropriate response. The Life Guidance Machine continues to provide support and updates throughout the evacuation process, ensuring Enrico reaches a safe location. In the aftermath of the hurricane, the machine offers resources for disaster recovery assistance and helps Enrico navigate the process of returning home and accessing aid services provided by the local, state or Federal agencies.

[0163] Elder Care Examples of the Life Path Guidance:

[0164] Elder Care Example 1: Managing Prescriptions

[0165] Margaret, a 75-year-old woman, takes multiple medications for various health conditions. The Life Guidance Machine helps Margaret manage her prescriptions and ensures she takes them as directed. The machine uses NLP to understand Margaret's medication schedule and dosages by analyzing her prescription labels and doctor's notes. It then creates a personalized medication plan, complete with reminders and instructions. Using generative Al, the Life Guidance Machine creates easy-to-follow visual aids, such as color-coded charts and simple illustrations, to help Margaret understand her medication regimen. It also generates reminders in the form of text messages, voice alerts, or smart home device notifications to ensure Margaret takes her medications on time. The machine's knowledge base comprises information on potential drug interactions and side effects. It monitors Margaret's symptoms and responses to medications, using machine learning to identify any adverse reactions or ineffective treatments. If concerns arise, the machine alerts Margaret's healthcare provider and suggests alternative medications or dosage adjustments.

[0166] Elder Care Example 2: Providing Memory Care Support

[0167] John, an 80-ycar-old man with early- stage dementia, lives alone and struggles with memory loss. The Life Guidance Machine helps John maintain his independence and quality oflife by providing memory care support. Using NLP, the machine engages John in conversations designed to stimulate his memory and cognitive function. It asks questions about his life experiences, hobbies, and interests, encouraging him to share stories and memories. It also if permitted connects with other family members to contribute sets of recollections for John to listen to and replay. To the extent voice synthesis allowed, the Life Guidance Machine could also synthesis its voice to resemble that of a departed loved one. The Life Guidance Machine's generative Al creates personalized memory aids, such as photo albums with captions, daily schedules with visual cues, and voice recordings of family members. These aids help John recall important information and stay connected to his loved ones. The machine's knowledge base comprises information on memory care best practices and activities that promote cognitive health. It suggests brain- stimulating games, exercises, and social activities tailored to John's interests and abilities. Machine learning allows the Life Guidance Machine to track John's engagement and progress, adapting its recommendations to optimize his cognitive function.

[0168] Example 3: Enhancing Elder Safety

[0169] Sarah, a 70-year-old woman with limited mobility, lives alone and is at risk of falls and other safety hazards. The Life Guidance Machine helps Sarah maintain a safe living environment and alerts caregivers in case of emergencies. The machine uses non-contact sensors to monitor Sarah's movements and detect potential safety risks, such as tripping hazards or unusually long periods of inactivity. NLP allows the machine to understand Sarah's verbal cues and respond to her requests for assistance. Generative Al enables the Life Guidance Machine to create personalized safety recommendations based on Sarah's specific needs and living space. It suggests home modifications for Sarah or her caretakers as she stumbles or catches herself in a similar location repeatedly, such as installing grab bars or improving lighting, to reduce the risk of accidents.

[0170] The machine's knowledge base comprises information on fall prevention, emergency response protocols, and local support services. If a fall or other emergency is detected, the Life Guidance Machine alerts Sarah's designated caregivers and emergency services, providing them with vital information to ensure a swift and appropriate response.

[0171] Example 4: Facilitating Elder Care Coordination

[0172] Robert, an 85-year-old man, receives care from multiple family members and healthcare professionals. The Life Guidance Machine helps coordinate Robert's care, ensuring everyone involved has access to the information they need. Using NLP, the machine analyzes collective notes from Robert's doctors, nurses, and therapists to create or otherwise execute a full care plan. It identifies relevant information, such as medication changes, therapy schedules, and dietary restrictions, and shares this information with Robert's care team. To the extent that Robert is living at home, this care plan can be extended to family members or caregivers in the community at large via secure APIs as identified by the care plan. Generative Al allows the Life Guidance Machine to create personalized care instructions and visual aids for each caregiver, tailored to their role and responsibilities. It also generates reports and updates on Robert's condition, keeping everyone informed of his progress and any changes in his care needs. The machine's knowledge base comprises information on elder care best practices, communication strategies, and caregiver support resources. It facilitates secure communication between caregivers, allowing them to share information, coordinate schedules, and address concerns collaboratively. Machine learning enables the Life Guidance Machine to identify patterns and potential issues in Robert's care, such as conflicting treatment plans, interactions between various prescriptions or caregiver burnout / abuse / inattention. It provides suggestions for optimizing care coordination and offers resources to support the well-being of both Robert and his caregivers.

[0173] Additional Examples of Community Service organizations response to the first 4 teen examples

[0174] Scenario 1: Educational Counseling Service response to LGM for Tina

[0175] Upon receiving a request from the Life Guidance Machine, the educational counseling service would review Tina's academic records, aptitude assessments, and the machine's analysis of her interests and potential. They would then provide detailed information about educational pathways to pursue a career in medicine, including recommended high school coursework, extracurricular activities, and volunteer opportunities.

[0176] The counselors would work with / interact with the Life Guidance Machine to develop a personalized study plan for Tina, offering resources such as tutoring, study groups, andmentorship programs. They would also assist in identifying and applying for relevant scholarships and grants. Throughout the process, they would collaborate with the Life Guidance Machine to monitor Tina's progress, address any challenges, and provide ongoing support to help her achieve her goals.

[0177] Scenario 2: Mental Health Support Service for Ethan

[0178] When contacted by the Life Guidance Machine regarding Ethan's social anxiety and potential autism, the mental health support service would first assess the information provided and potentially request additional evaluations or assessments. They would then develop a full treatment plan in collaboration with the machine, Ethan, and his family.

[0179] The team of therapists and specialists would provide evidence-based interventions, such as cognitive-behavioral therapy (CBT) and social skills training, to help Ethan manage his anxiety and improve his social interactions. They would work closely with the Life Guidance Machine to monitor Ethan's progress using both subjective and objective measures, adjusting the treatment plan as needed.

[0180] If an autism diagnosis is confirmed, the team would coordinate with the Life Guidance Machine to ensure that Ethan receives appropriate accommodations and support services in his educational and social settings. The goal would be to enhance Ethan's overall well-being and help him thrive in his daily life.

[0181] Scenario 3: Technology Education Service for Maya

[0182] Upon receiving Maya's information from the Life Guidance Machine, the technology education service would assess her skills, interests, and learning style to create a tailored curriculum. They would provide Maya with access to the online learning platform, which offers interactive coding courses, project-based assignments, and forums for collaboration with other students and mentors .

[0183] The service would work with the Life Guidance Machine to track Maya's progress, providing regular feedback and suggestions for improvement. They would also offer guidance on entering and participating in hackathons, coding competitions, and other extracurricularactivities to help Maya build her portfolio and network with professionals in the field to open other doors for Maya.

[0184] As Maya advances in her programming skills, the service would collaborate with the Life Guidance Machine to explore potential career paths and provide information on educational requirements, internships, and entry-level job opportunities in the tech industry.

[0185] Scenario 4: Family Support Service for Jayden

[0186] When the Life Guidance Machine reaches out regarding Jayden's family conflict situation, the family support service would initiate a full assessment to understand the dynamics and stressors within the household. They would work with the LGM to develop and load to the LGM a support plan that addresses both Jayden's individual needs and the overall family system.

[0187] The service would provide Jayden with individual counseling sessions, focusing on building coping skills, resilience, and self-esteem. They would also offer family therapy sessions to improve communication, conflict resolution, and emotional bonds within the family unit.

[0188] In collaboration with the Life Guidance Machine, the service would monitor Jayden's progress and well-being, offering crisis intervention and additional resources as needed, the service would also coordinate with other relevant agencies, such as child protective services or domestic violence support organizations, to ensure a complete and safe approach to supporting Jayden and his family.

[0189] The above description comprises references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of illustration, specific examples of the solutions in which the invention can be practiced. These variants of the solutions are also referred to herein as “examples.” Such examples can comprise elements in addition to those shown or described. However, the present inventors also contemplate examples in which only those elements shown or described are provided. Moreover, the present inventors also contemplate examples using any combination or permutation of those elements shown or described (or one or more aspects thereof), either with respect to a particular example(or one or more aspects thereof), or with respect to other examples (or one or more aspects thereof) shown or described herein.

[0190] In the event of inconsistent usages between this document and any documents so incorporated by reference, the usage in this document controls.

[0191] In this document, the terms “a” or “an” are used, as is common in patent documents, to comprise one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” comprises “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In this document, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “including” and “comprising” are open-ended, that is, a system, device, article, composition, formulation, or process that comprises elements in addition to those listed after such a term in a claim are still deemed to fall within the scope of that claim. Moreover, in the following claims, the terms “first,” “second,” and “third,” etc. are used merely as labels, and are not intended to impose numerical requirements on their objects.

[0192] Geometric terms, such as “parallel”, “perpendicular”, “round”, or “square”, are not intended to require absolute mathematical precision, unless the context indicates otherwise. Instead, such geometric terms allow for variations due to manufacturing or equivalent functions. For example, if an element is described as “round” or “generally round,” a component that is not precisely circular (e.g., one that is slightly oblong or is a many-sided polygon) is still encompassed by this description.

[0193] Method examples described herein can be machine or computer-implemented at least in part. Some examples can comprise a Computer-Readable Medium or machine-readable medium encoded with instructions operable to configure an electronic device to perform methods as described in the above examples. An implementation of such methods can comprise code, such as microcode, assembly language code, a higher-level language code, or the like. Such code can comprise computer readable instructions for performing various methods. The code may form portions of computer program products. Further, in an example, the code can be tangibly stored on one or more volatile, non-transitory, or non-volatile tangible computer- readable media, such as during execution or at other times. Examples of these tangible computer-readable media can comprise, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes,memory cards or sticks, random access memories (RAMs), read only memories (ROMs), and the like.

[0194] The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. Other variants of the solutions can be used, such as by one of ordinary skill in the art upon reviewing the above description. The Abstract is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. This should not be interpreted as intending that an unclaimed disclosed feature is essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed variant of the solution. Thus, the following claims are hereby incorporated into the Detailed Description as examples or variants of the solutions, with each claim standing on its own as a separate variant of the solution, and it is contemplated that such variant of the solutions can be combined with each other in various combinations or permutations. The scope of the invention should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims

CLAIMSWhat is claimed is:

1. A system for detecting and recommending life path advice to users, the system comprising: a sensor apparatus comprising a plurality of sensors configured to measure a set of physiological parameters to assess a user need; a computing entity in communication with the sensor apparatus, wherein the computing machine is configured to: receive, from the sensor apparatus, sensor data indicating the measured physiological parameters; analyze the sensor data to detect parameters for corrective advice within the permissions of the user; determine a life path guidance based on an analysis; and generate a user interface comprising display data indicating the recommended steps to accomplish the life path guidance for display via a display device.

2. The system of claim 1, wherein the plurality of sensors comprises at least two of these sensors: an electrocardiogram sensor, a galvanic skin response sensor, an accelerometer, a gyroscope, an oximeter sensor, or a skin temperature sensor.

3. The system of claim 1, wherein the computing machine is further configured to apply machine learning algorithms to the sensor data to identify patterns indicative of the user’s stress levels, physical activity levels, or sleep quality of the user.

4. The system of claim 1, wherein the sensor apparatus comprises at least a contactbased sensor worn by the user and at least a non-contact sensor positioned in the user's environment.

5. The system of claim 1, wherein the computing machine is further configured to statistically weigh a set of physiological parameters and provide analysis of the measured parameters correlated to a set of eight statistical values that are further comprising: a physical, an emotional, an intellectual, a social, a spiritual, an occupational, a financial, and an environmental wellness value.

6. The system of claim 1, wherein the user interface further comprises an interactive element allowing the user to provide feedback on the recommended steps and modify the life path guidance.

7. The system of claim 1, wherein the computing machine is further configured to correlate the measured sensor data with a set of user-reported information to validate the measured parameters for a set of corrective advice.

8. The system of claim 1, wherein the system further comprises a database storing historical sensor data and outcomes to improve future life path guidance recommendations through comparative analysis.

9. An apparatus for providing lifepath guidance, the apparatus comprising: at least one processor configured to: control a user interface to receive a user input indicating a goal related to the user's life, retrieve from a memory at least one personal information of the user, detect a user activities and experiences prior to the goal, and correlate the user input indicating the goal, the at least one personal information of the user, and the user activities and experiences prior to the goal; and a display for displaying a set of advice about the goal based on the correlation.

10. The apparatus of claim 9, wherein the at least one processor is further configured to apply at least a natural language processing algorithm to analyze the user input indicating the goal.

11. The apparatus of claim 9, wherein the personal information of the user further comprises demographic data, historical goal achievements, and user preferences stored in the memory.

12. The apparatus of claim 9, wherein the detecting user activities and experiences further comprises monitoring at least a set of sensor data from at least a wearable device and at least an environmental sensor.

13. The apparatus of claim 9, wherein the correlation comprises a set of weighting factors based on the user's life stage and cultural background.

14. The apparatus of claim 9, wherein the apparatus further comprises a communication interface for connecting to at least a third-party system to access a set of additional data resources related to the set of advice.

15. The apparatus of claim 9, wherein the at least one processor is further configured to update at least a correlation algorithm based on a set of user feedback regarding the effectiveness of previous advice.

16. A method for providing lifepath guidance, the method comprising: receiving, via a user interface, a user input indicating a goal related to the user's life; retrieving, from a memory, at least one personal information of the user; detecting a set of user activities and experiences prior to the goal; correlating the user input indicating the goal, the at least one personal information of the user, and the user activities and experiences prior to the goal; and displaying, on a display, advice about the goal based on the correlation.

17. The method of claim 16, further comprising validating the user input through an algorithmic correlation of the user’s sentiment analysis and the user’s intent.

18. The method of claim 16, wherein retrieving personal information comprises accessing a set of encrypted user profile data further comprising a set of previous goal histories and a set of achievement patterns.

19. The method of claim 16, wherein detecting a set of user activities further comprises collecting a set of data from a plurality of sensor modalities and applying data merger algorithms to improve accuracy.

20. The method of claim 16, wherein correlating the user’s goal further comprises applying weighted scoring algorithms that adjust based on the user's age, life circumstances, and cultural factors.

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