“BIOMETRIC AND CONVERSATIONAL INSIGHT SYSTEM FOR RELATIONSHIP COMPATIBILITY”

Abstract

The present invention relates to a Biometric-Enhanced Compatibility Assessment System that provides real-time evaluations of interpersonal compatibility using a wearable device. The system integrates conversational data captured through an audio module and biometric signals, including heart rate, skin conductance, and temperature. Using natural language processing and machine learning models, it analyzes interactions for sentiment, topics, and physiological responses to generate compatibility scores. Privacy-preserving techniques, such as federated learning and differential privacy, ensure secure data processing. Users can access compatibility insights and actionable recommendations via a mobile application or dashboard, allowing them to enhance their relationships. The system is applicable across multiple industries, including healthcare, corporate team building, education, and relationship services, providing objective and adaptive compatibility assessments that evolve based on ongoing interactions. This invention offers a comprehensive, data-driven approach to understanding and improving interpersonal relationships in various real-world contexts. FIG.1, CLAIMS 1-10

Specification

Description:FIELD OF INVENTION
The present invention relates to the field of artificial intelligence and wearable technology, specifically to systems and methods for assessing interpersonal compatibility through biometric and conversational data analysis.
BACKGROUND
[001] Existing relationship compatibility systems suffer from significant limitations, primarily due to their reliance on self-reported data. These systems often depend on user-provided information, which may be subjective, inaccurate, or biased, ultimately leading to unreliable compatibility assessments. The lack of objective data sources severely limits the accuracy of these assessments.
[002] Another major drawback of current systems is their static approach to compatibility evaluation. Most existing platforms assess compatibility at a single point in time, failing to account for the dynamic nature of human relationships. As individuals grow and change over time, compatibility assessments become outdated, making it difficult to provide ongoing and meaningful insights.
[003] Furthermore, current compatibility systems tend to rely on narrow data sources, such as personality tests or written questionnaires. These methods are insufficient for capturing the complex and multi-dimensional nature of human relationships. The inability to incorporate diverse data streams, including physiological or behavioral indicators, results in incomplete assessments of compatibility.
[004] Privacy concerns also present a challenge in many existing solutions. Compatibility platforms often struggle to balance data collection with user privacy, leading to concerns about the misuse or unauthorized sharing of personal information. The lack of effective privacy-preserving techniques discourages users from providing data necessary for meaningful compatibility assessments.
[005] In addition, existing systems face difficulty capturing real-world interactions in natural social contexts. Many solutions rely on artificial settings or online questionnaires, which do not accurately reflect genuine interpersonal behaviors. This lack of real-world data limits the effectiveness of compatibility assessments and reduces user confidence in the results.
[006] In view of the foregoing disadvantages inherent in the known systems in the prior art, the present invention provides a novel method and system.
OBJECTS OF THE INVENTION
[001] The principal objective of the present invention is to provide an accurate and dynamic system for evaluating relationship compatibility, utilizing real-time conversational data and supplementary biometric information to generate precise insights.
[002] Yet another objective is to enhance privacy in compatibility assessments by implementing advanced privacy-preserving techniques, thereby ensuring sensitive personal data is protected while delivering insightful analyses.
[003] Yet another objective is to provide a comprehensive analysis of human compatibility by capturing and interpreting the multifaceted aspects of human relationships, using artificial intelligence to analyze speech patterns, conversation content, and physiological responses.
[004] Yet another objective is to enable continuous learning and adaptation by developing a system that learns from ongoing interactions and adapts its assessments based on behavioral changes and individual preferences.
[005] Ultimately, the invention seeks to extend the application of biometric-enhanced compatibility analysis beyond the realm of romantic relationships, making it applicable to various contexts, such as team building, educational group formation, and therapeutic assessments.
SUMMARY OF THE INVENTION
[001] The present invention provides a Biometric-Enhanced Compatibility Assessment System that evaluates interpersonal compatibility through real-time conversational and biometric analysis. The system utilizes a wearable device, the NeoSapien Pendant (100), which acts as the primary data collection unit, capturing both conversational audio and biometric signals to create a comprehensive profile of social interactions.
[002] The NeoSapien Pendant (100) includes an Audio Capture Module (110) that records high-quality audio during social interactions, using Noise Cancellation (190) and Voice Isolation (200) to ensure clarity. Additionally, biometric data is collected through a set of sensors, including a Heart Rate Monitor (120), Skin Conductance Sensor (130), and Temperature Sensor (140). These sensors capture physiological signals in real-time, providing insights into the emotional and physiological states of individuals.
[003] An On-Device Preprocessing Unit (150) processes the collected data before it is transmitted for analysis, applying techniques like Feature Extraction (210) to identify key characteristics such as Pitch Analysis (220), Tone Analysis (230), and Speech Rate Analysis (240). This preprocessing reduces data transmission requirements and enhances user privacy.
[004] The data is then analyzed using a Natural Language Processing (NLP) Engine (290), which evaluates conversational content for Sentiment Analysis (300) and Topic Modeling (310). Machine learning models, including Supervised Learning (330), Unsupervised Learning (340), and Deep Learning Models (350), are employed to extract patterns and identify compatibility factors from the collected data. A Real-Time Model Adaptation Module (360) continuously updates these models, ensuring that the assessments evolve with changing behaviors.
[005] The system's Compatibility Assessment Module (370) integrates the analyzed conversational and biometric data to generate a comprehensive compatibility score. This module uses a Scoring Mechanism (380) to combine weighted factors and provide personalized insights through an Insight Generation Module (390). Users can interact with the system via a Mobile Application Interface (440) or a Dashboard (450), which displays compatibility metrics and allows customization.
[006] To protect user privacy, the system employs advanced privacy-preserving techniques, such as Federated Learning Implementation (420) and Differential Privacy Module (430). These techniques ensure that user data is processed without compromising sensitive information, maintaining a high level of privacy while benefiting from collective learning.
[007] Additionally, the system includes a Feedback Collection Module (400) that allows users to provide feedback on assessments, which is used by an Adaptive Learning Module (410) to refine the models over time. Data collected is securely stored using a Local Storage Unit (270) or optionally backed up to the cloud with encryption using the Encryption Module (260).
[008] The present invention is applicable across various domains, including relationship services, team building, education, and healthcare. By providing a dynamic, real-time, and privacy-preserving compatibility assessment, the invention aims to improve interpersonal relationships and enhance user experience across different contexts.
DETAILED DESCRIPTION
[001] The present invention relates to a Biometric-Enhanced Compatibility Assessment System, designed to provide an accurate and dynamic evaluation of interpersonal relationships using real-time data from conversational and biometric sources. Central to this invention is the wearable device known as the NeoSapien Pendant (100), which collects conversational data and biometric signals during natural social interactions. This system overcomes the limitations of traditional static approaches by enabling continuous assessment and learning based on real-world behavior.
[002] The NeoSapien Pendant (100) is designed as a discreet wearable device, allowing users to engage in natural interactions without the device drawing undue attention. The wearable pendant features an Audio Capture Module (110), responsible for recording conversations in high-quality audio. This module employs advanced techniques such as Noise Cancellation (190) and Voice Isolation (200), which work together to ensure that the recorded data is free of background noise and clearly isolates the speech signals, providing optimal conditions for conversational analysis.
[003] The system also includes biometric sensors integrated into the NeoSapien Pendant (100), which continuously collect physiological data during interactions. These sensors include a Heart Rate Monitor (120), a Skin Conductance Sensor (130), and a Temperature Sensor (140). The Heart Rate Monitor (120) measures the variability of the user's heartbeat, which can indicate stress, excitement, or relaxation levels. The Skin Conductance Sensor (130) measures galvanic skin response, which helps assess emotional arousal, while the Temperature Sensor (140) monitors fluctuations in body temperature, further contributing to the analysis of physiological responses.
[004] Data collected through the NeoSapien Pendant (100) is processed locally by an On-Device Preprocessing Unit (150). This preprocessing step includes the removal of noise and the extraction of relevant features from both conversational and biometric data streams. The Audio Processing (180) function, which consists of both Noise Cancellation (190) and Voice Isolation (200), enhances the quality of recorded audio to ensure that the subsequent analysis is accurate.
[005] The Feature Extraction (210) component of the On-Device Preprocessing Unit (150) identifies key attributes of the collected data, including pitch, tone, and speech rate. Specific analyses such as Pitch Analysis (220), Tone Analysis (230), and Speech Rate Analysis (240) are conducted to generate detailed conversational metrics. These metrics are vital for understanding how individuals communicate, their emotional states during conversations, and their level of engagement.
[006] To ensure data privacy, the system incorporates an Anonymization Module (250). This module removes or masks any personal identifiers before data processing, ensuring that sensitive personal information remains secure. The data is further protected using an Encryption Module (260), which employs AES-256 encryption. This encryption standard is utilized to safeguard data during storage in the Local Storage Unit (270) and during transmission, either to the cloud or to connected devices.
[007] For redundancy and secure data backup, a Cloud Backup (Optional) (280) mechanism is provided, allowing encrypted data to be stored on cloud servers. This ensures that in the case of device malfunction, the collected and processed data remains intact and recoverable. The use of Local Storage (270) ensures that critical data is retained without the need for continuous network connectivity.
[008] The conversational data and biometric information are further analyzed by a Natural Language Processing (NLP) Engine (290), which plays a critical role in the interpretation of user interactions. The NLP Engine (290) performs Sentiment Analysis (300) to determine the emotional tone of conversations and Topic Modeling (310) to identify the subjects being discussed. This contextual analysis provides a deeper understanding of the conversational dynamics between individuals.
[009] The machine learning component of the invention is crucial for adapting the system to each user's specific preferences and compatibility factors. A Model Training Module (320) is used for this purpose, employing different types of machine learning, including Supervised Learning (330), Unsupervised Learning (340), and Deep Learning Models (350), such as recurrent neural networks (RNNs). These models are initially trained using diverse datasets to recognize patterns indicative of compatibility across a range of contexts.
[010] The Real-Time Model Adaptation Module (360) ensures that the compatibility assessment evolves over time. This adaptation occurs as the system continuously updates the machine learning models with new data, allowing the system to learn and adapt based on ongoing interactions. This dynamic profiling is essential to maintain an accurate understanding of relationships that grow and evolve over time.
[011] The Compatibility Assessment Module (370) integrates both conversational and biometric data to provide a comprehensive compatibility score. The Scoring Mechanism (380) combines weighted factors derived from sentiment, conversation content, and biometric responses. The resulting score provides a holistic view of relationship compatibility, helping users understand the dynamics of their interactions better.
[012] The system generates personalized insights through the Insight Generation Module (390). These insights can include tips for improving communication, recommendations for relationship building, or alerts regarding potential issues. By combining conversational data with biometric responses, these insights are tailored to reflect the real-world experiences and emotional states of users.
[013] Users can interact with the system via a Mobile Application Interface (440) or a Dashboard (450). The Mobile Application Interface (440) provides users with a convenient way to view compatibility scores, receive insights, and adjust their preferences. The Dashboard (450), on the other hand, provides a more detailed view, including compatibility trends over time, enabling users to observe changes and developments in their interactions.
[014] Privacy is a significant focus of the invention. To maintain user trust and meet regulatory standards, the system employs Federated Learning Implementation (420) and a Differential Privacy Module (430). Federated Learning (420) ensures that data remains on the device during model training, and only model updates, rather than raw data, are shared for improvements. Differential Privacy Module (430) incorporates statistical noise into the data, ensuring that individual user information cannot be reconstructed from the model outputs, thereby safeguarding personal details.
[015] The system also includes a Feedback Collection Module (400), which allows users to provide input regarding the accuracy and relevance of the generated assessments. This feedback is crucial for refining the models and ensuring that the system remains user-centric. The Adaptive Learning Module (410) utilizes this feedback to update and adjust the models, making the system more personalized and improving its long-term reliability.
[016] Connectivity between the wearable device and other components is facilitated via a Bluetooth Low Energy Module (460). This module ensures that the NeoSapien Pendant (100) can seamlessly communicate with the mobile application without draining the battery, which is particularly important for ensuring a long battery life (18 hours of continuous use) of the pendant.
[017] The system is capable of adapting to various contexts, making it versatile for multiple industries, including dating and relationship services, team building in corporate settings, and therapeutic relationship assessments. The ability of the Compatibility Assessment Module (370) to adapt based on real-time data makes it suitable for any situation where understanding interpersonal compatibility is valuable.
[018] The NeoSapien Pendant (100) is powered by an ARM Cortex-M7 processor, which provides efficient on-device computation capabilities, minimizing latency and ensuring that data is processed in real-time. The pendant's internal memory has a storage capacity of 64 GB, which is sufficient for storing collected data before it is processed or transmitted. Connectivity is achieved through Bluetooth Low Energy (BLE) to maintain a stable connection with user devices while conserving power.
[019] The invention is designed with scalability and flexibility in mind. The modular architecture allows new sensors or additional machine learning models to be integrated as needed, enabling the system to grow with advancements in technology. The system's compatibility with mobile devices and the use of federated learning also ensure that it can scale to accommodate a large user base without compromising on privacy or performance.
[020] Ultimately, the Biometric-Enhanced Compatibility Assessment System provides a new approach to understanding interpersonal relationships by integrating conversational and biometric data analysis. The detailed, real-time insights and compatibility scores generated by the system are intended to help users build stronger and more informed connections, whether in romantic, professional, or educational settings. The emphasis on privacy-preserving techniques, coupled with the ability to adapt and learn from ongoing interactions, positions the invention as a significant advancement over existing compatibility assessment methods.
[021] The system also incorporates Cloud Backup (280), which provides an optional means of securely storing data off-device. This backup mechanism uses encrypted cloud servers to ensure redundancy and data security. By providing cloud storage as an optional feature, users can choose whether they wish to keep data purely local or have a backup for enhanced safety, thereby offering flexibility in terms of data management.
[022] Another critical aspect of the invention is the encryption standards used for both local and transmitted data. The Encryption Module (260) employs AES-256 encryption for data at rest in the Local Storage Unit (270) and ensures secure communication between the wearable device and other components, including the Mobile Application Interface (440) and cloud backup. The use of this strong encryption standard guarantees compliance with data security regulations and mitigates risks related to data breaches.
[023] In addition to the compatibility scoring system, the Dashboard (450) offers advanced data visualization features. The dashboard includes trend analyses, graphical representations of compatibility scores over time, and comparative insights, providing users with a deeper understanding of their relationship dynamics. The customization options available in the dashboard allow users to personalize data collection preferences, such as selecting specific interactions for detailed analysis or opting in/out of particular biometric sensors.
[024] To support a wide range of interactions, the system's NLP Engine (290) is designed to understand multiple languages and regional dialects. This multilingual capability ensures that users from diverse backgrounds can use the device effectively, thereby expanding its applicability. The Sentiment Analysis Module (300) and Topic Modeling Module (310) are both equipped with language-specific training to maintain accuracy irrespective of the user's language, making the system inclusive.
[025] The wearable NeoSapien Pendant (100) also features a design that ensures user comfort, allowing it to be worn for extended periods without causing discomfort. The ergonomic design supports optimal placement of the sensors-such as the Heart Rate Monitor (120), Skin Conductance Sensor (130), and Temperature Sensor (140)-to ensure accurate data collection. Additionally, the pendant is made from skin-friendly materials that minimize allergic reactions, promoting consistent and long-term use.
VARIOUS EMBODIMENTS OF THE INVENTION:
[001] In one embodiment of the invention, the NeoSapien Pendant (100) is integrated into a relationship compatibility application aimed at enhancing romantic partnerships. The pendant collects real-time conversational data through the Audio Capture Module (110) and biometric responses via the Heart Rate Monitor (120), Skin Conductance Sensor (130), and Temperature Sensor (140) during user interactions. The Natural Language Processing (NLP) Engine (290) processes the conversational content, and the Compatibility Assessment Module (370) generates a dynamic score, helping couples understand their compatibility and improve their communication.
[002] In another embodiment, the system is applied in corporate team-building scenarios. Here, employees wear the NeoSapien Pendant (100) during collaborative activities to gather data on conversational dynamics and physiological responses. The Compatibility Assessment Module (370) identifies ideal teams by matching individuals based on communication styles, stress responses, and engagement levels. The insights generated by the Insight Generation Module (390) help management in optimizing team compositions for better productivity.
[003] Another embodiment utilizes the system for therapeutic relationship assessments. The pendant (100) is worn by both the therapist and the patient during therapy sessions. The Audio Capture Module (110) records conversation, while biometric sensors track physiological indicators. The system generates compatibility insights using the NLP Engine (290) and Biometric Data Capture (170) to understand emotional responses, thereby allowing therapists to tailor their interventions and track patient progress more effectively over time.
[004] In yet another embodiment, the invention is employed in educational settings to facilitate group formation and enhance collaborative learning. Students are assigned NeoSapien Pendants (100) during group projects. The Compatibility Assessment Module (370) evaluates compatibility based on real-time conversations and biometric data. The Mobile Application Interface (440) provides insights to educators on how to best group students to promote productive collaboration and minimize conflicts, ultimately improving the learning experience.
[005] Another embodiment involves the use of the system in professional networking events. During these events, attendees wear the pendant (100) while interacting with others. The Audio Capture Module (110) and Biometric Sensors, including the Skin Conductance Sensor (130), gather real-time data that is analyzed to identify potential synergies between individuals. The Compatibility Score generated by the Compatibility Assessment Module (370) is used to match individuals, helping to create meaningful professional connections based on real interactions rather than superficial introductions.
[006] In a healthcare setting, the system can be used to improve patient-caregiver compatibility. The pendant (100) is used to monitor patient interactions with multiple caregivers. Using biometric indicators such as heart rate variability and temperature changes, along with conversational data processed by the NLP Engine (290), the system provides insights into the comfort level of the patient with each caregiver. This ensures that caregivers best suited to the patient's needs are prioritized.
[007] Another embodiment integrates the system into gaming and virtual reality environments. Users wear the NeoSapien Pendant (100) while interacting with others in virtual multiplayer games. The collected biometric data, combined with conversational analysis, is used to match players based on compatibility in terms of communication styles, engagement levels, and emotional responses. This embodiment enhances player experiences by ensuring teams are composed of individuals who work well together.
[008] In an embodiment aimed at the entertainment industry, the system is used for casting decisions and optimizing on-screen pairings. Actors wear the pendant (100) during rehearsals to analyze their interactions. The system's Compatibility Assessment Module (370) generates scores that indicate the degree of on-screen chemistry, helping directors select the best combinations for authentic audience engagement.
[009] In another embodiment, the system is used for negotiation and mediation processes. During negotiations, participants wear the NeoSapien Pendant (100) to capture both conversational dynamics and physiological responses. The insights generated by the Compatibility Assessment Module (370) help mediators understand the emotional states of participants, ensuring a balanced and effective negotiation process.
[010] The system can also be adapted for use in event planning and hospitality services. In this embodiment, guests at events wear the pendant (100), allowing the system to analyze social interactions and assess compatibility. The Dashboard (450) is used by event planners to optimize seating arrangements and identify potential matches among attendees, leading to more enjoyable and interactive events.
[011] In an embodiment aimed at market research, participants in focus groups wear the pendant (100) to capture their emotional and conversational responses during discussions. The collected data is analyzed to understand participants' reactions to different topics or products, providing market researchers with deeper insights into consumer preferences and behaviors.
[012] Another embodiment involves the use of the system for forming research and development teams in the corporate environment. The NeoSapien Pendant (100) is used to gather data during brainstorming and collaborative sessions. The Compatibility Assessment Module (370) and Real-Time Model Adaptation Module (360) are employed to ensure that teams are formed with members whose communication styles and physiological responses complement each other, thus fostering innovation and collaboration.
[013] In the realm of customer service, the system can be used to match customer service representatives with customers. During customer interactions, representatives wear the pendant (100) to track real-time conversational and biometric data. The Compatibility Assessment Module (370) provides insights into which representatives are best suited to handle different customer profiles, thereby enhancing customer satisfaction.
[014] Another embodiment uses the system for government and public services, such as assigning teams for community projects. During team assignment phases, participants wear the NeoSapien Pendant (100) to assess compatibility based on communication patterns and biometric signals, ensuring that public projects are executed by teams that work well together and have a positive group dynamic.
[015] In a sports and coaching context, the invention is used to match athletes with coaches. During training sessions, the NeoSapien Pendant (100) captures biometric and conversational data. The Compatibility Assessment Module (370) analyzes the data to ensure that athletes are paired with coaches whose training styles align with the athletes' needs, improving performance outcomes and building strong athlete-coach relationships.
[016] The system can also be adapted for use in community-based projects undertaken by non-profits. Volunteers are equipped with the NeoSapien Pendant (100) while they engage in group activities. The data captured is analyzed to form groups with optimal compatibility, ensuring the effectiveness of the projects and promoting volunteer satisfaction.
[017] In a modified embodiment, the NeoSapien Pendant (100) is replaced by a smartphone application that records conversational data using the phone's microphone. Although the accuracy of biometric readings might be limited compared to dedicated sensors, the application still allows for basic compatibility assessments through audio analysis. This embodiment makes the system more accessible to users who may not have access to the wearable device.
[018] In an alternative embodiment, the invention integrates environmental sensors that are installed in a controlled setting, such as an office or classroom. These sensors capture conversational audio and biometric signals from multiple users in the room. The Compatibility Assessment Module (370) uses the collected data to assess group dynamics, which is particularly useful for team building in a structured environment.
[019] In another modified embodiment, the system focuses solely on biometric data captured via wearable devices such as fitness bands. This approach is useful for individuals who may not wish to capture conversational data but still want to understand their physiological compatibility during interactions. The collected data, processed through the Real-Time Model Adaptation Module (360), provides a basic but insightful understanding of emotional compatibility.
[020] In yet another embodiment, the system integrates simulated interactions using AI agents. Users engage with an AI agent that simulates social interactions, and the collected data is analyzed to determine compatibility traits. This controlled setting helps users practice communication and understand their social behavior without the complexities of real-world interactions, making it ideal for individuals looking to enhance their interpersonal skills in a risk-free environment.
These various embodiments demonstrate the flexibility and broad applicability of the invention across different domains, highlighting its ability to enhance compatibility assessments and interpersonal relationships in a wide range of contexts.
STEPS INVOLVED IN THE SYSTEM
[001] Data Collection: The first step involves data collection using the NeoSapien Pendant (100). The wearable device captures conversational audio through the Audio Capture Module (110) while simultaneously collecting biometric data through sensors such as the Heart Rate Monitor (120), Skin Conductance Sensor (130), and Temperature Sensor (140). Conversational Data Capture (160) and Biometric Data Capture (170) take place in real-time during natural social interactions, ensuring a seamless experience for the user.
[002] Audio Processing: After data collection, the Audio Processing (180) step is performed by the On-Device Preprocessing Unit (150). Noise Cancellation (190) and Voice Isolation (200) are applied to remove ambient noise and isolate speech, ensuring high-quality audio data for further analysis. This enhances the accuracy of the insights derived from conversations.
[003] Feature Extraction: In the third step, the On-Device Preprocessing Unit (150) performs Feature Extraction (210). Specific features such as Pitch Analysis (220), Tone Analysis (230), and Speech Rate Analysis (240) are conducted to generate metrics that are essential for understanding individual communication styles and emotional cues.
[004] Data Anonymization and Encryption: Once the data is processed and features are extracted, the Anonymization Module (250) ensures that all personal identifiers are removed or masked to protect user privacy. Following this, the Encryption Module (260) applies AES-256 encryption to secure the data during both local storage and transmission, safeguarding the user's information.
[005] Data Storage: The processed and encrypted data is then stored locally on the device using the Local Storage Unit (270). If users opt for additional backup, a Cloud Backup (Optional) (280) is also available, which provides redundancy and ensures that data is recoverable in case of device malfunction.
[006] Natural Language Processing (NLP) Analysis: The conversational audio data is analyzed using the Natural Language Processing (NLP) Engine (290). Sentiment Analysis (300) is applied to determine the emotional tone of the conversation, while Topic Modeling (310) identifies the subjects discussed. This analysis helps provide context to the interaction and deepens the understanding of user communication.
[007] Biometric Analysis: In parallel with NLP analysis, the biometric data collected is analyzed to assess the physiological state of the users during interactions. Biometric Analysis provides key insights such as changes in heart rate, skin conductance, and temperature, correlating emotional and physiological responses to conversational dynamics.
[008] Machine Learning Model Training: The processed data is fed into a Model Training Module (320), which utilizes Supervised Learning (330), Unsupervised Learning (340), and Deep Learning Models (350). These models are trained to identify compatibility patterns from the collected data. This training phase ensures that the system is able to recognize important cues for compatibility assessments.
[009] Real-Time Model Adaptation: As new data is collected, the Real-Time Model Adaptation Module (360) updates the machine learning models with the latest information. This ensures that the system learns from ongoing interactions and adapts the compatibility assessment models to reflect any changes in user behavior, making the assessments more accurate and dynamic.
[010] Compatibility Assessment: Once the data is analyzed and the models are updated, the Compatibility Assessment Module (370) integrates both conversational and biometric data to generate a comprehensive compatibility score. The Scoring Mechanism (380) combines various weighted factors, providing an overall score that indicates how compatible the individuals are during their interactions.
[011] Insight Generation: The insights derived from the compatibility assessment are generated by the Insight Generation Module (390). These insights may include suggestions for improving communication, alerts about emotional changes during conversations, and tips for building stronger connections. The insights are tailored to each user, providing valuable feedback.
[012] User Feedback Collection: In the next step, the system collects user feedback through the Feedback Collection Module (400). Users are encouraged to confirm or adjust the system's assessments to ensure that the insights accurately reflect their experiences. This feedback is crucial for refining the system's accuracy and adapting it to specific user needs.
[013] Adaptive Learning: The Adaptive Learning Module (410) uses the feedback collected to adjust the models, allowing the system to continuously improve over time. By incorporating user input, the system remains user-centric and delivers highly personalized compatibility assessments.
[014] Federated Learning Implementation: In order to preserve privacy, the Federated Learning Implementation (420) is applied during model training. Instead of sharing raw user data, only the model updates are shared, allowing the system to learn from the collective data without compromising individual privacy. This distributed learning approach ensures that the system benefits from the broader dataset while keeping user data safe.
[015] Differential Privacy: To further enhance privacy, the Differential Privacy Module (430) adds statistical noise to the model updates, preventing any individual user's data from being reconstructed. This ensures that users can trust the system with sensitive personal information while benefiting from meaningful compatibility insights.
[016] Data Visualization: The insights and compatibility scores generated by the system are visualized through a Mobile Application Interface (440) and a Dashboard (450). The Mobile Application Interface (440) provides easy access to compatibility insights, while the Dashboard (450) offers advanced visualizations, including graphs of compatibility trends and comparative analyses, helping users understand their relationship dynamics over time.
[017] Customization Options: Users are given control over the data collection and notification settings through customization options available in the Mobile Application Interface (440). They can choose which sensors to activate, when to collect data, and how often to receive updates, providing them with a personalized experience that meets their comfort levels.
[018] Bluetooth Connectivity: Communication between the NeoSapien Pendant (100) and the mobile device is facilitated via Bluetooth Low Energy (BLE) Module (460). This ensures seamless data transfer with minimal battery consumption, enabling real-time syncing between the wearable device and the mobile application.
[019] Battery Management: The system ensures efficient power usage to support long-term operation. The NeoSapien Pendant (100) is powered by an ARM Cortex-M7 processor optimized for low-power consumption, allowing for 18 hours of continuous use. The Bluetooth Low Energy (BLE) module ensures minimal power drain during data transfer, and the internal memory capacity (64 GB) is used to store data until it is securely transmitted.
[020] Cloud Backup (Optional): As an optional step, the Cloud Backup (280) feature provides redundancy by storing encrypted data on cloud servers. This ensures that data remains available even in the event of device malfunction, providing users with peace of mind regarding the safety and durability of their information.
TECHNICAL AND ECONOMIC ADVANCEMENTS OVER PRIOR ART:
Technical Advancements:
[001] Real-Time Analysis of Conversational and Biometric Data: One of the significant technical advancements over the prior art is the system's ability to perform real-time analysis of both conversational and biometric data. Unlike existing systems that rely on static or self-reported information, the present invention utilizes the NeoSapien Pendant (100) to continuously gather high-quality data from real-world interactions. This provides a dynamic and up-to-date understanding of relationship compatibility, making the assessment more reflective of the user's current state and interactions.
[002] Multimodal Data Integration: The invention integrates multiple data streams, including conversational data via the Audio Capture Module (110) and physiological responses through sensors such as the Heart Rate Monitor (120), Skin Conductance Sensor (130), and Temperature Sensor (140). This multimodal approach is an advancement over traditional systems that rely solely on subjective questionnaires, enabling a holistic evaluation of compatibility that encompasses both emotional and physiological dimensions.
[003] On-Device Preprocessing and Data Privacy: The system employs an On-Device Preprocessing Unit (150) to process data locally before transmission. By performing initial processing such as Feature Extraction (210), including Pitch Analysis (220), Tone Analysis (230), and Speech Rate Analysis (240), directly on the device, the system significantly reduces the need for raw data transmission. This preserves user privacy, minimizes data latency, and enhances the overall efficiency of data processing.
[004] Advanced Machine Learning Techniques: The use of advanced machine learning techniques, including Supervised Learning (330), Unsupervised Learning (340), and Deep Learning Models (350), enables the system to learn from user data and adapt over time. Unlike prior systems that employ fixed, one-size-fits-all models, this invention employs the Real-Time Model Adaptation Module (360) to continuously improve compatibility assessments, ensuring that the system becomes increasingly personalized and accurate.
[005] Privacy-Preserving Model Training: A key differentiator of this invention is its use of privacy-preserving methods such as Federated Learning Implementation (420) and Differential Privacy Module (430). Federated learning allows for decentralized model training, ensuring that user data never leaves the device, while differential privacy introduces statistical noise to safeguard individual user information. These techniques provide significant improvements over prior systems, which often lack sufficient privacy measures, making this invention suitable for sensitive applications.
[006] Continuous Adaptive Profiling: Traditional compatibility assessments provide static insights that do not reflect the evolving nature of relationships. In contrast, the present invention employs a dynamic Compatibility Assessment Module (370) that updates profiles based on ongoing interactions and behaviors. This continuous adaptive profiling ensures that compatibility scores remain accurate as relationships change, an important advancement over outdated, fixed-profile approaches.
[007] Insightful User Interaction through UI/UX: The Mobile Application Interface (440) and Dashboard (450) enable users to visualize compatibility metrics and gain actionable insights in an intuitive manner. Unlike prior systems, which provide limited feedback, the present invention offers a detailed breakdown of compatibility scores, trends, and personalized recommendations through the Insight Generation Module (390), making the information more valuable and easier for users to act on.
[008] Multilingual Natural Language Processing (NLP): The NLP Engine (290) is capable of handling multiple languages and regional dialects, enabling users from diverse linguistic backgrounds to use the system effectively. Prior systems often struggle to understand diverse languages, whereas this system's multilingual capability ensures inclusivity, expanding its applicability across a broader user base.
[009] Bluetooth Low Energy Connectivity: The invention uses a Bluetooth Low Energy Module (460) for connectivity between the wearable device and the mobile application, ensuring minimal power consumption while maintaining seamless data transmission. This advancement supports longer device usage and efficient power management, which is a major technical improvement over earlier systems that often suffered from battery drainage issues.
Economic Advancements:
[010] Enhanced Match Quality and User Retention: The system's ability to provide accurate, real-time compatibility insights leads to enhanced match quality, which increases user satisfaction and retention rates. Unlike prior solutions that offer generic assessments, the personalized insights generated by this system encourage users to remain engaged, thereby reducing customer churn and enhancing economic value for service providers.
[011] Reduced Marketing and User Acquisition Costs: By providing a highly personalized and adaptive compatibility assessment, users are more likely to have positive experiences and recommend the service to others. This word-of-mouth effect reduces the marketing and user acquisition costs, a significant economic advantage over traditional systems that require extensive promotional activities to attract new users.
[012] Broader Market Applications and Revenue Streams: Unlike traditional compatibility systems that are limited to dating platforms, this invention is applicable across multiple domains, including team building, education, healthcare, and corporate training. This diversification opens up new revenue streams beyond the dating industry, thereby increasing the market potential and economic viability of the system.
[013] Premium Service Opportunities: The invention allows for the creation of premium features, such as advanced compatibility insights and in-depth relationship analytics. These features can be offered to users as part of a subscription service, providing opportunities for additional monetization and increasing the profitability of platforms utilizing the invention.
[014] Lower Customer Support Costs through Improved User Understanding: By providing clear insights into user compatibility and relationship dynamics, the system reduces the likelihood of misunderstandings and mismatched expectations. This results in fewer customer support inquiries and lower operational costs compared to prior art, where users often face issues due to inaccurate or incomplete compatibility assessments.
[015] Optimized Development Costs through Data-Driven Improvements: The data collected from users through the Feedback Collection Module (400) and Adaptive Learning Module (410) provides valuable information for product improvements. This data-driven approach ensures that development efforts are targeted and efficient, resulting in optimized development costs over time. Unlike static systems that require costly overhauls to introduce improvements, the adaptive nature of this invention allows for incremental upgrades, thereby reducing overall development expenses.
[016] Increased Adoption Due to Strong Privacy Measures: Privacy concerns are a major barrier to the adoption of many compatibility systems. By incorporating advanced privacy-preserving techniques, this invention builds user trust, leading to increased adoption rates and broader market penetration. This stands in contrast to prior art systems that lack sufficient privacy controls, thereby facing limited market adoption due to user hesitancy.
[017] Reduction in Hardware Requirements through On-Device Processing: The On-Device Preprocessing Unit (150) reduces the need for centralized processing, which, in turn, reduces server and hardware costs. By performing much of the data processing on the wearable device itself, this system minimizes reliance on cloud infrastructure, thereby reducing costs associated with data transmission, storage, and centralized processing.
These technical and economic advancements make the present invention a significant improvement over the prior art, addressing the major limitations of existing systems and offering a scalable, privacy-preserving, and versatile solution for compatibility assessment across multiple applications. Let me know if you'd like further elaboration on any specific point or additional modifications.


ENABLEMENT OF THE INVENTION
[001] The present invention is enabled through the integration of hardware components, machine learning algorithms, and a sophisticated data processing system that together provide a dynamic compatibility assessment based on real-time conversational and biometric data. The invention primarily involves the NeoSapien Pendant (100), a wearable device that continuously collects and processes various data streams to evaluate interpersonal compatibility.
[002] The NeoSapien Pendant (100) is designed as a user-friendly wearable device that can be comfortably worn during daily activities, ensuring natural data collection without causing interference or discomfort. The pendant contains an Audio Capture Module (110) that captures high-quality conversational data, and biometric sensors, including a Heart Rate Monitor (120), Skin Conductance Sensor (130), and Temperature Sensor (140), which collect physiological responses. These components enable the system to continuously gather relevant data during social interactions.
[003] To maintain the integrity and quality of the collected audio, the Audio Processing (180) step, including Noise Cancellation (190) and Voice Isolation (200), is performed directly on the wearable device. These features ensure that conversational audio is clear and suitable for analysis by the Natural Language Processing (NLP) Engine (290), which plays a critical role in extracting meaningful insights from user interactions.
[004] The wearable device also includes an On-Device Preprocessing Unit (150), which handles initial data processing tasks such as Feature Extraction (210). This step identifies important conversational features like pitch, tone, and speech rate, providing a detailed understanding of the user's communication style. The ability to preprocess data on the device helps reduce latency and maintain user privacy by minimizing the need for data transmission.
[005] Data collected by the wearable device is protected using the Anonymization Module (250) and Encryption Module (260). The Anonymization Module (250) removes personal identifiers, while the Encryption Module (260) uses AES-256 encryption to secure the data before it is stored locally in the Local Storage Unit (270) or backed up to cloud servers via the Cloud Backup (Optional) (280). This combination of anonymization and encryption ensures that user data is secure at every stage of the process.
[006] The Natural Language Processing (NLP) Engine (290) analyzes conversational data for emotional tone and context, using Sentiment Analysis (300) and Topic Modeling (310). These analyses enable the system to understand the nature of user interactions, providing a basis for assessing compatibility. The NLP Engine is multilingual, which broadens the invention's applicability to users from diverse linguistic backgrounds.
[007] Machine learning models form the core of the system's ability to assess compatibility. The Model Training Module (320) uses Supervised Learning (330), Unsupervised Learning (340), and Deep Learning Models (350) to learn from collected data and identify key compatibility factors. These models are continuously updated by the Real-Time Model Adaptation Module (360), ensuring that the system evolves with user interactions and adapts to changes in relationship dynamics.
[008] The Compatibility Assessment Module (370) integrates conversational and biometric data to generate compatibility scores, which are calculated using the Scoring Mechanism (380). By analyzing weighted factors from the different data streams, the module provides a holistic compatibility score that reflects the quality of interpersonal interactions. This compatibility score is key to enabling users to understand the dynamics of their relationships in real time.
[009] The Insight Generation Module (390) provides actionable insights to users based on compatibility scores. These insights are generated by interpreting the data analyzed by the system, offering users personalized recommendations, such as suggestions for improving communication or identifying areas of tension. This feature empowers users to make informed decisions about their relationships.
[010] The system provides an intuitive user experience through the Mobile Application Interface (440) and the Dashboard (450). The Mobile Application Interface (440) allows users to access compatibility scores, insights, and trends directly from their mobile devices. The Dashboard (450) provides a comprehensive overview of relationship metrics, including visual representations of compatibility trends over time, enabling users to understand the progress and evolution of their relationships.
[011] To ensure that the system remains user-centric, the Feedback Collection Module (400) enables users to provide input on the accuracy and relevance of the generated insights. This feedback is crucial for refining the machine learning models used by the Adaptive Learning Module (410), which adjusts the system based on user experiences to improve future assessments. This feedback mechanism makes the invention more adaptive and personalized, ultimately increasing its usefulness to users.
[012] The Federated Learning Implementation (420) ensures that user data remains private during model training. By keeping data on the device and only sharing model updates, the system is able to benefit from collective learning while maintaining user privacy. The Differential Privacy Module (430) adds statistical noise to model updates, further protecting individual user information and enabling the invention to meet stringent data privacy requirements.
[013] Connectivity between the NeoSapien Pendant (100) and the user's mobile device is facilitated by the Bluetooth Low Energy (BLE) Module (460). This connectivity ensures that data is synced seamlessly and in real time, without excessive battery consumption. This feature is critical for enabling continuous, real-time compatibility assessments, even during prolonged periods of use.
[014] The invention is designed for ease of deployment and use, allowing individuals to quickly integrate it into their daily routines. The wearable device is equipped with an ARM Cortex-M7 processor, which provides efficient on-device computation capabilities, allowing for complex data processing tasks without external computational resources. The pendant's battery is optimized to last up to 18 hours, supporting day-long usage without requiring frequent recharging.
[015] The system's modular architecture allows for scalability and adaptability. Additional sensors or upgraded machine learning models can be integrated as technology evolves, ensuring that the system remains state-of-the-art. This adaptability also allows the invention to be customized for specific use cases, such as compatibility assessments in dating, professional networking, or educational environments.
[016] The cloud storage option provided by the Cloud Backup (280) ensures that data is never lost, even if the wearable device is damaged or lost. This feature enables users to recover their data from secure cloud servers, thereby providing a layer of security and ensuring that valuable relationship insights are always available.
[017] The invention is also enabled by its versatility across multiple domains. For instance, in corporate settings, the system can be used to assess team compatibility, enabling human resources departments to build well-balanced and effective teams. In healthcare, the system can be used to assess patient-caregiver compatibility, enhancing patient comfort and treatment outcomes. This versatility is a significant enabler for broad adoption across various industries.
[018] The invention is enabled to deliver tangible benefits across both personal and professional contexts. By continuously capturing and processing conversational and biometric data, the system provides users with a deep understanding of their relationships. The insights generated are actionable and designed to facilitate positive changes, whether in personal relationships, professional collaborations, or therapeutic settings.
[019] The enablement of the invention also extends to individuals who may not be tech-savvy. The system is designed with an intuitive user interface that requires minimal user intervention. Data collection, processing, and analysis occur automatically, allowing users to focus on their relationships rather than managing complex technology. This ease of use makes the invention accessible to a wide audience, ensuring broad applicability.
ADVANTAGES OF THE INVENTION:
[001] Real-Time Compatibility Analysis: Unlike traditional systems that provide compatibility assessments based on self-reported or static data, the present invention offers real-time analysis by leveraging the NeoSapien Pendant (100). This ensures that the insights provided are reflective of users' current emotional states and ongoing interactions, offering an up-to-date understanding of relationship dynamics.
[002] Holistic Multimodal Data Integration: The invention combines conversational data with biometric inputs to create a comprehensive analysis of compatibility. By integrating data from the Audio Capture Module (110) and biometric sensors such as the Heart Rate Monitor (120), Skin Conductance Sensor (130), and Temperature Sensor (140), the system provides a holistic understanding of interpersonal compatibility that accounts for both behavioral and physiological responses.
[003] Privacy-Preserving Design: The use of Federated Learning Implementation (420) and the Differential Privacy Module (430) ensures that user data is protected throughout the system's operation. Data remains on the device during model training, and statistical noise is added to model updates, which helps preserve individual privacy while enabling collective learning. This privacy-preserving approach is a significant advantage over traditional systems that often lack robust data security measures.
[004] Adaptive and Personalized Assessments: The system's machine learning models, including Supervised Learning (330), Unsupervised Learning (340), and Deep Learning Models (350), enable adaptive profiling that evolves with user interactions. The Real-Time Model Adaptation Module (360) ensures that compatibility assessments are personalized and reflect the user's changing preferences, making the invention more effective and accurate over time.
[005] Enhanced User Experience through Intuitive Interfaces: The Mobile Application Interface (440) and Dashboard (450) provide users with easy access to compatibility scores, insights, and trends. The detailed visual representations offered by the dashboard enable users to easily interpret their compatibility assessments, enhancing engagement and making the information actionable. This intuitive user experience distinguishes the invention from existing systems that provide limited user interaction.
[006] Continuous Adaptive Profiling: Traditional compatibility systems often provide a one-time assessment, which becomes outdated as relationships change. The present invention, with its Compatibility Assessment Module (370) and Real-Time Model Adaptation Module (360), continuously updates compatibility profiles based on ongoing data collection. This adaptive profiling allows users to receive assessments that evolve with their relationship, offering more relevant and meaningful insights.
[007] Accurate Contextual Analysis: The Natural Language Processing (NLP) Engine (290), along with Sentiment Analysis (300) and Topic Modeling (310), enables the system to interpret conversational context accurately. Unlike systems that rely solely on static questionnaires or personality tests, this invention provides insights based on real-world interactions, resulting in more accurate and contextually relevant assessments.
[008] Scalable Design: The invention's modular architecture allows for easy scalability. Additional sensors can be integrated, and machine learning models can be expanded as new data becomes available. This ensures that the system remains adaptable to technological advancements, making it suitable for a wide range of applications-from personal compatibility to team-building and healthcare.
[009] Broad Applicability Across Domains: The invention's compatibility assessment system is applicable across various contexts, including romantic relationships, team building, professional networking, and therapeutic assessments. This broad applicability increases the usefulness of the invention beyond traditional compatibility systems, which are typically limited to specific use cases like dating.
[010] Premium Feature Opportunities: The invention provides opportunities to offer premium features, such as in-depth compatibility analysis, detailed relationship trends, and advanced insights through the Insight Generation Module (390). These features can be monetized, making the invention economically attractive for service providers.
[011] Multilingual Capability: The NLP Engine (290) is equipped to process multiple languages and regional dialects, making the system accessible to a diverse audience. Unlike prior art, which often has limited language support, this invention's multilingual capability allows for a broader user base, making it suitable for global deployment.
[012] Low Power Consumption: The invention uses a Bluetooth Low Energy (BLE) Module (460) to enable connectivity between the NeoSapien Pendant (100) and the mobile device, minimizing power consumption while maintaining real-time data syncing. The energy-efficient design allows for up to 18 hours of continuous use, making it ideal for day-long activities without frequent recharging.
[013] Effective Data Management and Security: The use of the Anonymization Module (250) and the Encryption Module (260) ensures that user data is securely processed and stored. Data is anonymized and encrypted before being stored in the Local Storage Unit (270) or transmitted for Cloud Backup (280), ensuring that user information remains protected at all stages of the process.
[014] Comprehensive Data Collection Without User Intervention: The wearable nature of the NeoSapien Pendant (100) allows for continuous, unobtrusive data collection. Unlike systems that require manual input, this invention captures conversational and biometric data without requiring user intervention, allowing individuals to focus on their interactions rather than managing technology.
[015] Objective Evaluation of Interpersonal Compatibility: The invention's integration of biometric data provides an objective means of evaluating interpersonal compatibility. While traditional systems depend heavily on self-reported questionnaires that can be biased or inaccurate, this invention's use of biometric signals, such as heart rate variability and skin conductance, ensures a more reliable and unbiased assessment.
[016] Enhanced Match Quality and Retention: The system's ability to provide real-time, adaptive, and accurate compatibility assessments results in enhanced match quality, particularly in contexts such as dating or team building. Users receive insights that help improve their relationships, leading to higher satisfaction and retention rates, which is economically beneficial for service providers.
[017] Support for Varied Use Cases: The system can be customized for different user needs. For instance, in a corporate environment, it can be used for team building by pairing employees based on compatibility factors. In healthcare, it can be used to assess patient-caregiver relationships to improve care outcomes. This versatility adds significant value, making the invention suitable for numerous applications beyond traditional compatibility systems.
[018] User-Controlled Customization Options: Users are provided with customization options in the Mobile Application Interface (440), allowing them to decide what data is collected and when. This empowers users with greater control over their privacy and the insights they receive, improving user satisfaction and trust in the system.
[019] Improved Decision-Making: By providing detailed, data-driven insights into the nature of relationships, the system helps users make informed decisions about their interactions. Whether it's improving communication in a romantic relationship, building a more cohesive team at work, or matching patients with suitable caregivers, the invention provides practical tools that lead to better decision-making.
[020] Cloud Backup and Data Redundancy: The optional Cloud Backup (280) feature ensures that data remains safe even if the wearable device is lost or damaged. This redundancy provides users with peace of mind that their valuable relationship insights are secure and can be easily recovered, adding a layer of reliability to the system.
ALTERNATIVE CONFIGURATIONS OR IMPLEMENTATIONS:
[001] Smartphone-Based Implementation: In an alternative configuration, the wearable NeoSapien Pendant (100) is replaced by a smartphone application that utilizes the smartphone's microphone and camera to capture conversational data and biometric indicators, such as facial expressions or heart rate (via camera-based photoplethysmography). This implementation reduces the need for additional hardware and utilizes existing smartphone sensors. However, it may lack the accuracy of dedicated sensors, particularly for physiological data collection.
[002] Integration with Existing Wearables: The invention can be implemented by integrating with existing wearable devices, such as fitness bands or smartwatches. Instead of using the NeoSapien Pendant (100), biometric data such as heart rate and skin conductance is gathered from the user's smartwatch, while conversational data is recorded via a paired smartphone application. This implementation leverages devices users may already own, making the system more accessible and cost-effective. However, the consistency and quality of the data may vary depending on the specifications of the wearable device.
[003] Room-Installed Environmental Sensors: Another configuration involves using room-installed environmental sensors instead of wearable devices. In this configuration, audio capture is performed by microphones installed in the room, and biometric data is gathered via non-contact sensors that measure parameters such as heart rate and body temperature. This implementation is particularly useful for controlled environments like meeting rooms or classrooms, where group dynamics need to be assessed. However, it lacks portability and requires a controlled setup.
[004] Post-Interaction Surveys: In this implementation, the system replaces real-time data capture with post-interaction surveys to gather information about users' experiences. Instead of using sensors and real-time conversational analysis, participants complete surveys after an interaction to provide insights about their emotional state, comfort level, and other factors. While this implementation is simpler and does not require wearable devices, it lacks the objectivity and real-time nature of the original system and is more prone to biases.
[005] Cloud-Based Processing: In another alternative, data processing, including feature extraction and natural language processing, is moved to cloud servers rather than being done on-device. The NeoSapien Pendant (100) or smartphone-based application sends captured audio and biometric data to a cloud server, where the NLP Engine (290) and machine learning models process the information. This implementation allows for more computational power and can accommodate complex deep learning models. However, it increases privacy risks and requires constant internet connectivity, which may be limiting in certain situations.
[006] Biometric-Only Analysis: In situations where conversational data is unavailable or undesirable, an alternative configuration focuses solely on biometric data collected through the NeoSapien Pendant (100) or an equivalent wearable device. By analyzing changes in heart rate, skin conductance, and temperature, the system can still assess emotional compatibility during interactions. This simplified version reduces data complexity but provides a less comprehensive assessment since it does not account for conversational dynamics.
[007] Simulated Interactions with AI Agents: In this configuration, users interact with AI agents instead of real people, and the data generated from these simulated interactions is analyzed for compatibility factors. The AI agent simulates social interactions and conversational dynamics, and the system captures both conversational and biometric data from these engagements. This implementation is useful for individuals looking to improve social skills or for safe, controlled interaction assessments. However, it may not fully reflect real-world dynamics due to the simulated nature of the interaction.
[008] Text-Based Communication Analysis: An alternative implementation focuses on analyzing text-based communication instead of voice conversations. This configuration captures messages, emails, or chat data between individuals and applies the NLP Engine (290) for sentiment and topic analysis. While this implementation does not require audio capture or wearables, it is limited to digital communication and lacks the emotional cues that can be inferred from vocal tone and biometric responses.
[009] Centralized Data Collection for Group Analysis: In an implementation intended for assessing group interactions, multiple NeoSapien Pendants (100) are used by participants in a group setting. The data collected from all pendants is transmitted to a central processing unit, where interactions are assessed collectively to understand group compatibility and dynamics. This implementation is suitable for corporate team-building exercises, group therapy sessions, or collaborative educational settings. It provides a comprehensive overview of group behavior but requires careful management of multiple data streams.
[010] Wearable Integrated into Clothing: Instead of a pendant, the wearable component can be integrated into clothing, such as smart shirts or wristbands. These wearables contain sensors for heart rate and temperature monitoring, as well as microphones for audio capture. This implementation allows users to wear the technology in a less obtrusive manner. However, the fixed placement of sensors within clothing may affect data accuracy due to variability in sensor positioning.
[011] Gesture and Body Language Integration: An alternative embodiment involves the integration of cameras to capture users' gestures and body language during conversations. This implementation combines visual data with conversational and biometric data to provide a more comprehensive understanding of interpersonal compatibility. By analyzing gestures and body posture, the system can assess non-verbal cues that are often indicative of emotional states and social dynamics. This implementation is particularly beneficial for analyzing group settings but may raise additional privacy concerns.
[012] Data Collection Triggered by Specific Events: In this configuration, data collection is triggered only during specific events, such as when the system detects heightened emotions based on biometric signals. For instance, the NeoSapien Pendant (100) may begin recording audio only when it detects a sudden increase in heart rate or skin conductance. This event-triggered approach reduces the amount of data collected, thereby enhancing privacy while still capturing key moments that are most relevant for compatibility assessment.
[013] Integration with Social Media and Online Communication: In a modified implementation, the system is configured to work with social media platforms and online communication tools. It captures conversations from these platforms and uses the NLP Engine (290) for sentiment and topic analysis. This approach allows for compatibility assessment based on digital interactions and is particularly useful for long-distance relationships or professional networking conducted online.
[014] Collaborative Use in Therapy Sessions: In a therapeutic setting, both the therapist and the patient can wear NeoSapien Pendants (100) during sessions. The system analyzes conversational and biometric data from both participants, providing insights into the emotional dynamics of the session. This implementation helps therapists better understand the patient's reactions and adapt their approach accordingly. It enhances the therapeutic process but requires careful handling of sensitive data to ensure privacy.
[015] Temporary Wearable Use for Situational Assessments: In this configuration, the wearable device is used only for specific interactions, such as during an important meeting or a key social event. Users wear the pendant (100) temporarily to gather data during these interactions, and the Compatibility Assessment Module (370) processes this data to provide situational insights. This implementation allows users to gain insights into specific interactions rather than continuous assessments, offering a less invasive use case.
[016] Use of Haptic Feedback for Real-Time Alerts: In this alternative implementation, the NeoSapien Pendant (100) provides real-time haptic feedback to alert users about potential emotional disconnects or rising stress levels during an interaction. The Feedback Collection Module (400) processes biometric data and, when certain thresholds are met, the pendant vibrates gently to indicate that a user might need to adjust their tone or behavior. This enables immediate corrective actions without waiting for post-interaction analysis.
[017] Data Storage and Analysis via Edge Computing: Instead of relying on centralized cloud servers, this implementation uses edge computing for data storage and analysis. The edge device processes data close to where it is generated, ensuring minimal latency and better data privacy. Edge computing is particularly useful in environments where internet connectivity is limited, ensuring that the system continues to function effectively without requiring cloud-based processing.
[018] Use of Audio Analysis Only: In situations where biometric data cannot be captured or users are uncomfortable with physiological monitoring, an audio-only version of the system can be used. The Audio Capture Module (110) and NLP Engine (290) analyze conversational data to provide insights into compatibility. This simplified version reduces the complexity of the data while still offering meaningful compatibility assessments based on communication style and sentiment.
These alternative configurations provide flexibility and cater to different user preferences and environments, making the system versatile for a wide range of applications.
DETAILED USE CASES OF THE INVENTION
[001] Romantic Relationship Compatibility Assessment: In this use case, the NeoSapien Pendant (100) is used by individuals in romantic relationships to assess compatibility based on real-time conversational and biometric data. During interactions, the pendant captures conversational data through the Audio Capture Module (110) and physiological responses through the Heart Rate Monitor (120), Skin Conductance Sensor (130), and Temperature Sensor (140). The data is processed using the NLP Engine (290) for sentiment analysis and Topic Modeling (310), while the Compatibility Assessment Module (370) calculates a compatibility score. The insights provided through the Mobile Application Interface (440) help couples understand their emotional and communicative dynamics, fostering better understanding and relationship growth.
[002] Corporate Team Building: In a corporate environment, the invention is used to facilitate team building by assessing employee compatibility during group activities. Employees wear the NeoSapien Pendant (100) during team exercises, which captures conversational and biometric data to evaluate each participant's engagement, stress, and communication style. The Real-Time Model Adaptation Module (360) adapts the system based on these interactions, and the Compatibility Assessment Module (370) identifies optimal teams for future projects. The Dashboard (450) provides managers with insights into how well team members collaborate, enabling them to form teams that are more effective and productive.
[003] Therapeutic Relationship Monitoring: In a therapeutic setting, the NeoSapien Pendant (100) is worn by both the therapist and the patient during sessions. The system captures real-time conversational data and biometric signals from both participants. The Sentiment Analysis (300) and biometric data are used to understand the emotional state of the patient and evaluate the effectiveness of the therapeutic interaction. The therapist can use these insights to adjust their approach, providing more tailored and effective therapy. The Feedback Collection Module (400) allows patients to provide input on how they felt during the session, which further refines the therapeutic process.
[004] Patient-Caregiver Compatibility Assessment: In healthcare settings, the invention is used to assess compatibility between patients and caregivers. The NeoSapien Pendant (100) is worn by both the caregiver and the patient during interactions, allowing the system to monitor conversational dynamics and physiological responses, such as changes in heart rate or skin conductance. The Compatibility Assessment Module (370) evaluates the comfort level and emotional alignment between the patient and caregiver. Insights provided through the Dashboard (450) help healthcare providers assign caregivers to patients more effectively, ensuring better patient care and improved treatment outcomes.
[005] Educational Grouping and Collaborative Learning: In educational environments, the invention is used to group students for collaborative learning activities. Each student wears the NeoSapien Pendant (100) during group projects, allowing the system to gather data on how well students communicate and engage with each other. The Compatibility Assessment Module (370) calculates compatibility scores to identify students who work well together, and the insights are used by educators to create effective groups. This use case enhances the learning experience by ensuring that group dynamics are conducive to productive collaboration.
[006] Professional Networking Events: During professional networking events, attendees wear the NeoSapien Pendant (100) to capture real-time conversational data and physiological responses while interacting with others. The system uses the NLP Engine (290) to evaluate the tone, sentiment, and topics of conversations, while the biometric sensors capture engagement levels. The Compatibility Assessment Module (370) generates scores that identify individuals who are likely to have meaningful professional synergies. The insights provided through the Mobile Application Interface (440) allow users to connect with others based on compatibility, making networking more efficient and impactful.
[007] Focus Group Market Research: The invention is also useful in market research environments, where participants in focus groups wear the NeoSapien Pendant (100) during discussions. The pendant captures participants' conversational contributions as well as physiological responses to different topics or product concepts. The Sentiment Analysis (300) and Topic Modeling (310) modules provide insights into participants' reactions, while the Compatibility Assessment Module (370) identifies areas of consensus or disagreement. These insights help market researchers understand consumer preferences at a deeper level, making the focus group feedback more actionable.
[008] Casting and On-Screen Pairing in Entertainment: In the entertainment industry, the system is used during auditions and rehearsals to assess on-screen compatibility between actors. Actors wear the NeoSapien Pendant (100) while interacting with each other, and the Compatibility Assessment Module (370) generates compatibility scores that reflect the chemistry between them. This enables directors to select the most compatible pairings for better on-screen performance. The insights provided through the Dashboard (450) offer data-driven recommendations for casting decisions, enhancing the overall audience experience.
[009] Athlete-Coach Pairing in Sports: In sports environments, the invention is used to optimize the pairing of athletes with coaches. The NeoSapien Pendant (100) is worn during training sessions to capture both conversational and biometric data. The Compatibility Assessment Module (370) assesses how well athletes respond to different coaching styles. The insights provided through the Dashboard (450) help identify the best athlete-coach pairings, improving training outcomes and fostering stronger coach-athlete relationships.
[010] Event Planning and Guest Compatibility: The invention can be used in event planning and hospitality services to optimize guest experiences. During social events, attendees wear the NeoSapien Pendant (100), which captures conversational and biometric data while guests interact with each other. The Compatibility Assessment Module (370) analyzes these interactions, and the Dashboard (450) provides event organizers with insights on potential matches among attendees. This data is used to optimize seating arrangements and create engaging opportunities for networking, enhancing the social experience.
[011] Negotiation and Mediation: During negotiations or mediation sessions, participants wear the NeoSapien Pendant (100) to capture both conversational and biometric data. The system analyzes the data to provide insights into each participant's emotional state and engagement levels. The Compatibility Assessment Module (370) helps mediators understand the dynamics at play and adjust their approach to ensure productive negotiations. The insights provided in real time help prevent emotional escalation and ensure balanced participation.
[012] Gaming and Virtual Reality Environments: In virtual multiplayer games, users wear the NeoSapien Pendant (100) while interacting with other players. The pendant captures biometric and conversational data, which the system analyzes to assess player compatibility. The Compatibility Assessment Module (370) identifies players whose communication styles and engagement levels are compatible, allowing game platforms to form balanced teams. This enhances player satisfaction and community building by creating teams that work well together.
[013] Team Formation in Non-Profit Projects: In community-based projects undertaken by non-profits, volunteers wear the NeoSapien Pendant (100) during collaborative activities. The Compatibility Assessment Module (370) analyzes conversational dynamics and biometric data to assess how well team members interact. Insights provided through the Dashboard (450) help organizers form teams that are more cohesive, ensuring the success of the project and promoting volunteer satisfaction.
[014] Customer Service Representative-Client Matching: In customer service environments, the invention is used to match customer service representatives with clients. During customer interactions, the representative wears the NeoSapien Pendant (100) to capture conversational and biometric data. The Compatibility Assessment Module (370) provides insights into which representatives are best suited to handle specific client profiles based on compatibility factors, such as tone and engagement. This improves customer satisfaction by ensuring a personalized approach to client service.
[015] Compatibility Assessment for Research and Development Teams: In R&D environments, the invention is used to form effective research teams. Researchers wear the NeoSapien Pendant (100) during brainstorming sessions and collaborative discussions. The Compatibility Assessment Module (370) evaluates the conversational and physiological dynamics of the team, and the insights provided help managers create well-balanced groups with complementary skills. This fosters an environment that supports innovation and effective collaboration.
[016] Human Resources Employee Onboarding: The invention can also be used by HR departments to assess the compatibility of new hires with existing teams during the onboarding process. New employees wear the NeoSapien Pendant (100) during their introductory period while interacting with team members. The Compatibility Assessment Module (370) identifies potential synergies and challenges in team dynamics, helping HR make informed decisions on team placement, ensuring that new hires are integrated smoothly and effectively.
[017] Virtual Meetings and Remote Work Collaboration: In remote work settings, the NeoSapien Pendant (100) can be used to assess compatibility during virtual meetings. Participants wear the pendant during video calls, and the system captures biometric and conversational data. The Compatibility Assessment Module (370) evaluates how well team members communicate and engage in a virtual environment, providing insights to managers about team dynamics in a remote work context.
[018] Pre-Marital Compatibility Counseling: The invention can be used in pre-marital counseling to help couples understand their compatibility before marriage. The NeoSapien Pendant (100) is worn during counseling sessions to capture both conversational and biometric data. The Compatibility Assessment Module (370) provides insights into each partner's communication style, stress responses, and emotional alignment, which counselors can use to help couples prepare for marriage.
[019] Collaborative Learning in Distance Education: In distance education, students wear the NeoSapien Pendant (100) during virtual group projects or discussions. The system captures biometric and conversational data and evaluates compatibility among students. Educators use these insights to form groups that are well-suited for collaborative tasks, ensuring that students can work effectively together despite being in a remote learning environment.
[020] Employee Feedback Analysis in Corporate Settings: During employee feedback sessions, both managers and employees wear the NeoSapien Pendant (100). The system captures conversational data and physiological responses, and the Compatibility Assessment Module (370) provides insights into the engagement and comfort levels of both parties. These insights help HR and management make feedback sessions more productive and supportive, enhancing the overall employee experience.
These detailed use cases demonstrate the versatility of the invention across various domains, including relationships, education, healthcare, corporate settings, and more. Each use case highlights the specific benefits of integrating real-time conversational and biometric data to provide valuable compatibility insights.
BRIEF DESCRIPTION OF THE DRAWING
Figure 1 illustrates the overall architecture of the biometric-enhanced compatibility assessment system, including its main components and their interactions.
Numbering Scheme for Elements of the Invention:
1. Wearable Device (NeoSapien Pendant) - (100)
2. Audio Capture Module - (110)
3. Heart Rate Monitor - (120)
4. Skin Conductance Sensor - (130)
5. Temperature Sensor - (140)
6. On-Device Preprocessing Unit - (150)
7. Conversational Data Capture - (160)
8. Biometric Data Capture - (170)
9. Audio Processing - (180)
10. Noise Cancellation - (190)
11. Voice Isolation - (200)
12. Feature Extraction - (210)
13. Pitch Analysis - (220)
14. Tone Analysis - (230)
15. Speech Rate Analysis - (240)
16. Anonymization Module - (250)
17. Encryption Module (AES-256) - (260)
18. Local Storage Unit - (270)
19. Cloud Backup (Optional) - (280)
20. Natural Language Processing (NLP) Engine - (290)
21. Sentiment Analysis Module - (300)
22. Topic Modeling Module - (310)
23. Model Training Module - (320)
24. Supervised Learning - (330)
25. Unsupervised Learning - (340)
26. Deep Learning Models (RNN) - (350)
27. Real-Time Model Adaptation Module - (360)
28. Compatibility Assessment Module - (370)
29. Scoring Mechanism - (380)
30. Insight Generation Module - (390)
31. Feedback Collection Module - (400)
32. Adaptive Learning Module - (410)
33. Federated Learning Implementation - (420)
34. Differential Privacy Module - (430)
35. Mobile Application Interface - (440)
36. Dashboard - (450)
37. Bluetooth Low Energy Module - (460)
DETAILED DESCRIPTION OF THE DRAWING:
Figure 1 illustrates the overall architecture of the biometric-enhanced compatibility assessment system, including its main components and their interactions.

[001] The drawing illustrates the various components of the biometric-enhanced compatibility assessment system as disclosed in the claims. The system includes a wearable device (100) that serves as the primary interface for users. The wearable device (100) is equipped with an audio capture module (110) for recording conversations during interactions, enabling the collection of conversational data through the conversational data capture module (160).
[002] The wearable device (100) further comprises a plurality of biometric sensors, including a heart rate monitor (120), a skin conductance sensor (130), and a body temperature sensor (140). These sensors work alongside the biometric data capture module (170) to monitor physiological parameters, providing real-time data on the user's physiological responses during interactions.
[003] The collected audio data is processed by an audio processing module (180) to filter and enhance the quality before it reaches the on-device preprocessing unit (150). The preprocessing unit (150) is responsible for extracting features, including pitch analysis (220), tone analysis (230), and speech rate analysis (240), which provide insights into user engagement and emotional state.
[004] The on-device preprocessing unit (150) also integrates a feature extraction module (210) that processes both conversational and biometric data to derive relevant metrics. An anonymization module (250) is used to ensure the privacy of the user's data before any further analysis.
[005] The processed data is then analyzed by the natural language processing (NLP) engine (290), which performs sentiment analysis (300) and topic modeling (310) on the conversational data to provide deeper insights into user interactions. The data is further used by the compatibility assessment module (370) to generate a compatibility score, which is displayed through the user interface, comprising a mobile application interface (440) and a dashboard (450).
[006] The data is stored securely using a local storage unit (270) and optionally backed up to a cloud storage server (280). To protect the privacy of the data, the system utilizes an encryption module (260) with AES-256 encryption. Furthermore, federated learning (420) and differential privacy (430) techniques are implemented to ensure secure data processing without compromising user privacy.
[007] To improve the system's effectiveness, a model training module (320) is incorporated, which includes supervised learning (330), unsupervised learning (340), and deep learning models (350). The real-time model adaptation module (360) dynamically updates these machine learning models based on ongoing user interactions and feedback.
[008] The compatibility assessment module (370) uses a scoring mechanism (380) to calculate a real-time compatibility score, which is shared with the user through the user interface. The feedback collection module (400) allows users to provide input regarding the accuracy of compatibility assessments, and this feedback is used by the adaptive learning module (410) to refine the models continuously.
[009] The wearable device (100) is also equipped with a noise cancellation unit (190) and a voice isolation unit (200) to enhance the quality of recorded conversational data, ensuring that the data used for analysis is clear and accurate. A Bluetooth Low Energy (BLE) module (460) is provided for seamless data transmission between the wearable device (100) and a connected mobile application.
[010] The compatibility assessment module (370) is further configured to generate a real-time compatibility score, providing users with immediate feedback on their relationship dynamics. The wearable device (100) also includes a haptic feedback mechanism that alerts users to changes in compatibility levels during interactions, enabling real-time awareness and adjustment to improve interpersonal communication.
[011] The insight generation module (390) processes the analyzed data to provide personalized recommendations, while the mobile application interface (440) and dashboard (450) allow users to easily access insights, track compatibility, and view historical interaction data.
, Claims:[Claim 1]
A biometric-enhanced compatibility assessment system, comprising:
(a) a wearable device (100) configured to collect conversational data and biometric signals during user interactions;
(b) an audio capture module (110) for recording conversations;
(c) a plurality of biometric sensors for monitoring physiological parameters, comprising:
(i) a heart rate monitor (120);
(ii) a skin conductance sensor (130); and
(iii) a body temperature sensor (140);
(d) an on-device preprocessing unit (150) for processing the collected data to extract features;
(e) a natural language processing (NLP) engine (290) for analyzing conversational content;
(f) a compatibility assessment module (370) for generating a compatibility score based on the analyzed conversational and biometric data;
(g) a user interface (440, 450) configured to present compatibility insights and recommendations.
[Claim 2]
The system of Claim 1, further comprising a privacy-preserving data handling module, wherein the system uses federated learning (420) and differential privacy (430) techniques to ensure secure data processing without compromising user privacy.
[Claim 3]
The system of Claim 1, wherein the compatibility assessment module (370) utilizes machine learning models that are periodically updated based on user feedback and real-time interaction data.
[Claim 4]
The system of Claim 1, further comprising a real-time model adaptation module (360) configured to adapt machine learning models dynamically to enhance compatibility profiling based on ongoing user interactions.
[Claim 5]
The system of Claim 1, wherein the NLP engine (290) is configured to perform sentiment analysis (300) and topic modeling (310) on the recorded conversational data, and an insight generation module (390) is configured to provide personalized recommendations to users based on compatibility scores and detected patterns in the conversational and biometric data.
[Claim 6]
The system of Claim 1, further comprising a noise cancellation unit (190) and a voice isolation unit (200) to enhance the quality of recorded conversational data.
[Claim 7]
The system of Claim 1, further comprising an encryption module (260) configured to secure the collected data before transmission or storage using AES-256 encryption.
[Claim 8]
The system of Claim 1, further comprising a Bluetooth Low Energy (BLE) module (460) for seamless data transmission to a connected mobile application.
[Claim 9]
The system of Claim 1, wherein the compatibility assessment module (370) is further configured to generate a real-time compatibility score, providing users with immediate feedback on relationship dynamics during interactions.
[Claim 10]
The system of Claim 1, wherein the wearable device (100) provides haptic feedback to alert users regarding changes in compatibility levels during interactions, thereby enabling real-time awareness and adjustment.

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