Belonging integration architecture system and method

Abstract

A belonging integration architecture system, the system comprising self belonging, interpersonal belonging, community belonging, universal belonging, and intersecting or overlapping areas of each of self belonging, interpersonal belonging, community belonging, and universal belonging, wherein each intersection comprises and integration of the intersecting or overlapping area. The system can be implemented by a computer.

Description

BELONGING INTEGRATION ARCHITECTURE SYSTEM AND METHODField of the Disclosure

[0001] The invention relates generally to the field of psychology and a belonging integration architecture system and method. More particularly, the invention relates to a system and method for identification of personality traits, gaps in understanding oneself, and psychological development. The system and method can be implemented using a computer system.Background of the Disclosure

[0002] Mental health and wellness issues are prevalent in the world. Lack of mental health and wellness in an individual can lead to an unfulfilling life which can lead to the individual harming others. Mentally unwell individuals often become bullies and intolerant of individuals that are different from themselves. Mentally unwell individuals often create hostile work and social environments.

[0003] Maslow’s hierarchy of needs is a well-known is a physiological theory comprising five levels of human needs depicted as a triangle or pyramid, with the lowest level needing to be fulfilled by an individual before the individual can move up the pyramid to satisfy the next level of needs. The levels of needs, starting with the bottom, are physiological, safety, love and belonging, esteem, and self-actualization. As an individual grows in life, Maslow teaches that the individual can move up in levels of his pyramid to be come fulfilled in life. If an individual fails to meet a lower-level need, the individual will be unable to progress upward. Also, Maslow teaches individuals may bounce back and forth between two levels for a while and progressing upward is not a one-directional path.

[0004] Level 3, or love and belonging, of Maslow’s hierarchy of needs is significant and can be employed to predict the behavioral outcomes and psychological make-up of an individual. The sense of belonging instrument (SOBI) has been used to analyze, quantify, and qualify an individual’s sense of belonging for Level 3, or love and belonging, of Maslow’s hierarchy ofneeds. A high sense of belonging has been associated with better health, longevity, psychological well-being, and disease recovery.

[0005] A high sense of belonging has been associated with better health, longevity, psychological well-being, and disease recovery. However, the drivers behind the sense of belonging have not been identified. With the lack of identifying the drivers behind the sense of belonging,

[0006] Ideally, it would be extremely useful to identify the drivers behind the sense of belonging. Accordingly, there is need for a solution to at least one of the aforementioned problems. For instance, there is an established need for identifying the drivers to build an architecture system to improve the mental health of individuals.Summary of the Disclosure

[0007] A system of one or more computers can be configured to perform particular operations or actions by virtue of comprising software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by virtue of comprising instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.

[0008] In one general aspect, a computer-implemented system may comprise a processor. The computer-implemented system may also comprise a memory storing executable instructions that, when executed by the processor, enable the system. The system may be adapted and / or programmed to receive data. The system may also be adapted or programmed to analyze the received data using machine learning algorithms to generate a report and transmit the report. The system may comprise at least one display. Additionally, it may comprise a user input interface layer (UIIL) configured to present a plurality of predefined statements to a user on the display. Each statement corresponds to a segment of belonging, where the segments of belonging comprise Self-Belonging, Interpersonal Belonging, Community Belonging, and Universal Belonging. The UIIL is also configured to receive input data from the user in response to thesepredefined statements. This input data includes demographic information, personal values, traits, and lived experiences. The system may also comprise a data storage module configured to store the received input data and the belonging segments associated with each user. Additionally, a data analysis module may be configured to analyze the input data to generate a belonging profile for the user, where the belonging profile identifies areas of belonging and gaps. A recommendation module may also be included to generate personalized recommendations based on the belonging profile to address the identified gaps. Other embodiments of this aspect comprise corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

[0009] Implementations may comprise one or more of the following features. The system may be configured or programmed to allow the user input interface to validate and preprocess the input data in real-time using dynamic feedback mechanisms. A data storage module may be present and programmed or adapted to comprise: a relational database for storing structured data such as predefined psychological attributes and user identifiers; and an SQL or SQL and / or NoSQL database configured to store unstructured and semi- structured data, comprising narratives and dynamic psychological profiles. The data analysis module may further comprise a feature extraction and transformation module configured to apply natural language processing (NLP) and dimensionality reduction to convert the input data into structured representations. The recommendation module may be further configured to employ collaborative filtering and content-based filtering techniques to generate personalized recommendations for the user. The recommendation module may also be configured to use a reinforcement learning algorithm to adapt the recommendations based on user feedback and engagement. The system may comprise a visualization module configured to create interactive dashboards displaying the user’ s belonging profile, identified patterns, and suggested actions. The visualization module may also be configured to generate detailed reports comprising analyses of the user’s belonging status and progress over time. The system may comprise a scalability and cloud integration layer configured to implement a distributed computing environment using a cloud platform to handle large datasets and high user loads. The data analysis module may be configured to utilize the belonging profile generated from the assessment to identify specific interventions or activities tailored to enhance the user's sense of belonging in specific areas. The system may also comprisea Belonging Integration System (BIS) configured to integrate input data from the user with predefined belonging metrics, generating a comprehensive belonging score that quantifies an overall sense of belonging across multiple domains. The data analysis module may further comprise a Belonging Integration System (BIS) configured to: compute scores for each segment of belonging using three components. These components include a Being Seen Score (BSS), which acknowledges identity, traits, values, and experiences; a Being Embraced Score (BES), which evaluates perception of acceptance and emotional connection in each category; and a Being Celebrated Score (BCS), which assesses how contributions and existence are recognized and valued. The module may then aggregate the scores for each segment to generate a comprehensive belonging profile that provides insights into a perceived sense of belonging across different domains. Implementations of the described techniques may comprise hardware, a method or process, or a computer tangible medium.

[0010] In one general aspect, the belonging integration architecture system may comprise Self-Belonging, representing an individual's personal relationship with their own identity, values, and lived experiences. The belonging integration architecture system may also comprise Interpersonal Belonging, representing the individual's sense of connection, acceptance, and emotional support within close personal relationships. The system may furthermore comprise Community Belonging, representing the individual's sense of membership, recognition, and contribution within social groups, organizations, or communities. The system may in addition comprise Universal Belonging, representing perceived connection to a broader human experience and world at large. The system may also comprise intersecting or overlapping areas of the segments of Self-Belonging, Interpersonal Belonging, Community Belonging, and Universal Belonging. Each intersection integrates attributes and characteristics from the intersecting or overlapping areas. The system may also be configured to present a plurality of predefined statements to a user, each statement corresponding to a specific segment of belonging; and to receive and categorize input data in response to the predefined statements, categorizing the data into one or more of the segments of belonging and their intersections. The system may also be configured to calculate, for each segment, a Being Seen Score (BSS), a Being Embraced Score (BES), and a Being Celebrated Score (BCS) based on responses or data inputted. The Being Seen Score (BSS) measures the extent to which identity, traits, values, andexperiences are acknowledged; the Being Embraced Score (BES) evaluates perception of acceptance and emotional connection in each category; and the Being Celebrated Score (BCS) assesses how contributions and existence arc recognized and valued. The system may then aggregate the scores for each segment to generate a comprehensive belonging profile that provides insights into the user’s perceived sense of belonging across different domains and intersections. The system may also be configured to identify belonging gaps by comparing the user's scores against predefined threshold values for each segment and intersection. Additionally, the system may generate personalized feedback and recommendations based on the identified belonging gaps, suggesting specific interventions to enhance a sense of belonging in areas with lower scores. Other embodiments of this aspect comprise corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

[0011] In one general aspect, the method may comprise: a. receiving, by a processor, input data from a user, the input data comprising responses to a plurality of predefined statements, where each statement corresponds to a segment of belonging selected from Self-Belonging, Interpersonal Belonging, Community Belonging, and Universal Belonging. The method may also comprise b. storing, by the processor, the received input data in a data storage module, the data storage module comprising a relational database for structured data and a SQL and / or NoSQL database for unstructured data. The method may furthermore comprise c. analyzing, by the processor, the received input data using a data analysis module. The analysis may comprise applying feature extraction and transformation techniques, comprising natural language processing (NLP) and dimensionality reduction, and generating a belonging profile for the user. This belonging profile identifies areas of belonging and potential gaps based on responses or data inputted. The method may also include generating, by the processor, personalized recommendations using a recommendation module. These recommendations are tailored to address the identified belonging gaps. The method may moreover comprise e. transmitting, by the processor, the belonging profile and personalized recommendations to a user device upon request. Other embodiments of this aspect comprise corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

[0012] Implementations may comprise one or more of the following features. The method may comprise validating and preprocessing the input data in real-time using dynamic feedback mechanisms before storing it in the data storage module. The method where the step of storing the input data further comprises: storing the structured data, comprising predefined psychological attributes and user identifiers, in the relational database; and storing unstructured and semistructured data, comprising user narratives and dynamic psychological profiles, in the SQL and / or NoSQL database. The method where the step of analyzing the input data further comprises applying machine learning algorithms to convert the input data into structured representations for use in generating the belonging profile. The method may also include utilizing the belonging profile generated from the assessment to identify specific interventions or activities tailored to enhance the user's sense of belonging in specific areas. The method where the step of analyzing the input data comprises using a Belonging Integration System (BIS) configured to: compute scores for each segment of belonging using three components, the components comprising: a Being Seen Score (BSS), which measures an extent to which identity, traits, values, and experiences are acknowledged; a Being Embraced Score (BES), which evaluates perception of acceptance and emotional connection in each category; and a Being Celebrated Score (BCS), which assesses the degree to which contributions and existence a e recognized and valued. The scores for each segment are then calculated to generate a comprehensive belonging profile that provides insights into the perceived sense of belonging across different domains. Implementations of the described techniques may comprise hardware, a method or process, or a computer tangible medium.

[0013] In one general aspect, the method may comprise a. presenting, by a user interface, a plurality of predefined statements to a user, each statement corresponding to a specific segment of belonging, comprising segments Self-Belonging, Interpersonal Belonging, Community Belonging, and Universal Belonging. The method may also comprise receiving and categorizing, by a processor, input data from the user in response to the predefined statements, the categorization comprising one or more of the segments of belonging and their intersections. The method may furthermore comprise c. calculating, by the processor, for each segment a Being Seen Score (BSS), a Being Embraced Score (BES), and a Being Celebrated Score (BCS), based on responses or data inputted. The Being Seen Score (BSS) measures the extent to whichidentity, traits, values, and experiences are acknowledged. The Being Embraced Score (BES) evaluates perception of acceptance and emotional connection in each category. The Being Celebrated Score (BCS) assesses the degree to which contributions and existence arc recognized and valued.

[0014] The method may also comprise calculating, by the processor, the scores for each segment to generate a comprehensive belonging profile that provides insights into the user’s perceived sense of belonging across different domains and intersections. The method may also include identifying belonging gaps by comparing the user's scores against predefined threshold values for each segment and intersection. Additionally, the method may comprise generating personalized feedback and recommendations based on the identified belonging gaps, suggesting specific interventions to enhance a sense of belonging in areas with lower scores. Other embodiments of this aspect comprise corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

[0015] These and other objects, features, and advantages of the present disclosure will become more readily apparent from the attached drawings and the detailed description of the preferred embodiments, which follow.Brief Description of the Drawings

[0016] FIG. 1 is an exemplary map illustration of the 4 types of belonging and the intersecting or overlapping areas of each of the 4 types of belonging; and

[0017] FIG. 2 presents an exemplary aspect of the computer system as described in the disclosure.

[0018] Like reference numerals refer to like parts throughout the several views of the drawings.Detailed Description

[0019] The following detailed description is merely exemplary in nature and is not intended to limit the described embodiments or the application and uses of the described embodiments. As used herein, the word “exemplary” or “illustrative” means “serving as an example, instance, or illustration.” Any implementation described herein as “exemplary” or “illustrative” is not necessarily to be construed as preferred or advantageous over other implementations. All of the implementations described below are exemplary implementations provided to enable persons skilled in the ait to make or use the embodiments of the disclosure and are not intended to limit the scope of the disclosure, which is defined by the claims. For purposes of description herein, the terms “upper”, “lower”, “left”, “rear”, “right”, “front”, “vertical”, “horizontal”, and derivatives thereof shall relate to the invention as oriented in FIG. 1, if FIG. 1 is present in this disclosure. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary or the following detailed description. It is also to be understood that the specific devices and processes illustrated in the attached drawings, and described in the following specification, are simply exemplary embodiments of the inventive concepts defined in the appended claims. Hence, specific dimensions and other physical characteristics relating to the embodiments disclosed herein are not to be considered as limiting, unless the claims expressly state otherwise.

[0020] The present disclosure provides a computer-implemented system, manual system, and method for assessing and enhancing an individual's sense of belonging through a comprehensive framework known as the Belonging Integration Scale (BIS). The system measures the degree to which an individual experiences a sense of belonging across four categories and their intersections, resulting in distinct segments. Each segment can be evaluated based on the degree to which the individual experiences three core components of belonging: being seen, being embraced, and being celebrated.

[0021] FIG. 1 is an exemplary illustration of a belonging integration map illustrating the various overlaps of the four types of belonging discussed herein. Each overlap creates a room with a specific architecture and that architecture can be used to employ the system and methods described herein.

[0022] Belonging Integration Scale (BIS) Architecture

[0023] The BIS architecture categorizes belonging into four primary domains:

[0024] 1. Self-Belonging (S): The individual’s relationship with themselves.

[0025] 2. Interpersonal Belonging (I): The individual's sense of belonging in one-on-one relationships.

[0026] 3. Community Belonging (C): The individual's sense of belonging within groups or communities.

[0027] 4. Universal Belonging (U): The individual’s sense of purpose and belonging in the broader context of society and the world.

[0028] These four primary categories can be further divided into overlapping areas, which represent intersections of the primary categories as follows:

[0029] S and I: Self-Belonging and Interpersonal Belonging

[0030] S and C: Self-Belonging and Community Belonging

[0031] S and U: Self-Belonging and Universal Belonging

[0032] I and C: Interpersonal Belonging and Community Belonging

[0033] I and U: Interpersonal Belonging and Universal Belonging

[0034] C and U: Community Belonging and Universal Belonging

[0035] S, I, and C: Self-Belonging, Interpersonal Belonging, and Community Belonging

[0036] S, 1, and U: Self-Belonging, Interpersonal Belonging, and Universal Belonging

[0037] S, C, and U: Self-Belonging, Community Belonging, and Universal Belonging

[0038] I, C, and U: Interpersonal Belonging, Community Belonging, and Universal Belonging

[0039] S, I, C, and U: Self-Belonging, Interpersonal Belonging, Community Belonging, and Universal Belonging

[0040] Each of the BIS segments can be assessed based on the degree to which an individual experiences three distinct components of belonging:

[0041] 1. Being Seen: This component measures the degree to which an individual’s traits, values, preferences, behavioral patterns, identities, and lived experiences are recognized and understood. It involves the identification and acknowledgment of various aspects of the individual’s personality and experiences.

[0042] 2. Being Embraced: This component assesses the emotional connection and acceptance the individual feels. It is characterized by an intrapsychic connection through which the individual's authentic self is actively accepted, encouraged, and welcomed.

[0043] 3. Being Celebrated: This component measures the extent to which the individual is openly and respectfully acknowledged for their lived experiences, perspectives, accomplishments, and overall being. It comprises both public and private recognition of the individual's contributions and value.

[0044] Exemplary Assessment:

[0045] Self

[0046] I can identify an extensive list of personality traits and values in myself. (See)

[0047] I reject parts of myself out of fear that others will not like me. (Embrace)

[0048] I advocate for myself even if it makes others uncomfortable. (Celebrate)

[0049] Interpersonal

[0050] I feel deeply understood in my one-on-one relationships. (See)

[0051] I feel valuable and loved in my interpersonal relationships. (Embrace)

[0052] I have 1:1 relationships with people who acknowledge my strengths, talents, skills, and / or gifts without feeling threatened by them. (Celebrate)

[0053] Community

[0054] In general, I have at least one group of people in my life who know me intimately. (See)

[0055] I usually feel that my family of origin and / or chosen family (group of good friends) genuinely like me. (Embrace)

[0056] I have a group people in at least one area of my life who make a point of positively acknowledging my accomplishments. (Celebrate)

[0057] Universal

[0058] I often wonder if there is any place in the world where people understand me. (See)

[0059] I believe that my presence makes the world a better place. (Embrace)

[0060] I feel a strong life calling or purpose that adds to the greater good. (Celebrate)

[0061] Self + Interpersonal

[0062] I feel safe sharing personal things about myself in my 1:1 relationships. (Seen)

[0063] I have 1:1 connections with people who love me just the way I am. (Embrace)

[0064] I feel safe telling a friend (or partner or colleague, etc.) when I am upset about something. (Celebrate)

[0065] Self + Community

[0066] I am part of a community that shares my values (See).

[0067] I am part of a community that values my contributions (Embrace).

[0068] I am part of a community that respects my boundaries (Celebrate).

[0069] Self + Universal

[0070] Sometimes I feel like I have an interpersonal relationship with a life purpose (See).

[0071] I feel safe walking through life to the beat of my own drum (Embrace).

[0072] I feel like my life falls into place when my actions match my values (Celebrate).

[0073] Interpersonal + Community

[0074] My (sibling, friend, partner, colleague, etc.) and I feel understood within our communities (Embrace).

[0075] My (sibling, friend, partner, colleague, etc.) and I feel accepted when we are in public together (Embrace).

[0076] My (sibling, friend, partner, colleague, etc.) and I feel respectfully acknowledged within our communities (Celebrate).

[0077] Interpersonal + Universal

[0078] I have at least one person in my life who shares similar values or worldviews with me (See).

[0079] I have at least one person in my life whose shared values gives me a sense of peace (Embrace).

[0080] I have at least one person in my life whose behaviors align with our shared values (Celebrate).

[0081] Community + Universal

[0082] 1 am part of at least one community in my life who shares my values (See).

[0083] I am part of at least one community in my life who (Embrace).

[0084] I have at least one community in my life that demonstrates their values through action (Celebrate).

[0085] Self + Interpersonal + Community

[0086] Within a group of people (family, work, etc.), I have individual relationships with people who understand me. (See)

[0087] Within a group of people (family, work, etc.), I have individual relationships with people who allow me to be myself. (Embrace)

[0088] Within a group of people (family, work, etc.), I have individual relationships with people who encourage me to be myself. (Celebrate)

[0089] Self + Interpersonal + Universal

[0090] I have at least one person in my life who understands my life purpose in this world (See).

[0091] I have at least one person in my life who accepts my life purpose in this world (Embrace).

[0092] I have at least one person in my life who makes an effort to support my life purpose in this world (Celebrate).

[0093] Self + Community + Universal

[0094] I am part of a group of people (family, work, etc.) who understand me. (See)

[0095] I am part of a group of people (family, work, etc.) who allow me to be myself.(Embrace)

[0096] I am part of a group of people (family, work, etc.) who encourage me to be myself. (Celebrate)

[0097] Interpersonal + Community + Universal

[0098] I have a community where I have interpersonal relationships, where we understand each shared values or identities or lived experiences (See).

[0099] I have a community where I have interpersonal relationships, where we accept our shared values or identities or lived experiences, even if they are socially unpopular or unaccepted (Embrace).

[0100] I have a community where I have interpersonal relationships, where we respectively acknowledge our shared values or identities or lived experiences (Celebrate).

[0101] Self + Interpersonal + Community + Universal

[0102] I feel personally understood in a community where I have interpersonal relationships, where we have shared values or identities or lived experiences (See).

[0103] I feel individually accepted by a community where I have interpersonal relationships, where we have shared values or identities or lived experiences (Embrace).

[0104] I am respectively acknowledged in a community where I have interpersonal relationships, where we hold shared values or identities or lived experiences (Celebrate).

[0105] Exemplary Technical Implementation

[0106] The BIS can be implemented through a computer architecture that comprises the following layers and components:

[0107] User Input Interface Layer (UIIL):

[0108] The UIIL can be a web-based interface designed using HTML5, CSS3, and JavaScript frameworks such as React and Angular'. This interface may allow users to input detailed data about their identity, traits, preferences, and lived experiences. The interface can support real-time validation and dynamic feedback using AJAX and WebSocket protocols.

[0109] Data Storage and Management Layer (DSML):

[0110] - The DSML may utilize a multi-model database architecture, incorporating both relational and SQL and / or NoSQL databases. A relational database (e.g., PostgreSQL) stores structured data such as predefined psychological attributes and user identifiers. Concurrently, a SQL and / or NoSQL database (e.g., MongoDB) may be employed to store unstructured and semistructured data, including but not limited to, narratives and dynamic psychological profiles. This hybrid approach can allow for flexible data representation and efficient querying across diverse data types.

[0111] Data Processing and Analysis Layer (DPAL)

[0112] - Data Ingestion and Preprocessing Module (DIPM): The DIPM may employApache Kafka for real-time data ingestion from the UIIL to the DSML. Raw data may be prcproccsscd using Apache Spark, where it undergoes cleansing, normalization, and enrichment processes. Natural language processing (NLP) techniques may be implemented using Python libraries like SpaCy, and used to extract semantic meaning from unstructured text inputs, such as user narratives and experiences.

[0113] - Feature Extraction and Transformation Module (FETM): The FETM may employ advanced feature engineering techniques to convert preprocessed data into structured representations. Multi-dimensional arrays and sparse matrices can be employed to model complex relationships between psychological traits and belonging dimensions. This module can be programmed to apply dimensionality reduction techniques such as Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) to distill high-dimensional data into a manageable number of components that are fed into machine learning models.

[0114] Machine Learning and Belonging Integration Layer (MLB IL)

[0115] - Machine Learning Model Repository (MLMR): The MLBIL can be built to be capable of hosting a repository of machine learning models, comprising convolutional neural networks (CNNs) for pattern recognition in user input data, and recurrent neural networks (RNNs) for analyzing temporal data patterns. These models may be stored in a containerized environment managed by Kubernetes and are accessible via RESTful APIs.

[0116] - Belonging Integration Engine (BIE): The BIE may be an ensemble system that is programmed to combine outputs from multiple machine learning models to generate a comprehensive belonging profile for the user. The engine can be programmed to employ meta- leaming techniques to dynamically weigh the contributions of each model based on real-time data, optimizing accuracy and robustness.

[0117] - Contextual Analysis Module (CAM): The CAM can comprise graph databases(e.g., Neo4j) to map complex interrelationships between different belonging elements and user contexts. It can be programmed to employ graph traversal algorithms to identify key nodes and edges that represent significant social interactions, community dynamics, and temporal changes in the user's belonging profile.

[0118] Recommendation and Feedback Layer (RFL)

[0119] -Recommendation System (RS): The RS collaborative filtering and content-based filtering techniques to provide personalized recommendations. Reinforcement learning algorithms, such as Q-learning, may be employed to adapt the recommendation engine based on user feedback and engagement. This can enable the system to continuously refine its suggestions for enhancing the user's sense of belonging.

[0120] - Visualization and Reporting Module (VRM): The VRM can comprise and employ data visualization libraries such as D3.js and Chart. js to create interactive dashboards. These visualizations can display the user’s belonging profile, the identified patterns, andsuggested actions in a clear and accessible format. Reports generated by the VRM can comprise detailed analyses of the user’s belonging status and progress over time.

[0121] Scalability and Cloud Integration Layer (SCIL)

[0122] -Distributed Computing Environment (DCE): The system can comprise programming and hardware to leverage cloud computing platforms like AWS or Google Cloud to implement a distributed computing environment. Kubernetes may be used for container orchestration, and Apache Hadoop is employed for distributed data storage and processing. This architecture can assist with horizontal scalability, enabling the system to handle large datasets and high user loads while maintaining performance and reliability.

[0123] Exemplary Belonging Integration Process

[0124] 1. Data Collection: Data can be inputted through the UIIL, which then can be communicated to the DSML using the DIPM. Real-time validation can be employed to determine if all necessary data is collected.

[0125] 2. Data Analysis: The DPAL can be employed to process the data, extract and transform features using the FETM. The transformed data can be analyzed by machine learning models stored in the MLB IL or through a manual process.

[0126] 3. Belonging Profile Generation: The BIE can be programmed to aggregate model outputs and contextual data to generate a detailed belonging profile, identifying strengths and areas for improvement across the segments of the BIS.

[0127] 4. Personalized Recommendations: The RS in the RFL can provide tailored suggestions and strategies based on the belonging profile. The recommendations can be dynamic, adapting to feedback and behavioral data.

[0128] 5. Feedback and Visualization: The VRM can display interactive visualizations and detailed reports, offering insights into the belonging status and actionable steps for improvement or modification.

[0129] Exemplary Data Structures and Technical Benefits

[0130] The advanced data structures employed in the BIS system can provide several technical advantages over traditional manual methods:

[0131] - Multi-Dimensional Arrays (MDAs): MDAs may be employed to represent complex psychological traits and their interactions across multiple belonging dimensions. This structure can be programmed to allow for efficient storage and retrieval of high-dimensional data.

[0132] - Sparse Matrices: The system can be programmed to output and display parse matrices for representing relationships between traits and belonging components. If data is sparse (i.e., when not all traits are relevant to all components), the sparse matrices can be employed to reduce memory usage and computational complexity.

[0133] -Graph-Based Representations: The use of graph databases for the CAM can allow for efficient modeling of social interactions and community dynamics, which are difficult to represent in traditional relational databases.

[0134] A high sense of belonging has been associated with better health, longevity, psychological well-being, and disease recovery.

[0135] Exemplary Technical Process Description for Belonging Integration

[0136] Exemplary Process Overview:

[0137] The following method describes an exemplary systematic approach implemented within a computer system to assess and enhance an individual’s sense of belonging across four primary categories: Self-Belonging, Interpersonal Belonging, Community Belonging, and Universal Belonging. The method is designed to identify and address psychological gaps in these categories by leveraging a structured set of queries and algorithms that evaluate belonging components through a multi-stage data processing system.

[0138] Step 1: Data Acquisition

[0139] 1.1. Input Acquisition:

[0140] The system may be initiated by receiving input data from a user through a user interface. This data may comprise demographic information, personal values, traits, and lived experiences. The data collection process may comprise presenting the user with a series of predefined statements, each linked to a specific belonging component and category.

[0141] 1.2. Statement Presentation:

[0142] The system can present a plurality of statements, with each statement corresponding to one of the segments of belonging. Each segment may be evaluated using three components: Being Seen, Being Embraced, and Being Celebrated. The user can respond to these statements using a 9-point Likert scale ranging from 1 (strongly disagree) to 9 (strongly agree).

[0143] Step 2: Data Processing and Categorization

[0144] 2.1. Data Parsing and Normalization:

[0145] Responses can be parsed and normalized to a standard format using a data preprocessing module. This module can be programmed to convert the raw Likert scale responses into a numerical format that can be further processed and analyzed.

[0146] 2.2. Categorical Assignment:

[0147] The system can be programmed to categorize each response into one of the four primary categories (Self-Belonging, Interpersonal Belonging, Community Belonging, Universal Belonging) and further, into their respective intersections. This categorization may be based on predefined mappings between the statements and the belonging segments.

[0148] 2.3. Component Scoring:

[0149] For each segment, the system can be programmed to compute and / or calculate scores for the three components of belonging:

[0150] - Being Seen Score (BSS): Measures the extent to which the user's identity, traits, values, and experiences are acknowledged.

[0151] - Being Embraced Score (BES): Evaluates the user’s perception of acceptance and emotional connection in each category.

[0152] - Being Celebrated Score (BCS): Assesses the degree to which the user’s contributions and existence are recognized and valued.

[0153] Step 3: Belonging Gap Analysis

[0154] 3.1. Baseline Calculation:

[0155] The system may be be programmed to compute baseline scores for each of the segments by averaging the BSS, BES, and BCS scores for each segment. The scores cam beemployed to create a quantitative representation of the user’s overall sense of belonging in each category and intersection.

[0156] 3.2. Gap Identification:

[0157] The system may be programmed to identify belonging gaps by comparing the user’s scores against predefined threshold values for each segment. Segments with scores below the threshold may be used to identify or indicate a gap in the user’s sense of belonging.

[0158] Step 4: Personalized Feedback and Recommendations

[0159] 4.1. Feedback Generation:

[0160] Based on the gap analysis, the system can be programmed to generate personalized feedback for the user. This feedback may comprise a detailed explanation of identified gaps in belonging and specific components that require improvement.

[0161] 4.2. Recommendation Algorithm:

[0162] The system may comprise a recommendation module that may employ a collaborative filtering and content-based algorithms to suggest personalized actions for enhancing belonging. Recommendations may be derived from a database of potential interventions, suggestions, and / or strategies, comprising personal reflection exercises, community engagement activities, and interpersonal relationship improvement strategies.

[0163] Step 5: User Engagement and Iterative Improvement

[0164] 5.1. User Feedback Integration:

[0165] The system may be programmed to allow users to provide feedback on the relevance and effectiveness of the recommendations. This feedback can be collected through postintervention surveys and is used to refine the recommendation algorithm.

[0166] 5.2. Iterative Reassessment:

[0167] The system may be programmed to schedule periodic reassessments where the user retakes the belonging scale assessment. The reassessment results can be compared against previous results to measure progress and update the user's belonging profile, and the reassessment results and the compared results can be displayed to a user on a display device.

[0168] Step 6: System Integration and Data Security

[0169] 6.1. Cloud-Based Storage and Processing:

[0170] The system can be programmed and comprise the proper hardware and be connected to a network to allow it to integrate with a cloud-based infrastructure to securely store user data and enable scalable data processing. Data may be stored in an encrypted format to ensure privacy and compliance with data protection regulations.

[0171] 6.2. Access Control and User Authentication:

[0172] The system employs role-based access control and multi-factor authentication to restrict access to user data and ensure that only authorized personnel can view or modify the information.

[0173] FIG. 2 presents an exemplary aspect of the computer system as described in the disclosure. The computer system 10 may comprise a processor 12, a memory 13, a communications component 13, database 15, TO component 15, power supply 17, and Bus 18, all in electronic communication with each other. The computer system 10 may be connected to network 50. The network 50 may be connected to one or more device 28 comprising a display. The computer system 10 may also comprise a display. Device 28 can be a desktop computer or mobile device, such as a cell phone, laptop, tablet, etc. Device 28 can send and receive information and data from network 50. Network 50 can send and receive data or information from computer system 10.

[0174] FIGs. 3A-3B present an exemplary flow diagram of an aspect of the disclosure. The system 10 may comprise a computer and computer network and can receive, by a processor, input data from a user, the input data comprising responses to a plurality of predefined statements, wherein each statement corresponds to a segment of belonging selected from SelfBelonging, Interpersonal Belonging, Community Belonging, and Universal Belonging 102. The system 100 can store, by the processor, the received input data in a data storage module, the data storage module comprising a relational database for structured data and a database for unstructured data 104, such as an SQL or SQL and / or NoSQL database. The method for system 100 can be adapted and / or programmed to analyze, by the processor, the received input data using a data analysis module, the analysis comprising: applying feature extraction andtransformation techniques, comprising natural language processing (NLP) and dimensionality reduction; generating a belonging profile for the user, the belonging profile identifying areas of belonging and potential gaps based on responses or data inputted 106, . generate, by the processor, personalized recommendations using a recommendation module, the recommendations tailored to address the identified belonging gaps 108, and transmit, by the processor, the belonging profile and personalized recommendations to a device upon request 110. The method for system 100 can be adapted and / or programmed to further comprise validating and preprocessing the input data in real-time using dynamic feedback mechanisms before storing it in the data storage module 112.

[0175] FIGs. 4A-4B present an exemplary flow diagram of an aspect of the disclosure. The system 200 may present, by a user interface, a plurality of predefined statements to a user, each statement corresponding to a specific segment of belonging, comprising segments Self Belonging, Interpersonal Belonging, Community Belonging, and Universal Belonging 202, . receive and categorizing, by a processor, input data from the user in response to the predefined statements, the categorization comprising one or more of the segments of belonging and their intersections 204, calculate, by the processor, for each segment a Being Seen Score (BSS), a Being Embraced Score (BES), and a Being Celebrated Score (BCS), based on responses or data inputted 206, wherein the Being Seen Score (BSS) measures the extent to which identity, traits, values, and experiences are acknowledged; the Being Embraced Score (BES) evaluates perception of acceptance and emotional connection in each category; the Being Celebrated Score (BCS) assesses the degree to which contributions and existence are recognized and valued 208, calculate, by the processor, the scores for each segment to generate a comprehensive belonging profile that provides insights into the user’s perceived sense of belonging across different domains and intersections 210, identify belonging gaps by comparing the user's scores against predefined threshold values for each segment and intersection 212, and generate personalized feedback and recommendations based on the identified belonging gaps, suggesting specific interventions to enhance a sense of belonging in areas with lower scores 214.

[0176] Since many modifications, variations, and changes in detail can be made to the described preferred embodiments of the invention, it is intended that all matters in the foregoingdescription and shown in the accompanying drawings be interpreted as illustrative and not in a limiting sense.

[0177] In some embodiments the method or methods described above may be executed or carried out by a computing system comprising a tangible computer-readable storage medium, also described herein as a storage machine, that holds machine-readable instructions executable by a logic machine (i.e. a processor or programmable control device) to provide, implement, perform, and / or enact the above described methods, processes and / or tasks. When such methods and processes are implemented, the state of the storage machine may be changed to hold different data. For example, the storage machine may comprise memory devices such as various hard disk drives, CD, or DVD devices. The logic machine may execute machine -readable instructions via one or more physical information and / or logic processing devices. For example, the logic machine may be configured to execute instructions to perform tasks for a computer program. The logic machine may comprise one or more processors to execute the machine- readable instructions. The computing system may comprise a display subsystem to display a graphical user interface (GUI) or any visual element of the methods or processes described above.

[0178] For example, the display subsystem, storage machine, and logic machine may be integrated such that the above method may be executed while visual elements of the disclosed system and / or method are displayed on a display screen for user consumption. The computing system may comprise an input subsystem that receives input. The input subsystem may be configured to connect to and receive input from devices such as a mouse, keyboard or gaming controller. For example, a user input may indicate a request that certain task is to be executed by the computing system, such as requesting the computing system to display any of the above described information, or requesting that the user input updates or modifies existing stored information for processing. A communication subsystem may allow the methods described above to be executed or provided over a computer network. For example, the communication subsystem may be configured to enable the computing system to communicate with a plurality of personal computing devices. The communication subsystem may comprise wired and / or wireless communication devices to facilitate networked communication. The described methods orprocesses may be executed, provided, or implemented for a user or one or more computing devices via a computer-program product such as via an application programming interface (API).

[0179] Although some, and maybe preferred, embodiments of the disclosure have been described in detail herein, those skilled in the art will recognize that various substitutions and modifications may be made without departing from the scope and spirit of the appended claims. Thus, the scope of the invention should be determined by the appended claims and their legal equivalents.

Claims

What is claimed is:

1. A computer implemented system for assessing and enhancing an individual's sense of belonging, the system comprising: a processor, a memory storing executable instructions that, when executed by the processor, enable the system to: a. receive data; b. analyze the received data using machine learning algorithms to generate a report; c. transmit the report; a display, a user input interface layer (UIIL) configured: to present a plurality of predefined statements to a user on the display, each statement corresponding to a segment of belonging, wherein the segments of belonging comprising Self-Belonging, Interpersonal Belonging, Community Belonging, and Universal Belonging; receive input data from the user in response to the predefined statements, the input data comprising demographic information, personal values, traits, and lived experiences; a data storage module configured to store the received input data and the belonging segments associated with each user; a data analysis module configured to analyze the input data to generate a belonging profile for the user, wherein the belonging profile identifies areas of belonging and gaps; and a recommendation module configured to generate personalized recommendations based on the belonging profile to address the identified gaps.

2. The system of claim 1, wherein the user input interface is further configured to validate and preprocess the input data in real-time using dynamic feedback mechanisms.

3. The system of claim 1, wherein the data storage module comprises:a relational database for storing structured data such as predefined psychological attributes and user identifiers; and a SQL and / or NoSQL database configured to store unstructured and scmi-structurcd data, comprising narratives and dynamic psychological profiles.

4. The system of claim 1, wherein the data analysis module further comprises a feature extraction and transformation module configured to apply natural language processing (NLP) and dimensionality reduction to convert the input data into structured representations.

5. The system of claim 1, wherein the recommendation module is further configured to employ collaborative filtering and content-based filtering techniques to generate personalized recommendations for the user.

6. The system of claim 5, wherein the recommendation module is configured to use a reinforcement learning algorithm to adapt the recommendations based on user feedback and engagement.

7. The system of claim 1, further comprising a visualization module configured to create interactive dashboards displaying the user’s belonging profile, identified patterns, and suggested actions.

8. The system of claim 7, wherein the visualization module is configured to generate detailed reports comprising analyses of the user’s belonging status and progress over time.

9. The system of claim 1, further comprising a scalability and cloud integration layer configured to implement a distributed computing environment using a cloud platform to handle large datasets and high user loads.

10. The system of claim 1, wherein the data analysis module is configured to utilize the belonging profile generated from the assessment to identify specific interventions or activities tailored to enhance the user's sense of belonging in specific areas.The system of claim 1, wherein the data analysis module comprises a Belonging Integration System (BIS) configured to integrate input data from the user with predefined belonging metrics, generating a comprehensive belonging score that quantifies an overall sense of belonging across multiple domains.

11. The system of claim 1, wherein the data analysis module comprises a Belonging Integration System (BIS) configured to:compute scores for each segment of belonging using three components, the components comprising: a Being Seen Score (BSS), which acknowledges identity, traits, values, and experiences; a Being Embraced Score (BES), which evaluates perception of acceptance and emotional connection in each category; and a Being Celebrated Score (BCS), which assesses how contributions and existence are recognized and valued; and aggregate the scores for each segment to generate a comprehensive belonging profile that provides insights into a perceived sense of belonging across different domains.

12. A belonging integration architecture system, the system comprising:Self Belonging, representing an individual's personal relationship with their own identity, values, and lived experiences;Interpersonal Belonging, representing the individual's sense of connection, acceptance, and emotional support within close personal relationships;Community Belonging, representing the individual's sense of membership, recognition, and contribution within social groups, organizations, or communities;Universal Belonging, representing perceived connection to a broader human experience and world at large; and intersecting or overlapping areas of each of segments of Self Belonging, Interpersonal Belonging, Community Belonging, and Universal Belonging, wherein each intersection comprises an integration of attributes and characteristics of the intersecting or overlapping areas; wherein the system is configured to: present a plurality of predefined statements to a user, each statement corresponding to a specific segment of belonging; receive and categorize input data in response to the predefined statements, categorizing the data into one or more of the segments of belonging and their intersections; calculate for each segment a Being Seen Score (BSS), a Being Embraced Score (BES), and a Being Celebrated Score (BCS), based on responses or data inputted, wherein:the Being Seen Score (BSS) measures an extent to which identity, traits, values, and experiences are acknowledged; the Being Embraced Score (BES) evaluates perception of acceptance and emotional connection in each category; the Being Celebrated Score (BCS) assesses how contributions and existence are recognized and valued; aggregate the scores for each segment to generate a comprehensive belonging profile that provides insights into the user’s perceived sense of belonging across different domains and intersections; identify belonging gaps by comparing the user's scores against predefined threshold values for each segment and intersection; and generate personalized feedback and recommendations based on the identified belonging gaps, suggesting specific interventions to enhance a sense of belonging in areas with lower scores.

13. A method for assessing and enhancing an individual's sense of belonging, the method comprising: a. receiving, by a processor, input data from a user, the input data comprising responses to a plurality of predefined statements, wherein each statement corresponds to a segment of belonging selected from Self-Belonging, Interpersonal Belonging, Community Belonging, and Universal Belonging; b. storing, by the processor, the received input data in a data storage module, the data storage module comprising a relational database for structured data and a SQL and / or NoSQL database for unstructured data; c. analyzing, by the processor, the received input data using a data analysis module, the analysis comprising: applying feature extraction and transformation techniques, comprising natural language processing (NLP) and dimensionality reduction; generating a belonging profile for the user, the belonging profile identifying areas of belonging and potential gaps based on responses or data inputted;d. generating, by the processor, personalized recommendations using a recommendation module, the recommendations tailored to address the identified belonging gaps; and c. transmitting, by the processor, the belonging profile and personalized recommendations to a user device upon request.

14. The method of claim 13, further comprising validating and preprocessing the input data in real-time using dynamic feedback mechanisms before storing it in the data storage module.

15. The method of claim 13, wherein the step of storing the input data further comprises: storing the structured data, comprising predefined psychological attributes and user identifiers, in the relational database; and storing unstructured and semi-structured data, comprising user narratives and dynamic psychological profiles, in the SQL and / or NoSQL database.

16. The method of claim 13, wherein the step of analyzing the input data further comprises applying machine learning algorithms to convert the input data into structured representations for use in generating the belonging profile.

17. The method of claim 13, further comprising utilizing the belonging profile generated from the assessment to identify specific interventions or activities tailored to enhance the user's sense of belonging in specific areas.

18. The method of claim 13, wherein the step of analyzing the input data comprises using a Belonging Integration System (BIS) configured to: compute scores for each segment of belonging using three components, the components comprising: a Being Seen Score (BSS), which measures an extent to which identity, traits, values, and experiences are acknowledged; a Being Embraced Score (BES), which evaluates perception of acceptance and emotional connection in each category; and a Being Celebrated Score (BCS), which assesses degree to which contributions and existence are recognized and valued; and calculating the scores for each segment to generate a comprehensive belonging profile that provides insights into perceived sense of belonging across different domains.

19. A method for integrating and enhancing an individual's sense of belonging, the method comprising: a. presenting, by a user interface, a plurality of predefined statements to a user, each statement corresponding to a specific segment of belonging, comprising segments Self Belonging, Interpersonal Belonging, Community Belonging, and Universal Belonging; b. receiving and categorizing, by a processor, input data from the user in response to the predefined statements, the categorization comprising one or more of the segments of belonging and their intersections; c. calculating, by the processor, for each segment a Being Seen Score (BSS), a Being Embraced Score (BES), and a Being Celebrated Score (BCS), based on responses or data inputted, wherein: the Being Seen Score (BSS) measures the extent to which identity, traits, values, and experiences are acknowledged; the Being Embraced Score (BES) evaluates perception of acceptance and emotional connection in each category; the Being Celebrated Score (BCS) assesses the degree to which contributions and existence are recognized and valued; d. calculating, by the processor, the scores for each segment to generate a comprehensive belonging profile that provides insights into the user’s perceived sense of belonging across different domains and intersections; e. identifying belonging gaps by comparing the user's scores against predefined threshold values for each segment and intersection; and f. generating personalized feedback and recommendations based on the identified belonging gaps, suggesting specific interventions to enhance a sense of belonging in areas with lower scores.

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