A SYSTEM AND A METHOD FOR QUANTIFYING A LEXICAL HAPPINESS LEVEL OF A USER

Abstract

A system (100) for quantifying a lexical happiness level of a user is disclosed. A data collection module (120) collects input from a panel of experts to iteratively define a plurality of happiness metrics. The data collection module also collects physiological data, environmental data and textual data from the user. A processing module (125) refines the metrics using a combination of the Delphi method and genetic optimization techniques. Additionally, a data analysis module (130) analyzes the collected data, applying machine learning to identify trends and correlations in user sentiment and emotional well-being. A happiness index computation module (135) generates a dynamic happiness index, delivering real-time happiness scores for individuals, groups, or populations. A feedback module (140) provides personalized recommendations to enhance emotional well-being and offers actionable insights via a dashboard for policymakers, employers, and healthcare providers. FIG. 1

Specification

Description:FIELD OF INVENTION
[0001] Embodiments of the present disclosure relate to the field of emotional well-being, and more particularly, a system and a method for quantifying a lexical happiness level of a user.
BACKGROUND
[0002] Happiness is not just a fleeting emotion, it's a vital component of human well-being that impacts various aspects of our lives, including our productivity at work and our overall societal development. Recognizing its significance, governments worldwide are increasingly prioritizing the happiness of their citizens. Happiness comes from having a good life, though not necessarily an easy one. By measuring subjective well-being, individuals and societies can better understand current levels of satisfaction and identify areas for improvement, ultimately leading to a more meaningful and fulfilling life.
[0003] However, the challenge lies in quantifying this abstract concept in a scientific and measurable manner. Happiness may mean different things to different people, making it difficult to develop a universally applicable measurement system. A few of the challenges include the limitations of ordinal scale surveys, which do not quantify differences between values, making it hard to rank happiness accurately. Additionally, while physiological measurements of happiness are possible, the complexity of the human body, which produces over 50 hormones, complicates understanding all their triggers and functions. Furthermore, attempts by policymakers to influence happiness may sometimes substitute their perception of happiness for the preferences of individuals, leading to ineffective interventions.
[0004] Hence, there is a need for an improved system and a method for quantifying a lexical happiness level of a user which addresses the aforementioned issue(s).
OBJECTIVES OF THE INVENTION
[0005] Primary objective of the invention is to develop a system measure a lexical happiness level of a user by integrating the Delphi method with genetic algorithms to iteratively refine happiness metrics, for improving accuracy and reliability of happiness measurements.
[0006] Another objective of the invention is to facilitate the aggregation of data from a plurality of sources, such as social media, wearable devices, sensors, and the like, to provide a comprehensive happiness metrics and actionable insights for policymakers, employers, and healthcare providers to enhance societal well-being.
BRIEF DESCRIPTION
[0007] In accordance with an embodiment of the present disclosure, a system for quantifying a lexical happiness level of a user is provided. The system includes a processing subsystem hosted on a server. The processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes a data collection module configured to collect input from a panel of experts to iteratively define a plurality of happiness metrics. The data collection module is configured to collect physiological data and environmental data in real-time from a plurality of sensors. The plurality of sensors is placed in a wearable device worn by the user to collect the physiological data and placed in one more location within an environment pertaining to the user to collect the environmental data. The data collection module is configured to collect textual data from a plurality of user-generated content. The processing subsystem includes a processing module operatively coupled to the data collection module. The processing module is configured to refine the plurality of happiness metrics based on the input from the panel of experts by utilizing a Delphi method and one or more genetic optimization techniques. The processing subsystem includes a data analysis module operatively coupled to the processing module. The data analysis module is configured to analyze the physiological data, environmental data, and textual data using a machine learning model to identify one or more patterns, trends, and correlations. The data analysis module is configured to assess the textual data to identify public sentiment and emotional well-being by the machine learning model. The processing subsystem includes a happiness index computation module operatively coupled to the data analysis module. The happiness index computation module is configured to generate a dynamic happiness index based on the analysis and the plurality of happiness metrics refined. The happiness index computation module is configured to provide a real-time happiness score for individuals, groups, or a targeted population. The processing subsystem includes a feedback module operatively coupled to the happiness index computation module. The feedback module is configured to provide personalized recommendations and insights to a user based on the real-time happiness data to enhance emotional well-being. The feedback module is configured to display a comprehensive happiness metric for government policymakers, employers, and healthcare providers, derived from the happiness index via a dashboard.
[0008] In accordance with another embodiment of the present disclosure, a method for quantifying a lexical happiness level of a user is provided. The method includes collecting, by a data collection module, input from a panel of experts to iteratively define a plurality of happiness metrics. The method includes collecting, by the data collection module, physiological data and environmental data in real-time from a plurality of sensors. The plurality of sensors is placed in a wearable device worn by the user to collect the physiological data and placed in one more location within an environment pertaining to the user to collect the environmental data. The method includes collecting, by the data collection module, textual data from a plurality of user-generated content. The method includes refining, by a processing module, the plurality of happiness metrics based on the input from the panel of experts by utilizing a Delphi method and one or more genetic optimization techniques. The method includes analyzing, by a data analysis module, the physiological data, environmental data, and textual data using a machine learning model to identify one or more patterns, trends, and correlations. The method includes assessing, by a data analysis module, the textual data to identify public sentiment and emotional well-being by the machine learning model. The method includes generating, by a happiness index computation module, a dynamic happiness index based on the analysis and the plurality of happiness metrics refined. The method includes providing, by the happiness index computation module, a real-time happiness score for individuals, groups, or a targeted population. The method includes providing, by a feedback module, personalized recommendations and insights to a user based on the real-time happiness data to enhance emotional well-being. The method includes displaying, by the feedback module, a comprehensive happiness metric for government policymakers, employers, and healthcare providers, derived from the happiness index via a dashboard.
[0009] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:
[0011] FIG. 1 is a block diagram representation of a system for quantifying a lexical happiness level of a user in accordance with an embodiment of the present disclosure;
[0012] FIG. 2 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure;
[0013] FIG. 3(a) illustrates a flow chart representing the steps involved in a method for quantifying a lexical happiness level of a user in accordance with an embodiment of the present disclosure; and
[0014] FIG. 3(b) illustrates continued steps of the method of FIG. 3(a) in accordance with an embodiment of the present disclosure.
[0015] Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.
DETAILED DESCRIPTION
[0016] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.
[0017] The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or subsystems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.
[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.
[0019] In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms "a", "an", and "the" include plural references unless the context clearly dictates otherwise.
[0020] Embodiments of the present disclosure relate to a system for quantifying a lexical happiness level of a user. The system includes a processing subsystem hosted on a server. The processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes a data collection module configured to collect input from a panel of experts to iteratively define a plurality of happiness metrics. The data collection module is configured to collect physiological data and environmental data in real-time from a plurality of sensors. The plurality of sensors is placed in a wearable device worn by the user to collect the physiological data and placed in one more location within an environment pertaining to the user to collect the environmental data. The data collection module is configured to collect textual data from a plurality of user-generated content. The processing subsystem includes a processing module operatively coupled to the data collection module. The processing module is configured to refine the plurality of happiness metrics based on the input from the panel of experts by utilizing a Delphi method and one or more genetic optimization techniques. The processing subsystem includes a data analysis module operatively coupled to the processing module. The data analysis module is configured to analyze the physiological data, environmental data, and textual data using a machine learning model to identify one or more patterns, trends, and correlations. The data analysis module is configured to assess the textual data to identify public sentiment and emotional well-being by the machine learning model. The processing subsystem includes a happiness index computation module operatively coupled to the data analysis module. The happiness index computation module is configured to generate a dynamic happiness index based on the analysis and the plurality of happiness metrics refined. The happiness index computation module is configured to provide a real-time happiness score for individuals, groups, or a targeted population. The processing subsystem includes a feedback module operatively coupled to the happiness index computation module. The feedback module is configured to provide personalized recommendations and insights to a user based on the real-time happiness data to enhance emotional well-being. The feedback module is configured to display a comprehensive happiness metric for government policymakers, employers, and healthcare providers, derived from the happiness index via a dashboard.
[0021] FIG. 1 is a block diagram representation of a system (100) for quantifying a lexical happiness level of a user in accordance with an embodiment of the present disclosure. The system (100) includes a processing subsystem (105) hosted on a server (110). In one embodiment, the server (110) may include a cloud-based server. In another embodiment, parts of the server (110) may be a local server coupled to a user device (not shown in FIG.1). The processing subsystem (105) is configured to execute on a network (115) to control bidirectional communications among a plurality of modules. In one example, the network (115) may be a private or public local area network (LAN) or Wide Area Network (WAN), such as the Internet. In another embodiment, the network (115) may include both wired and wireless communications according to one or more standards and/or via one or more transport mediums. In one example, the network (115) may include wireless communications according to one of the 802.11 or Bluetooth specification sets, or another standard or proprietary wireless communication protocol. In yet another embodiment, the network (115) may also include communications over a terrestrial cellular network, including, a global system for mobile communications (GSM), code division multiple access (CDMA), and/or enhanced data for global evolution (EDGE) network.
[0022] The processing subsystem (105) includes a data collection module (120) configured to collect input from a panel of experts to iteratively define a plurality of happiness metrics. The panel of experts refers to a group of selected subject matter experts with deep knowledge or experience in areas that influence happiness metrics. The data collection module (120) employs a series of questionnaires to collect the opinions from the panel of experts. The questionnaires include questions related to assessing happiness in individuals or groups. In an embodiment, the input from the panel of experts is collected through both online and offline mode.
[0023] The plurality of happiness metrics refers to ways of measuring happiness. The plurality of happiness metrics includes but is not limited to happiness index, life satisfaction, general happiness scale, emotional well-being, physical well-being, social relationships and the like. The plurality of happiness metrics may also be used to measure the user experience of a product or service. The plurality of happiness metrics may relate to subjective aspects of the user experience, such as satisfaction, perceived ease of use, visual appeal, and likelihood to recommend.
[0024] The data collection module (120) also collects physiological data and environmental data in real-time from a plurality of sensors. The plurality of sensors is strategically positioned in different locations based on the type of data being captured. The plurality of sensors that are used to collect the physiological data and are placed into a wearable device worn by the user. Examples for the wearable device include but are not limited to smartwatches, fitness trackers, smart bands and the like. The physiological data includes heart rate variability, skin conductance, respiration rate and the like to monitor emotional well-being of the user. Examples of the plurality of sensors used to collect the physiological data include but not limited to heart rate sensors, skin conductance sensors, respiration rate sensors, and the like which are integrated into the wearable devices.
[0025] Further, the plurality of sensors that are used to collect the environmental data are placed in one more location within an environment pertaining to the user. Examples of environment include but not limited to a workplace, classroom, conference hall and the like. The environmental data includes air quality, temperature, noise levels, and the like, which contributes to the happiness index. Examples of the plurality of sensors used to collect the environmental data include but not limited to air quality monitor, thermo-hygrometer, ambient noise sensor and the like.
[0026] In one embodiment, the physiological data may be collected using a user device, such as a smartphone, equipped with sensors for tracking the physiological data and environmental data. It is to be noted that the user device may include , but is not limited to, a mobile phone, desktop computer, portable digital assistant (PDA), smart phone, tablet, ultra-book, netbook, laptop, multi-processor system, microprocessor-based or programmable consumer electronic system, or any other communication device that a user may use. In some embodiments, the user device may comprise a display module (not shown) to display information (for example, in the form of user interfaces). In further embodiments, the user device may comprise one or more of touch screens, accelerometers, gyroscopes, cameras, microphones, global positioning system (GPS) devices, and so forth.
[0027] The data collection module (120) is configured to collect textual data from a plurality of user-generated content. The plurality of user-generated content includes a plurality of social media platforms and surveys.
[0028] The processing subsystem (105) includes a processing module (125) operatively coupled to the data collection module (120). The processing module (125) is configured to refine the plurality of happiness metrics based on the input from the panel of experts by utilizing a Delphi method and one or more genetic optimization techniques. The Delphi method is a structured communication technique used to gather opinions from the panel of experts to achieve convergence on complex issues, such as defining the happiness metrics. More specifically, after the input is collected through the questionnaire, the processing module (125) analyzes the input to identify recurring themes, trends, and areas of agreement among the panel of experts. Through several iterative rounds of feedback, a final set of metrics is defined, leading to the establishment of refined the plurality of happiness metrics. The plurality of happiness metrics then serves as inputs for further processing and optimization. The one or more genetic optimization techniques are evolutionary algorithms inspired by natural selection processes, capable of optimizing complex problems. Here, these may help with the aggregation of diverse happiness indicators.
[0029] The processing subsystem (105) includes a data analysis module (130) operatively coupled to the processing module (125). The data analysis module (130) is configured to analyze the physiological data, environmental data, and textual data using a machine learning model to identify one or more patterns, trends, and correlations. It must be noted that the artificial intelligence model is configured with an artificial intelligence algorithm. Examples of the artificial intelligence algorithm include, but is not limited to, a Deep Neural Network (DNN), Convolutional Neural Network (CNN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN) and Deep Q-Networks.
[0030] It must be noted that the artificial intelligence model is trained on a dataset including historical physiological, environmental, and textual data from a plurality of users.
[0031] The data analysis module (130) is configured to assess the textual data to identify public sentiment and emotional well-being by the machine learning model. Natural Language Processing (NLP) is used to assess textual data to identify public sentiment and emotional well-being.
[0032] Natural Language Processing (NLP) refers to the field of artificial intelligence (AI), that enables computers to understand, interpret, and generate human language in a manner that is both meaningful and contextually relevant. The NLP model utilizes algorithms and techniques to process large amounts of natural language data, extracting meaningful insights, and enabling various applications such as machine translation, sentiment analysis, and speech recognition. The algorithms used in NLP model includes but are not limited to Hidden Markov Models (HMMs) or Conditional Random Fields (CRFs), support Vector Machines (SVM), Decision Trees, Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and the like.
[0033] The processing subsystem (105) includes a happiness index computation module (135) operatively coupled to the data analysis module (130). The happiness index computation module (135) is configured to generate a dynamic happiness index based on the analysis and the plurality of happiness metrics refined. The dynamic happiness index indicates an overall lexical happiness level by combining data from the plurality of sensors, sentiment analysis, and expert feedback.
[0034] In one embodiment, the dynamic happiness index generated by the happiness index computation module (135) is represented on a scale of 1 to 10, where 1 represents the lowest level of happiness or emotional well-being and 10 represents the highest.
[0035] The happiness index computation module (135) is configured to provide a real-time happiness score for individuals, groups, or a targeted population. The real-time happiness score indicates the current emotional state of individuals, groups, or entire populations.
[0036] A feedback module (140) is configured to provide personalized recommendations and insights to the user based on the real-time happiness data to enhance emotional well-being. The feedback module (140) is configured to display a comprehensive happiness metric for government policymakers, employers, and healthcare providers, derived from the happiness index via a dashboard. The dashboard aggregates data from social media, wearable devices, and environmental sensors, to provide comprehensive happiness metrics and actionable insights. The dashboard enables businesses to obtain insights into consumer preferences, sentiment, and purchasing behavior, guiding product development and marketing strategies.
[0037] In one embodiment, the feedback module (140) delivers personalized recommendations and insights to the user through a user device in various formats including but not limited to text notifications, push notifications, audio notifications and the like. The notifications may prompt users to take specific actions, such as practicing relaxation exercises or moving to a quieter environment, based on the real-time happiness data.
[0038] It is to be noted that, the system (100) may be integrated into wearable devices, allowing users to access their lexical happiness levels at any time. In one embodiment, employers may use the wearables devices to monitor the emotional well-being of employees, ensuring a positive work environment and potentially increasing productivity. Similarly, the healthcare providers may utilize these devices to track the mental health of patients, enabling early intervention and personalized treatment plans.
[0039] The system (100) may also function as a web or mobile application. In this embodiment, companies may integrate the application into wellness initiatives to provide employees with tools for managing stress, improving morale, and fostering a supportive workplace culture. Additionally, mental health professionals could recommend the application to clients as a complementary tool for self-reflection, mood tracking, and accessing emotional support resources.
[0040] Let's consider an example, a company "X" may integrate system (100) into wellness initiatives to provide employees with tools for managing stress. Each employee is equipped with a wearable device, such as a smartwatch, that collects physiological data like heart rate variability, respiration rate, and skin conductance. This data, along with environmental data such as office noise levels, air quality, and the like are collected by the data collection module (120). The data analysis module (130) analyzes real time data. The happiness index computation module (135) generates the dynamic happiness index based on the analysis and the plurality of happiness metrics refined from the Delphi method for each employee. If the system (100) detects rising stress levels in an employee due to increased heart rate variability or poor air quality, it immediately sends personalized recommendations via a mobile app. The feedback module (140) also sends insights to the each employee based on the real-time happiness data to enhance emotional well-being of the employees. Additionally, aggregated happiness data across the company "X" as dashboard allows the HR department to assess the emotional well-being of teams.
[0041] In one embodiment, the various functional components of the system may reside on a single computer, or they may be distributed across several computers in various arrangements. The various components of the system may, furthermore, access one or more databases, and each of the various components of the system may be in communication with one another. Further, while the components of FIG. 1 are discussed in the singular sense, it will be appreciated that in other embodiments multiple instances of the components may be employed.
[0042] FIG. 2 is a block diagram of a computer or a server (110) in accordance with an embodiment of the present disclosure. The server (110) includes processor(s) (230), and memory (210) operatively coupled to the bus (220). The processor(s) (230), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.
[0043] The memory (210) includes several subsystems stored in the form of executable program which instructs the processor (230) to perform the method steps illustrated in FIG. 1. The memory (210) includes a processing subsystem (105) of FIG.1. The processing subsystem (105) further has following modules: a data collection module (120), a processing module (125), a data analysis module (130), a happiness index computation module (135), and a feedback module (140).
[0044] In accordance with an embodiment of the present disclosure, a system (100) for quantifying a lexical happiness level of a user is provided. The system (100) includes a processing subsystem (105) hosted on a server (110). The processing subsystem (105) is configured to execute on a network (115) to control bidirectional communications among a plurality of modules. The processing subsystem (105) includes a data collection module (120) configured to collect input from a panel of experts to iteratively define a plurality of happiness metrics. The data collection module (120) is configured to collect physiological data and environmental data in real-time from a plurality of sensors. The plurality of sensors is placed in a wearable device worn by the user to collect the physiological data and placed in one more location within an environment pertaining to the user to collect the environmental data. The data collection module (120) is configured to collect textual data from a plurality of user-generated content. The processing subsystem (105) includes a processing module (125) operatively coupled to the data collection module (120). The processing module (125) is configured to refine the plurality of happiness metrics based on the input from the panel of experts by utilizing a Delphi method and one or more genetic optimization techniques. The processing subsystem (105) includes a data analysis module (130) operatively coupled to the processing module (125). The data analysis module (130) is configured to analyze the physiological data, environmental data, and textual data using a machine learning model to identify one or more patterns, trends, and correlations. The data analysis module (130) is configured to assess the textual data to identify public sentiment and emotional well-being by the machine learning model. The processing subsystem (105) includes a happiness index computation module (135) operatively coupled to the data analysis module (130). The happiness index computation module (135) is configured to generate a dynamic happiness index based on the analysis and the plurality of happiness metrics refined. The happiness index computation module (135) is configured to provide a real-time happiness score for individuals, groups, or a targeted population. The processing subsystem (105) includes a feedback module (140) operatively coupled to the happiness index computation module (135). The feedback module (140) is configured to provide personalized recommendations and insights to a user based on the real-time happiness data to enhance emotional well-being. The feedback module (140) is configured to display a comprehensive happiness metric for government policymakers, employers, and healthcare providers, derived from the happiness index via a dashboard.
[0045] The bus (220) as used herein refers to be internal memory channels or computer network that is used to connect computer components and transfer data between them. The bus (220) includes a serial bus or a parallel bus, wherein the serial bus transmits data in bit-serial format and the parallel bus transmits data across multiple wires. The bus (220) as used herein, may include but not limited to, a system bus, an internal bus, an external bus, an expansion bus, a frontside bus, a backside bus and the like.
[0046] FIG. 3(a) illustrates a flow chart representing the steps involved in a method (300) for quantifying a lexical happiness level of a user in accordance with an embodiment of the present disclosure. FIG. 3(b) illustrates continued steps of the method (300) of FIG. 3(a) in accordance with an embodiment of the present disclosure. The method (300) includes collecting, by a data collection module, input from a panel of experts to iteratively define a plurality of happiness metrics in step 305. The panel of experts consists of selected subject matter experts with deep knowledge or experience in areas that influence happiness metrics. The data collection module employs a series of questionnaires to collect the opinions from the panel of experts. The questionnaires include questions related to assessing happiness in individuals or groups. In an embodiment, the input from the panel of experts is collected through both online and offline mode.
[0047] The plurality of happiness metrics refers to ways to measure happiness. The plurality of happiness metrics includes but is not limited to happiness index, life satisfaction, general happiness scale, emotional well-being, physical well-being, social relationships and the like. The plurality of happiness metrics may also be used to measure the user experience of a product or service. The plurality of happiness metrics may relate to subjective aspects of the user experience, such as satisfaction, perceived ease of use, visual appeal, and likelihood to recommend.
[0048] The method (300) includes collecting, by the data collection module, physiological data and environmental data in real-time from a plurality of sensors. The plurality of sensors is placed in a wearable device worn by the user to collect the physiological data and placed in one more location within an environment pertaining to the user to collect the environmental data in step 310. Examples for the wearable device include but are not limited to smartwatches, fitness trackers, smart bands and the like. The physiological data includes heart rate variability, skin conductance, respiration rate and the like to monitor emotional well-being of the user. Examples of the plurality of sensors used to collect the physiological data include but not limited to heart rate sensors, skin conductance sensors, respiration rate sensors, and the like which are integrated into the wearable devices. Examples of environment include but not limited to a workplace, classroom, conference hall and the like. The environmental data includes air quality, temperature, noise levels, and the like, which contributes to the happiness index. Examples of the plurality of sensors used to collect the environmental data include but not limited to air quality monitor, thermo-hygrometer, ambient noise sensor and the like.
[0049] In one embodiment, the physiological data may be collected using a user device, such as a smartphone, equipped with sensors for tracking the physiological data and environmental data.
[0050] The method (300) includes collecting, by the data collection module, textual data from a plurality of user-generated content in step 315. The plurality of user-generated content includes a plurality of social media platforms and surveys.
[0051] The method (300) includes refining, by a processing module, the plurality of happiness metrics based on the input from the panel of experts by utilizing a Delphi method and one or more genetic optimization techniques in step 320. The Delphi method is a structured communication technique used to gather opinions from the panel of experts to achieve convergence on complex issues, such as defining the happiness metrics. More specifically, after the input is collected through the questionnaire, the processing module analyzes the input to identify recurring themes, trends, and areas of agreement among the panel of experts. Through several iterative rounds of feedback, a final set of metrics is defined, leading to the establishment of refined the plurality of happiness metrics. The plurality of happiness metrics then serves as inputs for further processing and optimization. The one or more genetic optimization techniques are evolutionary algorithms inspired by natural selection processes, capable of optimizing complex problems. Here, these may help with the aggregation of diverse happiness indicators.
[0052] The method (300) includes analyzing, by a data analysis module, the physiological data, environmental data, and textual data using a machine learning model to identify one or more patterns, trends, and correlations in step 325.
[0053] It must be noted that the artificial intelligence model is trained on a dataset including historical physiological, environmental, and textual data from a plurality of users.
[0054] The method (300) includes assessing, by a data analysis module, the textual data to identify public sentiment and emotional well-being by the machine learning model in step 330. Natural Language Processing (NLP) is used to assess textual data to identify public sentiment and emotional well-being.
[0055] Natural Language Processing (NLP) refers to the field of artificial intelligence (AI), that enables computers to understand, interpret, and generate human language in a manner that is both meaningful and contextually relevant. The NLP model utilizes algorithms and techniques to process large amounts of natural language data, extracting meaningful insights, and enabling various applications such as machine translation, sentiment analysis, and speech recognition.
[0056] The method (300) includes generating, by a happiness index computation module, a dynamic happiness index based on the analysis and the plurality of happiness metrics refined in step 335. The dynamic happiness index indicates overall lexical happiness levels by combining data from the plurality of sensors, sentiment analysis, and expert feedback.
[0057] In one embodiment, the dynamic happiness index generated by the happiness index computation module is represented on a scale of 1 to 10, where 1 represents the lowest level of happiness or emotional well-being and 10 represents the highest.
[0058] The method (300) includes providing, by the happiness index computation module, a real-time happiness score for individuals, groups, or a targeted population in step 340. The real-time happiness score indicates the current emotional state of individuals, groups, or entire populations.
[0059] The method (300) includes providing, by a feedback module, personalized recommendations and insights to a user based on the real-time happiness data to enhance emotional well-being in step 345.
[0060] The method (300) includes displaying, by the feedback module, a comprehensive happiness metric for government policymakers, employers, and healthcare providers, derived from the happiness index via a dashboard in step 350. The dashboard aggregates data from social media, wearable devices, and environmental sensors, to provide comprehensive happiness metrics and actionable insights. The dashboard enables businesses to obtain insights into consumer preferences, sentiment, and purchasing behavior, guiding product development and marketing strategies.
[0061] In one embodiment, the feedback module delivers personalized recommendations and insights to the user through a user device in various formats including but not limited to text notifications, push notifications, audio notifications and the like. The notifications may prompt users to take specific actions, such as practicing relaxation exercises or moving to a quieter environment, based on the real-time happiness data.
[0062] Various embodiments of the system and the method for quantifying the lexical happiness level of the user as described above provides a comprehensive, real-time assessment of emotional well-being by integrating the physiological, environmental, and textual data along with expert input. The happiness index computation module generates the dynamic happiness index that reflects both individual and collective lexical happiness levels. Additionally, the feedback module offers personalized recommendations and insights to users, based on the real-time happiness data, to enhance their emotional well-being. For policymakers, employers, and healthcare providers, the feedback module delivers the comprehensive happiness metric via the dashboard. The dashboard allows government agencies to monitor overall lexical happiness levels of citizens and identify areas for targeted interventions such as infrastructure improvements, social programs, and community engagement initiatives. Businesses may also utilize the dashboard to gain insights into consumer preferences, sentiment, and purchasing behavior, which can guide product development and marketing strategies effectively.
[0063] The techniques described in this disclosure may be implemented, at least in part, in hardware, software, firmware, or any combination thereof. For example, various aspects of the described techniques may be implemented within one or more processors, including one or more microprocessors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or any other equivalent integrated or discrete logic circuitry, as well as any combinations of such components. The term "processor" or "processing subsystem" may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent circuitry. A control unit including hardware may also perform one or more of the techniques of this disclosure.
[0064] Such hardware, software, and firmware may be implemented within the same device or within separate devices to support the various techniques described in this disclosure. In addition, any of the described units, modules, or components may be implemented together or separately as discrete but interoperable logic devices. Depiction of different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be realized by separate hardware, firmware, or software components. Rather, functionality associated with one or more modules or units may be performed by separate hardware, firmware, or software components, or integrated within common or separate hardware, firmware, or software components.
[0065] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof.
[0066] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.
[0067] The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. , Claims:1. A system (100) for quantifying a lexical happiness level of a user comprising:
a processing subsystem (105) hosted on a server (110), wherein the processing subsystem (105) is configured to execute on a network (115) to control bidirectional communications among a plurality of modules comprising:
characterized in that,
a data collection module (120) configured to:
collect input from a panel of experts to iteratively define a plurality of happiness metrics;
collect physiological data and environmental data in real-time from a plurality of sensors, wherein the plurality of sensors is placed in a wearable device worn by the user to collect the physiological data and placed in one more location within an environment pertaining to the user to collect the environmental data; and
collect textual data from a plurality of user-generated content;
a processing module (125) operatively coupled to the data collection module (120), wherein the processing module (125) is configured to refine the plurality of happiness metrics based on the input from the panel of experts by utilizing a Delphi method and one or more genetic optimization techniques;
a data analysis module (130) operatively coupled to the processing module (125), wherein the data analysis module (130) is configured to:
analyze the physiological data, environmental data, and textual data using a machine learning model to identify one or more patterns, trends, and correlations; and
assess the textual data to identify public sentiment and emotional well-being by the machine learning model;
a happiness index computation module (135) operatively coupled to the data analysis module (130), wherein the happiness index computation module (135) is configured to:
generate a dynamic happiness index based on the analysis and the plurality of happiness metrics refined; and
provide a real-time happiness score for individuals, groups, or a targeted population; and
a feedback module (140) operatively coupled to the happiness index computation module (135), wherein the feedback module (140) is configured to:
provide personalized recommendations and insights to a user based on the real-time happiness data to enhance emotional well-being; and
display a comprehensive happiness metric for government policymakers, employers, and healthcare providers, derived from the happiness index via a dashboard.
2. The system (100) as claimed in claim 1, wherein the physiological data comprises heart rate variability, skin conductance and respiration rate to monitor emotional well-being.

3. The system (100) as claimed in claim 1, wherein the environmental data comprises air quality, temperature, and noise levels, which contributes to the happiness index.

4. The system (100) as claimed in claim 1, wherein the plurality of user-generated content comprises a plurality of social media platforms and surveys.

5. The system (100) as claimed in claim 1, wherein the artificial intelligence model is trained on a dataset comprising historical physiological, environmental, and textual data from a plurality of users.

6. The system (100) as claimed in claim 1, wherein the dynamic happiness index indicates overall lexical happiness levels by combining data from the plurality of sensors, sentiment analysis, and expert feedback.

7. The system (100) as claimed in claim 1, wherein the real-time happiness score indicates the current emotional state of individuals, groups, or entire populations.

8. The system (100) as claimed in claim 1, wherein the dashboard enables businesses to obtain insights into consumer preferences, sentiment, and purchasing behavior, guiding product development and marketing strategies.

9. A method (300) for quantifying a lexical happiness level of a user comprising:
characterized in that,
collecting, by a data collection module, input from a panel of experts to iteratively define a plurality of happiness metrics; (305)
collecting, by the data collection module, physiological data and environmental data in real-time from a plurality of sensors, wherein the plurality of sensors is placed in a wearable device worn by the user to collect the physiological data and placed in one more location within an environment pertaining to the user to collect the environmental data; (310)
collecting, by the data collection module, textual data from a plurality of user-generated content; (315)
refining, by a processing module, the plurality of happiness metrics based on the input from the panel of experts by utilizing a Delphi method and one or more genetic optimization techniques; (320)
analyzing, by a data analysis module, the physiological data, environmental data, and textual data using a machine learning model to identify one or more patterns, trends, and correlations; (325)
assessing, by a data analysis module, the textual data to identify public sentiment and emotional well-being by the machine learning model; (330)
generating, by a happiness index computation module, a dynamic happiness index based on the analysis and the plurality of happiness metrics refined; (335)
providing, by the happiness index computation module, a real-time happiness score for individuals, groups, or a targeted population; (340)
providing, by a feedback module, personalized recommendations and insights to a user based on the real-time happiness data to enhance emotional well-being; (345) and
displaying, by the feedback module, a comprehensive happiness metric for government policymakers, employers, and healthcare providers, derived from the happiness index via a dashboard. (350)
Dated this 13th day of November 2024 Signature

Prakriti Bhattacharya
Patent Agent (IN/PA-5178)
Agent for the Applicant

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