Method and system for analyzing and predicting human decision-making patterns

Abstract

A method and system for analyzing and predicting human decision-making patterns is disclosed. A processor receives facial expressions of a subject while the subject interacts with displayed content. The displayed content is displayed on an interactive user interface. The captured facial expressions are processed using a computer vision algorithm to identify emotional states of the subject. Contextual data from the displayed content is extracted using a text categorization module to determine topics associated with the emotional states of the subject. The identified emotional states are correlated with the determined topics to generate an emotional response profile. The emotional response profile is analyzed using a machine learning model to identify patterns in decision-making behaviour of the subject. predictive insights regarding decision-making tendencies of the subject is generated based on the identified patterns. Further, the predictive insights are presented through the interactive user interface.

Description

METHOD AND SYSTEM FOR ANALYZING AND PREDICTING HUMANDECISION-MAKING PATTERNSDESCRIPTIONTechnical Field

[0001] This disclosure relates generally to the field of human behaviour analysis and, more particularly, to a method and system for analysing and predicting human decision-making patterns.BACKGROUND

[0002] In the digital era, understanding human behavior through emotional responses has become increasingly important across domains such as education, finance, healthcare, and virtual communication. Despite the critical influence emotions have on human decisionmaking, conventional digital systems lack the ability to effectively capture, interpret, or respond to emotional cues. This limitation leads to a disconnect between user behavior and system responsiveness, resulting in missed opportunities for personalization, reduced engagement, and less informed decision-making support.

[0003] Further, virtual communication has become an essential tool for personal, professional, and educational interactions. With the rise of remote work and online learning, video conferencing platforms. These platforms provide basic tools for audio and video communication, chat features, and virtual collaboration. However, despite their widespread use, they lack the capability to capture and analyse non-verbal cues and emotional responses effectively. Understanding emotions during virtual communication is critical for fostering engagement, improving collaboration, and ensuring that participants feel connected.

[0004] While video conferencing platforms allow participants to see and hear each other, they often fail to replicate the nuances of In-person interactions. Non-verbal cues such as facial expressions, tone of voice, and sentiment are either overlooked or difficult to interpret in a virtual setting. This creates challenges for presenters, educators, and leaders, who must rely on limited or subjective feedback to gauge participant engagement and emotional responses. Current technologies provide isolated solutions, such as facial expression recognition or speech analysis, bit do not integrate these capabilities into a cohesive system for real-time emotiondetection and sentiment analysis. As a result, virtual interactions often lack the depth and responsiveness necessary for meaningful communication.

[0005] Therefore, there is a need for a methodology to analyse and predict human decisionmaking patterns. Further, there is a need for a methodology of analysing human emotional responses in a virtual communication session.SUMMARY OF THE INVENTION

[0006] In an embodiment, a computer-implemented method for analyzing human decisionmaking patterns based on emotional responses is disclosed. The method may include receiving, by a processor and via a capturing device, facial expressions of a subject while the subject interacts with displayed content, wherein the displayed content is displayed on an interactive user interface. The method may further include processing, by the processor and using a computer vision algorithm, the captured facial expressions to identify emotional states of the subject. The method may further include extracting, by the processor and using a text categorization module, contextual data from the displayed content to determine topics associated with the emotional states of the subject. The method may further include correlating, by the processor, the identified emotional states with the determined topics to generate an emotional response profile. The method may further include analyzing, by the processor, the emotional response profile using a machine learning model to identify patterns in decisionmaking behaviour of the subject. The method may further include generating, by the processor, predictive insights regarding financial decision-making tendencies of the subject based on the identified patterns. The method may further include presenting, by the processor, the predictive insights through the interactive user interface.

[0007] In an embodiment, a system for analysing human decision-making patterns based on emotional responses is disclosed. The system may include a capturing device, a processor, and a memory communicatively coupled to the processor, wherein the memory stores processorexecutable instructions, which, on execution, cause the processor to receive, via the capturing device, facial expressions of a subject while the subject interacts with displayed content, wherein the displayed content is displayed on an interactive user interface. The processor may further process, using a computer vision algorithm, the captured facial expressions to identify emotional states of the subject. The processor may further extract, using a text categorization module, contextual data from the displayed content to determine topics associated with theemotional states of the subject. The processor may further correlate the identified emotional states with the determined topics to generate an emotional response profile. The processor may further analyse the stored emotional response profile using a machine learning model to identify patterns in decision-making behaviour of the subject. The processor may further analyse the stored emotional response profile using a machine learning model to identify patterns in decision-making behaviour of the subject. The processor may further present the predictive insights through the interactive user interface.

[0008] In an embodiment, a computer-implemented method for predicting decision-making patterns is disclosed. The method may include receiving, by a processor, demographic data and human thinking traits from structured input sources and unstructured input sources using a natural language processing technique. In an embodiment, the natural language processing technique analyses the structured input sources and the unstructured input sources to extract demographic attributes and cognitive traits. The method may further include the natural language processing technique analyses the structured input sources and the unstructured input sources to extract demographic attributes and cognitive traits. In an embodiment, the classification algorithm groups the demographic data into a corresponding demographic group selected from a plurality of predefined demographic groups based on learned patterns. In an embodiment, the demographic attributes comprising race, sex, and cultural background. The method may further include mapping, by the processor, the cognitive traits to the categorized demographic data by employing a correlation model. In an embodiment, the cognitive traits comprising drive, focus, reasoning, and self-sufficiency. In an embodiment, the correlation model establishes associations between the cognitive traits and the categorized demographic groups to generate a predictive profile. The method may further include generating, by the processor, decision-making simulations for one or more scenarios by applying machine learning-based trait analysis. In an embodiment, the machine learning-based trait analysis predicts behavioural patterns and potential responses based on the mapped cognitive traits and scenario-specific parameters. The method may further include generating, by the processor, predictive insights by evaluating the simulated decision-making scenarios using a statistical modelling technique. In an embodiment, the statistical modelling technique analyses historical data and real-time data to determine potential future outcomes. In an embodiment, the predictive insights comprising forecasted trends, behavioural predictions, and recommended actions. The method may further include presenting, by the processor, the predictive insights through an interactive user interface.

[0009] In an embodiment, a system for predicting decision-making patterns is disclosed. The method may include a processor, and a memory communicatively coupled to the processor, wherein the memory stores process-executable instructions, which, on execution, cause the processor to receive demographic data and human thinking traits from structured input sources and unstructured input sources using a natural language processing technique. In an embodiment, the natural language processing technique analyses the structured input sources and the unstructured input sources to extract demographic attributes and cognitive traits. The processor may further categorize the received demographic data by processing the extracted demographic attributes using a classification algorithm. In an embodiment, the classification algorithm groups the demographic data into a corresponding demographic group selected from a plurality of predefined demographic groups based on learned patterns. In an embodiment, the demographic attributes comprising race, sex, and cultural background. The processor may further map the cognitive traits to the categorized demographic data by employing a correlation model. In an embodiment, the cognitive traits comprising drive, focus, reasoning, and self- sufficiency. In an embodiment, the correlation model establishes associations between the cognitive traits and the categorized demographic groups to generate a predictive profile. The processor may further generate decision-making simulations for one or more scenarios by applying machine learning-based trait analysis. In an embodiment, the machine learning-based trait analysis predicts behavioural patterns and potential responses based on the mapped cognitive traits and scenario-specific parameters. The processor may further generate predictive insights by evaluating the simulated decision-making scenarios using a statistical modelling technique. In an embodiment, the statistical modelling technique analyses historical data and real-time data to determine potential future outcomes. In an embodiment, the predictive insights may include forecasted trends, behavioural predictions, and recommended actions. The processor may further present the predictive insights through an interactive user interface.

[0010] In an embodiment, a computer-implemented method for analyzing human emotional responses in a virtual communication session is disclosed. The method may include receiving, by a processor, a real-time video stream and an audio input of a subject captured via an image capturing device during the virtual communication session. The method may further include processing, by the processor, the real-time video stream using a computer vision model to detect facial expressions indicative of an emotional state of the subject from a plurality of predefined emotional states, wherein the emotional states comprise happiness, sadness, anger,and confusion. The method may further include analyzing, by the processor and using a trained machine learning model, the detected emotional state to generate real-time emotion indicators for presentation to one or more participants of the virtual communication session. The method may further include processing, by the processor, the audio input of the subject using an automatic speech recognition (ASR) module to generate textual data corresponding to the audio input. The method may further include analyzing, by the processor, the textual data using a natural language processing (NLP) module to determine sentiment characteristics of the audio input, wherein the NLP module comprises a transformer-based deep learning model to analyse linguistic patterns in the textual data to classify the sentiment characteristics. The method may further include correlating, by the processor, the detected emotional state with the sentiment characteristics to generate an emotion-sentiment profile associated with the subject. The method may further include generating, by the processor, real-time visual feedback based on the correlated emotion-sentiment profile. The method may further include determining, by the processor, one or more engagement insights to the subject based on the generated real-time visual feedback.

[0011] In an embodiment, a system for analyzing human emotional responses in a virtual communication session is disclosed. The system includes an image capturing device, a processor communicatively coupled to the image capturing device, and a memory communicatively coupled to the processor. The memory stores processor-executable instructions, which, on execution, cause the processor to receive a real-time video stream and an audio input of a subject captured via an image capturing device during the virtual communication session. The processor may further process the real-time video stream using a computer vision model to detect facial expressions indicative of an emotional state of the subject from a plurality of predefined emotional states, wherein the emotional states comprise happiness, sadness, anger, and confusion. The processor may further analyse, using a trained machine learning model, the detected emotional state to generate real-time emotion indicators for presentation to one or more participants of the virtual communication session. The processor may further process the audio input of the subject using an automatic speech recognition (ASR) module to generate textual data corresponding to the audio input. The processor may further analyse the textual data using a natural language processing (NLP) module to determine sentiment characteristics of the audio input, wherein the NLP module comprises a transformer-based deep learning model to analyse linguistic patterns in the textual data to classify the sentiment characteristics. The processor may further correlate the detectedemotional state with the sentiment characteristics to generate an emotion-sentiment profile associated with the subject. The processor may further generate real-time visual feedback based on the correlated emotion-sentiment profile. The processor may further generate real-time visual feedback based on the correlated emotion-sentiment profile.

[0012] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.BRIEF DESCRIPTION OF THE DRAWING

[0013] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles.

[0014] FIG. 1 is a block diagram of an exemplary system for analyzing human decisionmaking patterns based on emotional responses, in accordance with an embodiment of the present disclosure.

[0015] FIG. 2 is a functional block diagram of the computing device of the system of FIG. 1, in accordance with an embodiment of the present disclosure.

[0016] FIG. 3 illustrates a table depicting collected data, in accordance with an embodiment of the present disclosure.

[0017] FIG. 4 is a flow diagram of a methodology of analyzing human decision-making patterns based on emotional responses, in accordance with an embodiment of the present disclosure.

[0018] FIG. 5 is a block diagram of an exemplary computer system for implementing embodiments consistent with the FIG. 1, in accordance with an embodiment of the present disclosure.

[0019] FIG. 6 is a block diagram of an exemplary system for predicting decision-making patterns, in accordance with an embodiment of the present disclosure.

[0020] FIG. 7 is a functional block diagram of the computing device of the system of FIG. 6, in accordance with an embodiment of the present disclosure.

[0021] FIG. 8 illustrates a Graphical User Interface (GUI) for predicting deci si on -making patterns, in accordance with an embodiment of the present disclosure.

[0022] FIG. 9 is a flow diagram of a methodology of predicting decision-making patterns, in accordance with an embodiment of the present disclosure.

[0023] FIG. 10 is a block diagram of an exemplary computer system for implementing embodiments consistent with the FIG. 6, in accordance with an embodiment of the present disclosure.

[0024] FIG. 11 is a block diagram of an exemplary system for analyzing human emotional responses in a virtual communication session, in accordance with an embodiment of the present disclosure.

[0025] FIG. 12 is a functional block diagram of the computing device of the system of FIG. 11, in accordance with an embodiment of the present disclosure.

[0026] FIG. 13 is a flow diagram of a methodology of analyzing human emotional responses in a virtual communication session, in accordance with an embodiment of the present disclosure.

[0027] FIG. 14 is a block diagram of an exemplary computer system for implementing embodiments consistent with the FIG. 11, in accordance with an embodiment of the present disclosure.DETAILED DESCRIPTION OF THE DRAWINGS

[0028] Exemplary embodiments are described with reference to the accompanying drawings. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments. It is intended that the following detailed description be considered exemplary only, with the true scope being indicated by the following claims. Additional illustrative embodiments are listed.

[0029] Further, the phrases “in some embodiments”, “in accordance with some embodiments”, “in the embodiments shown”, “in other embodiments”, and the like mean a particular feature,structure, or characteristic following the phrase is included in at least one embodiment of the present disclosure and may be included in more than one embodiment. In addition, such phrases do not necessarily refer to the same embodiments or different embodiments. It is intended that the following detailed description be considered exemplary only, with the true scope being indicated by the following claims.

[0030] Referring now to FIG. 1, a block diagram of an exemplary system 100 for analyzing human decision-making patterns is illustrated in accordance with an embodiment of the present disclosure. The system 100 may include a computing device 102, an external device 112, a data server 114, and a capturing device 116, which are communicatively coupled to each other through a wired or wireless communication network 110. The computing device 102 may include a processor 104, a memory 106, and an input / output (I / O) device 108.

[0031] In an embodiment, examples of processor(s) 104 may include, but are not limited to, an Intel® Itanium® or Itanium 2 processor(s), or AMD® Opteron® or Athlon MP® processor(s), Motorola® lines of processors, Nvidia®, FortiSOC™, system on a chip processors or other future processors.

[0032] In an embodiment, the memory 106 may store instructions that, when executed by the processor 104, cause the processor 104 to analyse human decision-making patterns based on emotional responses, as will be discussed in greater detail herein below. In an embodiment, the memory 106 may be a non-volatile memory or a volatile memory. In an embodiment, the memory 106 may also store a single module or a combination of different modules to analyse human decision-making patterns based on emotional responses. Examples of non-volatile memory may include, but are not limited to, flash memory, a Read Only Memory (ROM), a Programmable ROM (PROM), Erasable PROM (EPROM), and electrically EPROM (EEPROM) memory. Further, examples of volatile memory may include but are not limited to Dynamic Random-Access Memory (DRAM) and Static Random-Access Memory (SRAM).

[0033] In an embodiment, the I / O device 108 may comprise a variety of interface(s), for example, interfaces for data input and output devices and the like. The I / O device 108 may facilitate the inputting of instructions by a user communicating with the computing device 102. In an embodiment, the I / O device 108 may be wirelessly connected to the computing device 102 through wireless network interfaces such as Bluetooth®, infrared, or any other wireless radio communication known in the art. In an embodiment, the I / O device 108 may be connectedto a communication pathway for one or more components of the computing device 102 to facilitate the transmission of inputted instructions and output results of data generated by various components such as, but not limited to, processor(s) 104 and memory 106.

[0034] In an embodiment, the data server 114 may be enabled in a remote cloud server or a colocated server and may include a secure database (not shown) to store any data necessary for the system 100 to analyse human decision-making patterns based on emotional responses. In an embodiment, the data server 114 may store data input by an external device 112 or output generated by the computing device 102. In an embodiment, the computing device 102 may be communicatively coupled with the data server 114 through the communication network 110.

[0035] In an embodiment, the capturing device 116 may be configured to capture visual data, including facial expressions and micro-expressions of a subject interacting with the computing device 102. The capturing device 116 may include, but is not limited to, cameras integrated within the computing device 102, external webcams, or specialized imaging devices capable of detecting subtle emotional cues and micro-expressions. The captured visual data may be transmitted to the processor 104 for processing and analysis using a computer vision algorithm. The capturing device 116 may support various imaging technologies, such as infrared imaging, RGB cameras, or depth-sensing cameras, to enhance the accuracy of emotion detection. In some embodiments, the capturing device 116 may include real-time tracking capabilities to continuously monitor and update the subject’s facial expressions during interaction with the displayed content on the interactive user interface.

[0036] In an embodiment, the communication network 110 may be a wired or a wireless network or a combination thereof. The communication network 110 can be implemented as one of the different types of networks, such as but not limited to ethemet IP network, intranet, local area network (LAN), wide area network (WAN), or a Metropolitan Area Network (MAN). Various devices in the system 100 may be configured to connect to the communication network 110, in accordance with various wired and wireless communication protocols. Examples of such wired and wireless communication protocols may include, but are not limited to, a Transmission Control Protocol and Internet Protocol (TCP / IP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), Zig Bee, EDGE, IEEE 802.11, light fidelity (Li-Fi), 802.16, IEEE 802.11s, IEEE 802.11g, multi-hop communication, wireless access point (AP), device to device communication, cellular communication protocols, and Bluetooth (BT) communication protocols. Further thecommunication network 110 can include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.

[0037] In an embodiment, the computing device 102 may receive a plurality of inputs from the external device 112 through the communication network 110. In an embodiment, the computing device 102 and the external device 112 may be a computing system including, but not limited to, a laptop computer, a desktop computer, a notebook, a workstation, a server, a portable computer, a handheld, or a mobile device. In an embodiment, the computing device 102 may be, but not limited to, in-built into the external device 112 or may be a standalone computing device.

[0038] In an embodiment, the computing device 102 may perform various processing in order to analyse human decision-making patterns based on emotional responses. By way of an example, the computing device 102 may receive, via the capturing device 116, facial expressions of a subject while the subject interacts with displayed content, wherein the displayed content may be displayed on an interactive user interface of the I / O device 108. In an exemplary embodiment, the subject displays expressions such as raised eyebrows indicating surprise, a furrowed brow suggesting concern, or a slight smile indicating satisfaction. Microexpressions, such as a subtle clenching of the jaw, may indicate stress or tension. In an exemplary embodiment, the interactive user interface displays various investment opportunities, such as low-risk bonds, high-risk stocks, and retirement plans. It may also provide interactive financial simulations where the subject can see potential gains and losses based on different financial decisions. If the subject furrows their brow while viewing high- risk stock options, the system processes this as concern and recommends safer investment alternatives, such as government bonds or mutual funds.

[0039] In an exemplary embodiment, the subject may exhibit facial cues such as a tilted head suggesting confusion, narrowed eyes indicating concentration, or a brief smile indicating understanding or satisfaction. The interactive user interface presents educational content, including text, graphics, videos, and quizzes on subjects such as physics, chemistry, and mathematics. The system 100 allows the subject to interact by answering questions or exploring interactive simulations. If the system 100 detects prolonged confusion through a furrowed brow or head tilt, it dynamically adjusts the lesson by offering simpler explanations or providing additional practice exercises.

[0040] The computing device 102, using a computer vision algorithm, may process the captured facial expressions to identify the emotional states of the subject. In order to process the captured facial expressions, the computing device 102 may detect micro-expressions indicative of specific emotional states by applying a feature extraction algorithm on the captured facial expressions. The computing device 102, in order to process the captured facial expressions, may further normalize the detected micro-expressions based on historical facial expression data stored in the person-specific database. The computing device 102, in order to process the captured facial expressions, may further assign a confidence score to the identified emotional states to improve accuracy in decision -making analysis.

[0041] The computing device 102, using a text categorization module, may extract contextual data from the displayed content to determine topics associated with the emotional states of the subject. The computing device 102 may further correlate the identified emotional states with the determined topics to generate an emotional response profile. The computing device 102 may further store the emotional response profile in a person-specific database (not shown). In an embodiment, the person-specific database may include demographic, socio-economic, and educational information of the subject. The computing device 102 may further rank the correlated emotional states and the determined topics based on an emotional impact score. The computing device 102, using an artificial intelligence model, may further determine an optimized order of propriety for addressing the needs of the subject based on the emotional impact score. The computing device 102 may further generate a decision-making model that prioritized ranked needs of the subject in alignment with cultural and socio-economic influences extracted from the person-specific database.

[0042] The computing device 102 may further segment the stored emotional data based on cultural, regional, and contextual factors. The computing device 102 may further retrieve the segmented data for comparison with aggregate emotional response patterns across a plurality of demographic groups. The computing device 102 may further update the person-specific database with new emotional response data to refine predictive accuracy over time.

[0043] The computing device 102 may analyse the emotional response profile using a machine learning model to identify patterns in the decision-making behaviour of the subject. The computing device 102 may generate predictive insights regarding a financial decision-making tendency of the subject based on the identified patterns. The computing device 102 may further present the predictive insights through the interactive user interface of the I / O device 108.

[0044] In an exemplary embodiment, a subject, Alex, a 35 -year-old professional, uses a computing device 102 to browse financial planning content displayed on an interactive user interface of the I / O device 108. The interactive interface presents various financial topics, such as retirement planning, budgeting strategies, and investment options. The content is tailored to Alex’s background, which includes socio-economic and educational information stored in a person-specific database. As Alex interacts with the displayed content, the image-capturing device 116, a high-resolution camera integrated with the computing device 102, continuously captures Alex’s facial expressions. These expressions reflect his emotional responses as he reads information regarding financial risks, savings rates, and recommended investment options. The computing device 102, using a computer vision algorithm, processes the captured facial expressions to identify Alex’s emotional states, such as anxiety, excitement, or confusion. To achieve this, the computing device 102 applies a feature extraction algorithm to detect micro-expressions brief, involuntary facial expressions indicative of specific emotions. For example, a slight upward movement of the corners of Alex’s mouth may indicate satisfaction, while a furrowed brow may suggest concern.

[0045] To improve the accuracy of emotion detection, the computing device 102 normalizes the detected micro-expressions by comparing them with Alex’s historical facial expression data stored in the person-specific database. This normalization accounts for Alex’s unique facial characteristics and eliminates any potential noise in the captured data. The computing device 102 further assigns confidence scores to the identified emotional states, indicating the likelihood of correctness for each detected emotion. Simultaneously, the computing device 102, using a text categorization module, extracts contextual data from the displayed financial content to determine topics associated with Alex’s emotional responses. For example, when Alex displays signs of anxiety, the system may determine that the topic “high-risk investments” triggered this emotional state. The computing device 102 then correlates the identified emotional states (e.g., anxiety, satisfaction) with the determined topics (e.g., high-risk investments, long-term savings) to generate an emotional response profile. This profile provides a detailed mapping of Alex’s emotional reactions to different financial topics, which is stored in the person-specific database.

[0046] Next, the computing device 102 ranks the correlated emotional states and topics based on an emotional impact score. For instance, if Alex’s anxiety score for high-risk investments is significantly higher than his satisfaction score for long-term savings, the system assigns ahigher emotional impact score to the anxiety-triggering topic. Using an artificial intelligence module, the computing device 102 determines an optimized order of priority for addressing Alex’s financial needs. The system prioritizes topics such as low-risk savings plans and gradual investment strategies, which align with Alex’s emotional comfort level and socio-economic background. The computing device 102 further generates a decision-making model that recommends specific financial actions tailored to Alex’s preferences. This model takes into account not only the emotional impact score but also cultural and socio-economic influences stored in the person-specific database. For example, if the database indicates that Alex comes from a low-risk investment-oriented culture, the system adjusts its recommendations to favour conservative financial options. The system segments the stored emotional response data based on factors such as region and demographic group, allowing comparisons with aggregate emotional response patterns from other users. For instance, Alex’ s emotional responses to high- risk investments are compared with the responses of other individuals within the same demographic group to refine predictions. The computing device 102 continuously updates the person-specific database with new emotional response data collected during subsequent interactions, thereby improving predictive accuracy over time.

[0047] Finally, the computing device 102 analyses the emotional response profile using a machine learning model to identify patterns in Alex’s financial decision-making behaviour. Based on these patterns, the system generates predictive insights about Alex’s likely financial decisions, such as whether he is prone to opt for low-risk investments or diversify into moderate-risk portfolios. These predictive insights are presented to Alex through the interactive user interface of the VO device 108. For instance, the interface may display personalized suggestions, such as “Consider a balanced mutual fund with moderate growth potential,” along with visual representations of Alex’s emotional responses and their impact on the recommended financial strategies.

[0048] Referring now to FIG. 2, a functional block diagram 200 of the computing device 102 of the exemplary system of FIG. 1, as illustrated, in accordance with an embodiment of the present disclosure. The computing device 102 may include a receiving module 202, a facial expression processing module 204, a contextual data extraction module 206, an emotional state correlating module 208, a ranking module 210, an optimized order determination module 212, a decision-making model generation module 214, a segmenting module 216, a retrieving module 218, a database updating module 220, an emotional response profile analysing module222, a predicting insights generation module 224, and a predicting insights presentation module226.

[0049] The receiving module 202 may receive, via the capturing device 116, facial expressions of a subject while the subject interacts with displayed content, wherein the displayed content may be displayed on an interactive user interface of the I / O device 108. In an embodiment, the receiving module 202 may be configured to receive real-time visual data representing facial expressions of a subj ect while the subj ect interacts with displayed content. The capturing device 116, which may include an integrated or external camera capable of high-definition video capture, continuously monitors and records the subject’s facial movements during the interaction. The captured video stream is transmitted to the receiving module 202 for further processing. The displayed content may be dynamic, interactive information presented on an interactive user interface of the I / O device 108, which can be a touchscreen, monitor, or other input / output device. The content can include text, images, videos, financial information, advertisements, or interactive decision-making prompts. The system may adjust the content in real time based on the subject’s detected emotional responses to optimize user engagement and decision-making support. The receiving module 202 processes the incoming visual data to identify and capture distinct facial features, such as the position and movement of the eyebrows, mouth, eyes, and forehead. In an exemplary use case, a student, Alex, uses an online educational platform designed to dynamically adapt lessons based on user emotions. While Alex studies a complex topic in physics displayed on the interactive interface of the I / O device 108, the capturing device 116 records his facial reactions. The receiving module 202 receives data showing a pattern of frequent furrowing of Alex’s eyebrows and pursing of his lips, indicative of confusion or frustration. The system immediately processes the captured data and triggers an adaptive response: it simplifies the content or presents additional examples to aid Alex’s comprehension.

[0050] Further, the facial expression processing module 204, using a computer vision algorithm, may process the captured facial expressions to identify emotional states of the subject. In order to process the captured facial expressions, the facial expression processing module 204, may detect micro-expressions indicative of specific emotional states by applying a feature extraction algorithm on the captured facial expressions. The facial expression processing module 204, in order to process the captured facial expressions, may further normalize the detected micro-expressions based on historical facial expression data stored inthe person-specific database. The facial expression processing module 204, in order to process the captured facial expressions, may further assign confidence score to the identified emotional states to improve accuracy in decision-making analysis. In an embodiment, the facial expression processing module 204 may be configured to process captured facial expressions of a subject interacting with displayed content through a combination of computer vision and machine learning techniques. The capturing device 116 records the subject’s facial expressions in real time, which are then transmitted to the facial expression processing module 204 for further analysis. The module applies a feature extraction algorithm to detect micro-expressions brief, involuntary facial movements that indicate specific emotional states such as anxiety, happiness, confusion, or frustration.

[0051] Examples of Facial Expressions and Their Processing:Micro-Expressions of Anxiety:• Feature Detection: The module detects subtle facial muscle movements, such as furrowed brows, tightened lips, and rapid blinking.• Normalization: The detected features are normalized against historical data, ensuring that minor lighting changes or temporary facial muscle twitches are excluded.• Confidence Scoring: The system assigns a high confidence score if the combination of features aligns closely with historical anxiety data.• Outcome: The module flags the subject’s emotional state as "anxiety," prompting the system to provide relevant supportive or alternative content.Micro-Expressions of Happiness:• Feature Detection: The module detects upward movements of the mouth corners (smiling) and eye squinting (Duchenne marker of genuine happiness).• Normalization: The module compares the detected expressions with previous occurrences of happiness stored in the person-specific database.• Confidence Scoring: A high confidence score is assigned if the detected expressions strongly correlate with known patterns of happiness.• Outcome: The subject’s emotional state is flagged as "happiness," prompting reinforcement of the current content or offering rewards for continued engagement.Micro-Expressions of Confusion:• Feature Detection: Detection of raised eyebrows, head tilting, and mouth movements (e.g., slight downward curl of lips).• Normalization: The module normalizes the detected features based on the subject’s unique baseline data.• Confidence Scoring: A moderate to high confidence score is assigned based on historical patterns of confusion for the same or similar subjects.• Outcome: The system may provide additional explanations or simplified content to address the subject’s confusion.

[0052] In accordance with the exemplary use case, Alex, a 35-y ear-old professional, is using a financial planning application on his laptop to explore investment options for his long-term financial goals. The interactive user interface of the application presents a variety of options, including mutual funds, high-risk stocks, retirement plans, and government bonds, with detailed descriptions and real-time projections of potential returns. As Alex navigates through the content, the capturing device 116, an integrated high-resolution camera, continuously records his facial expressions, capturing subtle changes in his reactions to the information displayed. Initially, as Alex reads about mutual funds and low-risk investment options, his facial expressions remain neutral or slightly positive, with occasional signs of interest indicated by a slight upward movement of his lips. However, when he navigates to the section discussing high-risk stocks, the capturing device detects a furrowed brow, slight lip tightening, and rapid eye movement. The facial expression processing module 204 processes these captured facial expressions and identifies them as indicative of anxiety and concern. To ensure accuracy, the module applies a feature extraction algorithm to isolate key features, such as the tension around Alex’s eyes and mouth. The processing module 204 normalizes these detected microexpressions by comparing them with Alex’s historical emotional data stored in the personspecific database. The database contains prior instances of Alex’s reactions to risk-related topics, allowing the system to distinguish genuine anxiety from temporary or environmental factors. After normalization, the module assigns a high confidence score to the detected emotional state of anxiety, reflecting the system’s strong confidence that Alex is experiencing discomfort due to the perceived risks associated with the current investment option. Based on the high confidence score, the system triggers a personalized response. The displayed content on the interactive user interface dynamically adjusts to present safer alternatives, such as government bonds and low-risk mutual funds, along with explanatory content highlighting their stability and lower risk profiles. Additionally, the system provides a side-by-sidecomparison of potential gains and losses, allowing Alex to better understand the trade-offs involved in each investment option. As Alex reviews the alternative options, his facial expressions gradually shift, with the system detecting signs of reduced anxiety and increased engagement, such as relaxed facial muscles and a slight smile. The facial expression processing module continuously monitors these changes and updates the confidence scores accordingly. Once the system detects a stable emotional state, it reintroduces moderate-risk investment options, such as diversified mutual funds, to encourage balanced decision-making.

[0053] The contextual data extraction module 206, using a text categorization module, may extract contextual data from the displayed content to determine topics associated with the emotional states of the subject. In an embodiment, the contextual data extraction module 206 may be configured to analyse the displayed content on an interactive user interface and extract contextual information relevant to the subject’s emotional states. The extraction module relies on a text categorization module that processes the content using natural language processing (NLP) algorithms to identify and categorize key topics and subtopics embedded in the text, multimedia, or interactive elements. As the subject interacts with various displayed content, the module identifies the specific topics triggering emotional responses and correlates them with detected emotional states. For example, when the displayed content contains financial planning options, the text categorization module identifies terms related to risk, returns, investment categories, and economic forecasts. The contextual data extraction module then associates this information with the subject’s emotional state, such as anxiety when viewing high-risk stocks or satisfaction when viewing stable, low-risk investments. This contextual understanding is stored in an emotional response profile that serves as the basis for generating adaptive content recommendations and predictive insights.

[0054] In accordance with the exemplary use case, as Alex interacts with the financial planning application, the displayed content on the interactive user interface includes descriptions of various investment options, projections of financial returns, and risk assessment data. The contextual data extraction module 206, using the text categorization module, analyses the content to identify key topics. For instance, when Alex views information about high-risk stocks, the module extracts contextual keywords such as "market fluctuations," "potential high returns," and "increased volatility," categorizing the topic as "high-risk investments." Simultaneously, the facial expression processing module 204 detects signs of anxiety through Alex’s furrowed brow and tightened lips, assigning a high confidence score to this emotionalstate. The contextual data extraction module correlates Alex’s anxiety with the topic of high- risk investments, identifying this as a potential source of emotional discomfort. This correlation is stored in Alex’s emotional response profile within the person-specific database, which tracks his emotional responses to different financial topics over time. As Alex navigates to the section discussing mutual funds, the contextual data extraction module identifies terms like "diversification," "moderate returns," and "long-term stability," categorizing the topic as "low to moderate-risk investments." The facial expression processing module observes a reduction in Alex’s anxiety, detecting a slight smile that indicates satisfaction.

[0055] The emotional state correlating module 208 may further correlate the identified emotional states with the determined topics to generate an emotional response profile. The emotional state correlating module 208 may further store the emotional response profile in a person-specific database (not shown). In an embodiment, the person-specific database may include demographic, socio-economic, and educational information of the subject. In an embodiment, the emotional state correlating module 208 may be configured to receive input from the facial expression processing module 204 and the contextual data extraction module 206 to correlate the subject’s identified emotional states with the topics determined from the displayed content. The emotional state correlating module 208 analyses the strength of the relationship between each emotional state and its associated topic by considering confidence scores, emotional impact scores, and the relevance of contextual keywords. Once the correlation is established, the module generates an emotional response profile, which maps emotional reactions to specific topics of interest. This profile reflects the subject’s historical and real-time emotional responses, serving as a critical input for future predictive analysis and personalized recommendations. The emotional response profile is stored in a person-specific database, which may include additional information about the subject, such as demographic, socio-economic, and educational details. This enriched profile allows the system to contextualize emotional responses within broader personal and cultural backgrounds, enhancing its decision-making accuracy.

[0056] As Alex interacts with the financial planning application, the emotional state correlating module 208 continuously receives inputs from the facial expression processing module 204 and the contextual data extraction module 206. When Alex displays signs of anxiety while viewing high-risk stock options, the facial expression processing module assigns a high confidence score to this emotional state, and the contextual data extraction module categorizesthe topic as "high-risk investments." The emotional state correlating module correlates Alex’s detected anxiety with the topic of high-risk investments and assigns an emotional impact score based on the intensity of the detected emotion and the relevance of the topic. Given that Alex’s historical emotional response profile includes similar reactions to high-risk topics, the module reinforces this correlation and updates the emotional response profile accordingly. The profile entry for high-risk investments now reflects Alex’s persistent emotional discomfort, with the emotional impact score further increasing due to the consistent detection of anxiety. The emotional response profile also includes additional information from the person-specific database, such as Alex’s socio-economic background, which indicates that he is a moderaterisk investor. This additional context ensures that the system takes into account not only Alex’s immediate emotional reactions but also his long-term financial goals and comfort level with different investment strategies. When Alex later reviews mutual funds and low-risk options, the system detects a more positive emotional state, with confidence scores indicating satisfaction and interest. The emotional state correlating module links these emotional states with topics related to "long-term stability" and "moderate returns," updating the emotional response profile with a new correlation that reinforces Alex’s preference for safer investment strategies. The emotional response profile is stored in the person-specific database, which allows the system to retrieve it for future interactions. Over time, as Alex interacts with different financial topics, the profile evolves, continuously refining its understanding of his decision-making patterns and emotional responses. This dynamic adaptation ensures that future recommendations are more precisely tailored to his preferences. For instance, if Alex’s anxiety regarding high-risk investments decreases after being presented with educational content on risk management, the system may gradually reintroduce moderate-risk options to diversify his portfolio.

[0057] The ranking module 210 may further rank the correlated emotional states and the determined topics based on an emotional impact score. In an embodiment, the ranking module 210 may be configured to rank the correlated emotional states and their associated topics based on an emotional impact score, which reflects the intensity and significance of the subject’s emotional response. The emotional impact score may be computed using a combination of factors, such as confidence scores assigned to the detected emotional states, the strength of the correlation between the emotional state and the topic, and the subject’s historical emotional response profile stored in the person-specific database. The ranking module assigns higher priority to topics that evoke stronger emotional responses (e.g., anxiety or excitement with highemotional impact scores) and lower priority to topics with neutral or low-impact emotions. This ranking enables the system to prioritize content and recommendations that are most relevant to the subject’s emotional and cognitive state, ensuring personalized and adaptive experiences.

[0058] In accordance with the exemplary use case, as Alex interacts with the financial planning application, the emotional state correlating module 208 continues to link his detected emotional states with corresponding financial topics, updating his emotional response profile. For example, when Alex exhibits anxiety while viewing high-risk investment options, this emotional state is strongly correlated with the topic of "high-risk investments" and assigned a high confidence score by the facial expression processing module. The emotional state correlating module then sends this information to the ranking module 210. The ranking module calculates an emotional impact score based on the intensity of Alex’s emotional response, the consistency of his historical reactions to high-risk investments, and the relevance of the displayed content. Given that Alex has repeatedly shown signs of anxiety when exposed to high-risk options, the emotional impact score for this topic is assigned a value of 9 out of 10, indicating a significant emotional response that warrants prioritization.

[0059] Simultaneously, when Alex views information on mutual funds and low-risk investments, his emotional state shifts to satisfaction, with a high confidence score indicating positive engagement. The topic of "low-risk investments" is assigned an emotional impact score of 8 out of 10 due to its consistent correlation with positive emotional states and its alignment with Alex’s historical preference for safer financial options. The ranking module prioritizes the topics as High-risk investments: High priority due to high emotional impact score, requiring limited or controlled exposure to minimize Alex’s discomfort, Low-risk investments: High priority due to positive emotional impact and alignment with Alex’s overall investment strategy, Moderate-risk options: Medium priority, as Alex’s emotional response to these options is neutral or mildly positive.

[0060] Based on this ranking, the system dynamically adjusts the displayed content on the interactive user interface. For instance, it highlights mutual funds and government bonds while minimizing the visibility of high-risk stocks. If necessary, the system provides educational content on risk management to help Alex gradually overcome his anxiety and improve his understanding of higher-risk investments. As Alex continues to interact with the application, the ranking module updates the prioritization of topics in real time, ensuring that future recommendations are tailored to his evolving emotional responses. For example, if Alex beginsto show reduced anxiety and increased interest in high-risk investments after being exposed to risk-mitigation strategies, the system may adjust the ranking to gradually introduce more high- risk options into his portfolio.

[0061] The optimized order determination module 212, using an artificial intelligence model, may further determine an optimized order of propriety for addressing the needs of the subject based on the emotional impact score. In an embodiment, the optimized order determination module 212 may be configured to receive ranked topics and associated emotional impact scores from the ranking module 210 and determine an optimized order of priority for addressing the subject’s needs. The module leverages an artificial intelligence model, such as a machine learning model trained on historical emotional responses, behavioural data, and decisionmaking patterns to dynamically assess the subject’s current state and long-term goals. The Al model takes into account the subject’s demographic, socio-economic, and cultural information stored in the person-specific database to refine the prioritization process. The segmenting module 216 may further segment the stored emotional data based on cultural, regional, and contextual factors.

[0062] In accordance with the exemplary use case, as Alex interacts with the financial planning application, the ranking module 210 provides a list of topics and their associated emotional impact scores to the optimized order determination module 212. The list includes High-risk investments with an emotional impact score of 9 / 10 due to anxiety, Low-risk investments with an emotional impact score of 8 / 10 due to satisfaction, and Retirement planning with an emotional impact score of 6 / 10 due to a neutral response. The optimized order determination module 212 applies an Al model to determine the best sequence for presenting content and recommendations. The Al model considers Alex’s socio-economic background, which indicates a preference for low to moderate-risk investments, and his historical emotional responses, which show consistent anxiety when viewing high-risk options. The optimized order of priority is as follows: Low-risk investments: These are prioritized first to reinforce Alex’s positive emotional response and build confidence in the decision-making process, Retirement planning: This is addressed next as it has moderate relevance and can complement low-risk investments by focusing on long-term stability, High-risk investments: These are ranked last, with educational content on risk management provided to help Alex gradually overcome his anxiety. As Alex progresses through the prioritized recommendations, the system dynamically updates the order if his emotional responses change. For example, if Alex shows reducedanxiety after viewing educational content on diversification, the system may promote moderate-risk investments to a higher priority in the future.

[0063] The decision-making model generation module 214 may further generate a decisionmaking model that prioritized ranked needs of the subject in alignment with cultural and socioeconomic influences extracted from the person-specific database. In an embodiment, the decision-making model generation module 214 may be configured to generate a personalized decision-making model for the subject by prioritizing the subject’s ranked needs, as determined by the optimized order determination module 212. The decision-making model integrates emotional responses, ranked topics, and contextual data with cultural and socio-economic influences extracted from the person-specific database. The module utilizes an Al-based framework to align recommendations or tasks with the subject’s unique background, ensuring that decisions are culturally appropriate and aligned with long-term objectives. The personspecific database stores critical information, such as the subject’s age, income, education level, risk tolerance, and cultural background. For example, individuals from a low-risk investment culture may have decision-making models that prioritize safer financial strategies, while individuals from higher-risk backgrounds may receive recommendations that incorporate more aggressive investment options. The generated decision-making model dynamically adapts to the subject’s evolving emotional responses and historical preferences, ensuring continuous optimization of outcomes.

[0064] In accordance with the exemplary use case, as Alex interacts with the financial planning application, the optimized order determination module 212 provides a prioritized list of topics based on emotional impact scores and ranked needs. The decision-making model generation module 214 receives this input and accesses Alex’s socio-economic and cultural data from the person-specific database. The database reveals that Alex is a middle-income professional with a conservative investment background, indicating a preference for low to moderate-risk financial strategies. Based on this information, the module generates a decision-making model that prioritizes the following recommendations: Low-risk investments: The model suggests immediate investment in low-risk options such as government bonds and stable mutual funds, providing Alex with a sense of security and alignment with his emotional comfort, Retirement planning: The model includes long-term retirement savings plans as a secondary priority, ensuring that Alex builds a stable financial future, High-risk investments: Although ranked last,the model incorporates educational content on risk diversification and gradually introduces moderate-risk investments once Alex’s anxiety diminishes.

[0065] The decision-making model also tailors its recommendations to Alex’s cultural background. Since Alex’s socio-economic profile shows a preference for safe investments, the system provides conservative strategies with clear explanations of risk management. If Alex’s financial situation improves or if his emotional responses evolve, the system dynamically updates the decision-making model to reflect these changes. For example, if Alex demonstrates reduced anxiety after viewing educational content on portfolio diversification, the model may adjust its priorities, promoting moderate-risk investments as part of a diversified portfolio. The system continuously monitors Alex’s progress, ensuring that the recommendations remain relevant and personalized over time.

[0066] The retrieving module 218 may further retrieve the segmented data for comparison with aggregate emotional response patterns across a plurality of demographic groups. In an embodiment, the retrieving module 218 may be configured to retrieve segmented emotional response data from the subject's emotional response profile and compare it with aggregate emotional response patterns across a plurality of demographic groups stored in a centralized or distributed database. The segmented data may include emotional reactions categorized by context, such as financial topics, learning difficulties, or health-related concerns, and by specific demographic attributes like age, gender, income level, geographic location, and cultural background. The comparison performed by the retrieving module is critical to identifying behavioural trends and optimizing the decision-making model for the subject. By comparing the subject’s emotional responses to those of similar or diverse demographic groups, the module helps the system determine whether the subject’s responses align with general behavioural patterns or represent unique, individual reactions requiring personalized interventions.

[0067] In accordance with the exemplary use case, as Alex interacts with the financial planning application, the retrieving module 218 retrieves his segmented emotional response data from the person-specific database. This data includes Alex’s emotional responses categorized by topics, such as high-risk investments, mutual funds, and retirement planning. The segmented data indicates that Alex consistently experiences anxiety when reviewing high-risk investment options and shows satisfaction when exploring low-risk investments. The retrieving module compares this segmented data with aggregate emotional response patterns collected from alarge dataset of individuals within similar or different demographic groups. The comparison reveals that Alex’s emotional reactions are common among middle-income professionals with conservative financial backgrounds. The data also shows that individuals in Alex’s demographic group typically overcome their initial anxiety after engaging with risk education materials and progressively adopting moderate-risk investments. Based on this comparison, the system dynamically updates Alex’s decision-making model. It emphasizes low-risk and retirement planning options while introducing moderate-risk investments in a gradual manner. To further support Alex’s transition, the system provides examples of successful investment strategies used by others in similar demographic groups, helping to build Alex’s confidence in managing risk. The retrieving module continuously monitors Alex’s emotional responses and updates the comparisons as new data is collected. If Alex begins to display unique emotional responses that deviate from typical patterns, the system flags this behaviour and adjusts its recommendations accordingly. For example, if Alex continues to exhibit heightened anxiety despite educational interventions, the system may prioritize even safer investment options or suggest consulting a financial advisor for additional guidance.

[0068] The database updating module 220 may further update the person-specific database with new emotional response data to refine predictive accuracy over time. In an embodiment, the database updating module 220 may be configured to continuously or periodically update the person-specific database with new emotional response data collected during interactions between the subject and the system. The new data may include updated emotional states, topics of interest, confidence scores, and emotional impact scores generated during real-time interactions. The updating process ensures that the database reflects the subject’s evolving emotional and behavioural responses, enabling the system to improve its predictive accuracy over time.

[0069] In accordance with the exemplary use case, As Alex continues interacting with the financial planning application, the system collects new emotional response data during each session. For example, after viewing educational content on risk management, Alex shows reduced anxiety when revisiting high-risk investment options. The database updating module 220 records this change, linking the updated emotional state to the specific topic of risk diversification. The updating process assigns a higher weight to Alex’s recent interactions, reflecting his evolving comfort level with higher-risk investments. The module also integrates contextual updates from the person-specific database, such as Alex’s improved financialsituation or changes in his long-term investment goals. This holistic updating mechanism ensures that the database remains relevant and accurately reflects Alex’s current preferences. As the database is continuously updated, the system refines its predictive accuracy in real-time. For example, it can predict when Alex will be ready to explore more diversified investment options without experiencing anxiety. The updated database also helps the system recommend personalized content, such as case studies of successful investors with similar demographic backgrounds, further boosting Alex’s confidence. Over time, the database updating module ensures that the system’s predictions are based on both short-term interactions and long-term behavioural trends, allowing it to adapt seamlessly to Alex’s changing needs. If Alex’s emotional responses shift due to new financial challenges, the system dynamically updates its recommendations to reflect this change, providing ongoing support for his decision-making journey.

[0070] The emotional response profile analyzing module 222 may analyse the emotional response profile using a machine learning model to identify patterns in the decision-making behaviour of the subject. In an embodiment, the emotional response profile analyzing module 222 may be configured to analyse the subject’s emotional response profile using a machine learning model to identify patterns in the subject’s decision-making behaviour. The emotional response profile, which includes correlated emotional states, associated topics, and contextual data, is continuously updated and stored in the person-specific database. By leveraging historical and real-time data, the module can recognize recurrent emotional responses and their impact on the subject’s decision-making.

[0071] In accordance with the exemplary use case, as Alex interacts with the financial planning application, the emotional response profile analyzing module 222 continuously monitors his updated emotional response profile. The profile contains detailed records of Alex’s emotional reactions to various financial topics, such as high-risk investments, mutual funds, and retirement planning, along with associated confidence scores, emotional impact scores, and contextual data. Using a machine learning model, the module analyses patterns in Alex’s decision-making behaviour. The model identifies that Alex consistently exhibits anxiety when exposed to high-risk investment options, with the intensity of this response decreasing slightly after viewing educational content on risk diversification. In contrast, Alex consistently displays satisfaction and engagement when reviewing low-risk investment options and retirement plans. The analysis reveals that Alex’s anxiety toward high-risk investments is primarily triggered byunfamiliarity with market dynamics and volatility. The model predicts that Alex is more likely to consider higher-risk options if he is gradually exposed to them in combination with supportive educational content. Additionally, the system identifies that Alex tends to make better decisions when presented with structured, step-by-step plans rather than being given multiple options simultaneously.

[0072] The predicting insights generation module 224 may generate predictive insights regarding financial decision-making tendencies of the subject based on the identified patterns. In an embodiment, the predictive insights generation module 224 may be configured to generate predictive insights regarding the subject’s financial decision-making tendencies based on the patterns identified by the emotional response profile analyzing module 222. The module leverages insights derived from the subject’s emotional response profile, which includes historical emotional reactions, contextual data, and behavioural trends related to financial topics. The predictive insights may include likely future behaviours, such as the subject’s willingness to engage with higher-risk investments or their expected response to new financial opportunities. The module uses machine learning models and predictive algorithms to estimate how the subj ect’ s emotional responses will influence their decision-making in various financial contexts. These predictions can help the system present proactive recommendations, such as suggesting educational resources, modifying the complexity of investment options, or offering personalized financial plans.

[0073] In accordance with the exemplary embodiment, as Alex continues interacting with the financial planning application, the emotional response profile analyzing module 222 identifies several key patterns in his emotional responses and decision-making behaviour. The predictive insights generation module 224 analyses these patterns to generate actionable predictions regarding Alex’s future financial behaviour. For example, the system identifies that Alex consistently experiences anxiety when viewing high-risk investment options but exhibits reduced anxiety after engaging with educational content on risk mitigation. Based on this pattern, the module generates the following predictive insights: Alex is predicted to remain cautious toward high-risk investments in the short term but may gradually accept moderaterisk options if presented with clear explanations and incremental exposure, Over time, as Alex’s comfort level improves, he is expected to explore higher-risk investments as part of a diversified portfolio, Alex is more likely to engage with structured investment plans that provide long-term stability and predictable returns, such as retirement funds and conservativemutual funds. The system 100 uses these predictive insights to tailor its recommendations. Initially, it prioritizes low-risk investments to maintain Alex’s comfort level and builds his confidence through consistent positive experiences. The system gradually introduces moderate-risk options, accompanied by educational content explaining diversification benefits and risk management strategies. Once Alex demonstrates sustained comfort with moderaterisk investments, the system begins offering high-risk options with step-by-step guidance. Additionally, the module predicts that Alex’s decision-making tendencies may shift over time based on external factors, such as changes in his financial situation or exposure to new investment opportunities. As a result, the system continuously monitors Alex’s interactions and updates the predictive insights accordingly, ensuring that its recommendations remain relevant and effective.

[0074] The predicting insights presentation module 226 may further present the predictive insights through the interactive user interface of the I / O device 108. In an embodiment, the predicting insights presentation module 226 may be configured to present the predictive insights generated by the predictive insights generation module 224 through the interactive user interface of the I / O device 108. The module enables real-time visualization of insights, recommendations, and actionable suggestions to the subject in an intuitive, interactive format. The presentation can include charts, graphs, or personalized text messages, depending on the nature of the predictive insights and the subject’s preferences. The interactive user interface may be a touchscreen, desktop display, or mobile interface, allowing the subject to interact with and explore the presented insights. The module may further provide personalized explanations of the predictions, ensuring that the subject understands the underlying reasons behind each recommendation. The system may allow the subject to view potential outcomes for different decisions, enabling what-if analysis to explore various scenarios.

[0075] In accordance with the exemplary use case, after generating predictive insights regarding Alex’s financial decision-making tendencies, the predicting insights presentation module 226 presents the insights through the interactive user interface of the I / O device 108. The interface displays a personalized dashboard containing: The dashboard presents a recommended investment portfolio that includes low-risk options (e.g., government bonds and mutual funds) and moderate-risk options (e.g., diversified funds). The allocation is visually represented through pie charts and bar graphs, showing how the proportions will shift as Alex gains confidence. The system provides a step-by-step investment plan tailored to Alex’ s currentcomfort level and future financial goals. For example, the initial recommendation emphasizes low-risk investments, with subsequent steps introducing moderate-risk options supported by educational content. The interface allows Alex to explore different scenarios by adjusting variables such as investment amounts, time horizons, and market conditions. The system displays the projected outcomes of each scenario, helping Alex evaluate potential risks and rewards. The system provides personalized messages explaining why certain recommendations are made based on Alex’s emotional responses and decision-making patterns. For example, a message may read: “Based on your previous interactions and emotional responses, we recommend starting with low-risk investments to build confidence. As you gain more experience, we will gradually introduce moderate-risk options to help you diversify your portfolio.” The module continuously monitors Alex’s responses while interacting with the recommendations. If signs of confusion or anxiety are detected, the system provides additional explanations or simplifies the presentation to ensure that Alex remains engaged and comfortable. Over time, as new emotional response data is collected, the predictive insights and their presentation are updated dynamically, ensuring that Alex always receives the most relevant and effective recommendations.

[0076] In an exemplary scenario, Alex, a 35-year-old middle-income professional, wants to plan for his long-term financial goals and decides to use a financial planning application powered by the system described in the invention. The application, displayed on his laptop through the interactive user interface of the I / O device 108, is designed to adapt its recommendations based on Alex’s emotional responses to various financial topics. As Alex navigates the application, he views different investment options, including mutual funds, high- risk stocks, and retirement plans.

[0077] As Alex interacts with the displayed content, the capturing device 116 records his realtime facial expressions, such as a furrowed brow and tightened lips, when he views high-risk investment options. The receiving module 202 collects the visual data and transmits it to the facial expression processing module 204, which processes the captured data using a computer vision algorithm to identify micro-expressions indicative of emotional states. The module detects signs of anxiety and normalizes these detected features against Alex’s historical emotional response data stored in the person-specific database. A high confidence score is assigned to the detected anxiety, ensuring the accuracy of the emotional state detection. Simultaneously, the contextual data extraction module 206, using a text categorization module,analyses the displayed content to identify key contextual data related to financial topics such as “market volatility,” “potential high returns,” and “portfolio diversification.” The module determines that the topic associated with Alex’s anxiety is high-risk investments. The emotional state correlating module 208 then correlates Alex’s detected anxiety with this topic and creates an emotional response profile entry. This entry is stored in the person-specific database, which also contains additional information about Alex, including his socio-economic background and financial preferences.

[0078] The ranking module 210 assigns an emotional impact score to Alex’s correlated emotional states and topics. Since Alex’s anxiety toward high-risk investments has been consistently detected across multiple interactions, the topic is assigned a high emotional impact score of 9 / 10. Conversely, topics related to low-risk investments, which elicit positive emotional responses, receive an emotional impact score of 8 / 10. Based on these scores, the optimized order determination module 212, using an Al-based prioritization model, determines an optimized order for addressing Alex’s needs. The system prioritizes low-risk investments first, followed by moderate-risk options with accompanying educational content. High-risk investments are ranked last, to be introduced gradually once Alex gains more confidence. Next, the decision-making model generation module 214 generates a personalized decision-making model for Alex, incorporating the ranked needs, emotional responses, and his socio-economic background. The model emphasizes stable, low-risk investment options to maintain Alex’s emotional comfort while gradually introducing moderate-risk investments as part of a diversified strategy. To ensure that the model remains effective, the retrieving module 218 retrieves Alex’s segmented emotional response data and compares it with aggregate emotional response patterns from other middle-income professionals. The comparison reveals that individuals in Alex’s demographic group often experience similar anxiety toward high-risk investments but show improved confidence after engaging with risk-mitigation strategies.

[0079] The database updating module 220 continuously updates Alex’s emotional response profile with new data collected during the current session. This dynamic updating mechanism assigns higher weights to recent interactions, reflecting Alex’s evolving comfort level. Over time, the updated database allows the system to refine its predictions and recommendations more accurately. The emotional response profile analyzing module 222 then uses a machine learning model to identify key patterns in Alex’s decision-making behaviour. The analysis reveals that Alex’s anxiety decreases significantly after viewing educational content, and heconsistently shows satisfaction when engaging with structured, low-risk options. Based on these patterns, the predictive insights generation module 224 generates actionable predictive insights regarding Alex’s financial decision-making tendencies. The module predicts that Alex will remain cautious about high-risk investments in the short term but will be ready to explore moderate-risk options with sufficient educational support. Overtime, as Alex gains confidence, he is expected to gradually accept a diversified portfolio that includes a mix of low- and moderate-risk investments. Finally, the predicting insights presentation module 226 presents these predictive insights through the interactive user interface of the I / O device 108. The interface displays a personalized dashboard containing a recommended portfolio allocation, step-by-step investment guidance, and interactive “what-if’ scenarios that allow Alex to explore potential outcomes of different investment decisions. Personalized messages explain the rationale behind the recommendations, such as, “Based on your previous interactions and emotional responses, we recommend starting with low-risk investments. As you build confidence, we will gradually introduce moderate-risk options to help you diversify your portfolio.”

[0080] It should be noted that all such aforementioned modules, 202-226, may be represented as a single module or a combination of different modules. Further, as will be appreciated by those skilled in the art, each of the modules 202-226 may reside, in whole or in parts, on one device or multiple devices in communication with each other. In some embodiments, each of the modules 202-226 may be implemented as a dedicated hardware circuit comprising custom application-specific integrated circuit (ASIC) or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. Each of the modules 202-226 may also be implemented in a programmable hardware device such as a field programmable gate array (FGPA), programmable array logic, programmable logic device, and so forth. Alternatively, each of the modules 202-226 may be implemented in software for execution by various types of processors (e.g. processor 104). An identified module of executable code may, for instance, include one or more physical or logical blocks of computer instructions, which may, for instance, be organized as an object, procedure, function, or other construct. Nevertheless, the executables of an identified module or component need not be physically located together but may include disparate instructions stored in different locations which, when joined logically together, include the module and achieve the stated purpose of the module. Indeed, a module of executable code could be a single instruction, or many instructions, and may even bedistributed over several different code segments, among different applications, and across several memory devices.

[0081] As will be appreciated by one skilled in the art, a variety of processes may be employed for analyzing human decision-making patterns. For example, the exemplary system 100 and the associated computing device 102 may analyse human decision-making patterns by the processes discussed herein. In particular, as will be appreciated by those of ordinary skill in the art, control logic and / or automated routines for performing the techniques and steps described herein may be implemented by the system 100 and the associated computing device 102 either by hardware, software, or combinations of hardware and software. For example, suitable code may be accessed and executed by the one or more processors on the system 100 to perform some or all of the techniques described herein. Similarly, application-specific integrated circuits (ASICs) configured to perform some or all of the processes described herein may be included in the one or more processors on the system 100.

[0082] Referring now to FIG. 3, a table 300 depicting collected data, is illustrated, in accordance with an embodiment of the present disclosure. The table 300 is structured to demonstrate how emotional responses, text content, and contextual data are collected and analyzed to generate insights regarding a subject’s behavioural tendencies and decision-making patterns. The columns of the table represent key data points captured by the system 100, and the rows provide sample entries for different scenarios recorded over time.

[0083] The first column, labelled "Face ID," represents a unique identifier (5487596524) associated with the subject whose emotional responses are being recorded. This identifier allows the system to link the recorded data with the corresponding subject's emotional response profile stored in the person-specific database. The second column, labelled "Emotions," records the emotional states detected by the system. The recorded emotions include positive, neutral, and negative responses such as "Happy," "Sad," "Neutral," "Angry," and "Smiling." These emotions are detected through the analysis of facial expressions using the system’s facial expression processing module. The third column, labelled "Text Content Category," categorizes the content that the subject is interacting with, such as "Sports" or "Politics." This categorization allows the system to group and analyse emotional responses based on specific topics or areas of interest. The fourth column, labelled "Text Content Emotions," identifies the emotional context or tone of the content itself. For example, sports-related content may convey emotions such as "Team A winning" or "Team A losing," while political content may expressemotions related to leadership or global events. The fifth column, labelled "Text Content Pattern," records the specific context or topic within the broader category. For instance, football-related patterns, such as "Team A winning" or "Team C losing," are captured under the sports category, while political topics include statements like "Start a War in Europe" or "Abortion rights ended." The sixth column, labelled "Correlation of Emotion and Text Content," records the relationship between the subject’s detected emotional state and the emotional context of the displayed content. Correlations are classified as "Concordance," indicating alignment between the subject’s emotion and the content’s tone, or "Discordance," indicating a mismatch between the two. For example, if a subject feels happy when viewing content about their favoured team winning, it is classified as "Concordance," whereas if the subject feels happy when their opponent team loses, it is classified as "Discordance."

[0084] The seventh column, labelled "Conclusion," captures the insights derived from the subject’s emotional responses and their interaction with the content. For example, the system concludes that the subject is a "Team A supporter" based on their emotional reactions to Team A’s performance, or that they "do not like war in Europe" based on negative responses to political content. The eighth column, labelled "Time and Date," records the date and time when the emotional response data was captured, providing temporal context for the analysis. This allows the system to track the evolution of the subject’s emotional responses over time. As an example, row 1 records a happy emotional state when the subject interacts with sports content related to Team A winning a football match. The correlation between the happy emotion and the positive outcome for Team A is classified as "Concordance," and the system concludes that the subject is a "Team A supporter." Similarly, in row 5, when the subject reacts angrily to a political leader discussing a war in Europe, the system classifies the response as "Concordance" and concludes that the subject does not favour conflicts in Europe.

[0085] Referring now to FIG. 4, a flow diagram 400 of a methodology of analyzing human decision-making patterns, is illustrated, in accordance with an embodiment of the present disclosure. FIG. 4 is explained in conjunction with FIGs. 1-2. In an embodiment, the flow diagram 400 may include a plurality of steps that may be performed by various modules of the computing device 102 so as to analyse human decision-making patterns.

[0086] At step 402, the computing device 102 may receive, via the capturing device 116, facial expressions of a subject while the subject interacts with displayed content, wherein the displayed content may be displayed on an interactive user interface of the I / O device 108.

[0087] Further at step 404, the computing device 102, using a computer vision algorithm, may process the captured facial expressions to identify emotional states of the subject. In order to process the captured facial expressions, the computing device 102, may detect microexpressions indicative of specific emotional states by applying a feature extraction algorithm on the captured facial expressions. The computing device 102, in order to process the captured facial expressions, may further normalize the detected micro-expressions based on historical facial expression data stored in the person-specific database. The computing device 102, in order to process the captured facial expressions, may further assign confidence score to the identified emotional states to improve accuracy in decision-making analysis.

[0088] Further, at step 406, the computing device 102, using a text categorization module, may extract contextual data from the displayed content to determine topics associated with the emotional states of the subject. Further, at step 408, the computing device 102 may further correlate the identified emotional states with the determined topics to generate an emotional response profile. The computing device 102 may further store the emotional response profile in a person-specific database (not shown). In an embodiment, the person-specific database may include demographic, socio-economic, and educational information of the subject. The computing device 102 may further rank the correlated emotional states and the determined topics based on an emotional impact score. The computing device 102, using an artificial intelligence model, may further determine an optimized order of propriety for addressing needs of the subject based on the emotional impact score. The computing device 102 may further generate a decision-making model that prioritized ranked needs of the subject in alignment with cultural and socio-economic influences extracted from the person-specific database. The computing device 102 may further segment the stored emotional data based on cultural, regional, and contextual factors. The computing device 102 may further retrieve the segmented data for comparison with aggregate emotional response patterns across a plurality of demographic groups. The computing device 102 may further update the person-specific database with new emotional response data to refine predictive accuracy over time.

[0089] Further, at step 410, the computing device 102 may analyse the emotional response profile using a machine learning model to identify patterns in the decision -making behaviour of the subject. Further, at step 412, the computing device 102 may generate predictive insights regarding financial decision-making tendencies of the subject based on the identified patterns.Further at step 414, the computing device 102 may further present the predictive insights through the interactive user interface of the I / O device 108.

[0090] Referring now to FIG. 5, an exemplary computing system 500 for implementing embodiments consistent with the FIG. 1, is illustrated. The computing system 500 may be employed to implement processing functionality for various embodiments (e.g., as a SIMD device, client device, server device, one or more processors, and the like). Those skilled in the relevant art will also recognize how to implement the invention using other computer systems or architectures. The computing system 500 may represent, for example, a user device such as a desktop, a laptop, a mobile phone, a personal entertainment device, DVR, and so on, or any other type of special or general -purpose computing device as may be desirable or appropriate for a given application or environment. The computing system 500 may include one or more processors, such as a processor 502 that may be implemented using a general or special purpose processing engine such as, for example, a microprocessor, microcontroller or other control logic. In this example, the processor 502 is connected to a bus 504 or other communication medium. In some embodiments, the processor 502 may be an Artificial Intelligence (Al) processor, which may be implemented as a Tensor Processing Unit (TPU), or a graphical processor unit, or a custom programmable solution Field-Programmable Gate Array (FPGA).

[0091] The computing system 500 may also include a memory 506 (main memory), for example, Random Access Memory (RAM) or other dynamic memory, for storing information and instructions to be executed by the processor 502. The memory 506 may also be used for storing temporary variables or other intermediate information during the execution of instructions to be executed by the processor 502. The computing system 500 may likewise include a read-only memory (“ROM”) or other static storage device coupled to bus 504 for storing static information and instructions for the processor 502.

[0092] The computing system 500 may also include a storage device 508, which may include, for example, a media drive 510 and a removable storage interface. The media drive 510 may include a drive or other mechanism to support fixed or removable storage media, such as a hard disk drive, a floppy disk drive, a magnetic tape drive, an SD card port, a USB port, a micro- USB, an optical disk drive, a CD or DVD drive (R or RW), or other removable or fixed media drive. A storage media 512 may include, for example, a hard disk, magnetic tape, flash drive, or other fixed or removable medium that is read by and written to by the media drive 510. Asthese examples illustrate, the storage media 512 may include a computer-readable storage medium having stored there in particular computer software or data.

[0093] In alternative embodiments, the storage devices 508 may include other similar instrumentalities for allowing computer programs or other instructions or data to be loaded into the computing system 500. Such instrumentalities may include, for example, a removable storage unit 514 and a storage unit interface 516, such as a program cartridge and cartridge interface, a removable memory (for example, a flash memory or other removable memory module) and memory slot, and other removable storage units and interfaces that allow software and data to be transferred from the removable storage unit 514 to the computing system 500.

[0094] The computing system 500 may also include a communications interface 518. The communications interface 518 may be used to allow software and data to be transferred between the computing system 500 and external devices. Examples of the communications interface 518 may include a network interface (such as an Ethernet or other NIC card), a communications port (such as for example, a USB port, a micro-USB port), Near-Field Communication (NFC), etc. Software and data transferred via the communications interface 518 are in the form of signals which may be electronic, electromagnetic, optical, or other signals capable of being received by the communications interface 518. These signals are provided to the communications interface 518 via a channel 520. The channel 520 may carry signals and may be implemented using a wireless medium, wire or cable, fiber optics, or other communications medium. Some examples of the channel 520 may include a phone line, a cellular phone link, an RF link, a Bluetooth link, a network interface, a local or wide area network, and other communications channels.

[0095] The computing system 500 may further include Input / Output (I / O) devices 522. Examples may include, but are not limited to a display, keypad, microphone, audio speakers, vibrating motor, LED lights, etc. The I / O devices 522 may receive input from a user and also display an output of the computation performed by the processor 502. In this document, the terms “computer program product” and “computer-readable medium” may be used generally to refer to media such as, for example, the memory 506, the storage devices 508, the removable storage unit 514, or signal(s) on the channel 520. These and other forms of computer-readable media may be involved in providing one or more sequences of one or more instructions to the processor 502 for execution. Such instructions, generally referred to as “computer program code” (which may be grouped in the form of computer programs or other groupings), whenexecuted, enable the computing system 500 to perform features or functions of embodiments of the present invention.

[0096] In an embodiment where the elements are implemented using software, the software may be stored in a computer-readable medium and loaded into the computing system 500 using, for example, the removable storage unit 514, the media drive 510 or the communications interface 518. The control logic (in this example, software instructions or computer program code), when executed by the processor 502, causes the processor 502 to perform the functions of the invention as described herein.

[0097] Thus, the disclosed method 400 and system 100 overcome the challenges associated with conventional approaches to understanding and predicting human decision-making by offering a comprehensive and dynamic framework that integrates real-time emotional analysis, contextual understanding, and personalized recommendations. Traditional systems often lack the capability to adapt to a user’s changing emotional states and contextual interactions, resulting in static or generic recommendations that fail to meet individual preferences. In contrast, the disclosed system 100 and method 400 address these limitations by capturing, processing, and continuously updating emotional response data to refine predictive accuracy.

[0098] The system 100, through its various modules, including the facial expression processing module, contextual data extraction module, emotional state correlating module, and predictive insights generation module, ensures that emotional and contextual data are seamlessly correlated and analyzed in real time. The facial expressions of the subject, captured via the image-capturing device 116, are processed and linked to contextual text content to establish accurate correlations between emotions and topics. By using an emotional impact score and Al-driven prioritization, the system ranks and prioritizes topics based on their significance to the subject’s emotional and cognitive state. Furthermore, the challenges of static decisionmaking models are overcome by the system’s ability to retrieve and compare the subject’s emotional responses with aggregate patterns across multiple demographic groups, enabling the generation of personalized decision-making models. The system continuously updates the person-specific database, ensuring that recommendations evolve as the subject’s preferences and emotional responses change over time. The disclosed method 400 facilitates dynamic learning by utilizing a machine learning model to identify patterns in the subject’s decisionmaking behavior, allowing the system to anticipate future reactions and generate predictive insights. These insights are presented through an interactive user interface, offeringpersonalized explanations and interactive options for exploring different scenarios. By addressing real-time emotional feedback and long-term behavioural trends, the system provides targeted and adaptive recommendations that optimize the subject’s decision-making process. Overall, the disclosed system 100 and method 400 overcome the challenges associated with conventional systems by delivering personalized, real-time, and adaptive decision support. This innovative approach ensures that the subject receives emotionally aligned and contextually relevant recommendations across various domains, including financial planning, healthcare, education, and beyond, thereby enhancing user satisfaction and decision-making efficiency.

[0099] In light of the above-mentioned advantages and the technical advancements provided by the disclosed method and system, the claimed steps as discussed above are not routine, conventional, or well understood in the art, as the claimed steps enable the following solutions to the existing problems in conventional technologies. Further, the claimed steps bring an improvement in the functioning of the device itself as the claimed steps provide a technical solution to a technical problem.

[0100] The specification has described the method and system for analysing human decisionmaking patterns. The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for the purpose of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.

[0101] It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims.

[0102] Referring now to FIG. 6, a block diagram of an exemplary system 600 for predicting decision-making patterns, is illustrated, in accordance with an embodiment of the present disclosure. The system 600 may include a computing device 602, an external device 612, anda data server 614, which are communicatively coupled to each other through a wired or wireless communication network 610. The computing device 602 may include a processor 604, a memory 606 and an input / output (I / O) device 608.

[0103] In an embodiment, examples of processor(s) 604 may include, but are not limited to, an Intel® Itanium® or Itanium 2 processor(s), or AMD® Opteron® or Athlon MP® processor(s), Motorola® lines of processors, Nvidia®, FortiSOC™, system on a chip processors or other future processors.

[0104] In an embodiment, the memory 606 may store instructions that, when executed by the processor 604, cause the processor 604 to predict decision-making patterns, as will be discussed in greater detail herein below. In an embodiment, the memory 606 may be a non-volatile memory or a volatile memory. In an embodiment, the memory 606 may also store a single module or a combination of different modules to predict decision-making patterns. Examples of non-volatile memory may include, but are not limited to, a flash memory, a Read Only Memory (ROM), a Programmable ROM (PROM), Erasable PROM (EPROM), and Electrically EPROM (EEPROM) memory. Further, examples of volatile memory may include, but are not limited to, Dynamic Random-Access Memory (DRAM) and Static Random-Access Memory (SRAM).

[0105] In an embodiment, the I / O device 608 may comprise a variety of interface(s), for example, interfaces for data input and output devices and the like. The I / O device 608 may facilitate the inputting of instructions by a user communicating with the computing device 602. In an embodiment, the I / O device 608 may be wirelessly connected to the computing device 602 through wireless network interfaces such as Bluetooth®, infrared, or any other wireless radio communication known in the art. In an embodiment, the I / O device 608 may be connected to a communication pathway for one or more components of the computing device 602 to facilitate the transmission of inputted instructions and output results of data generated by various components such as, but not limited to, processor(s) 604 and memory 606.

[0106] In an embodiment, the data server 614 may be enabled in a remote cloud server or a colocated server and may include a secure database (not shown) to store any data necessary for the system 600 to predict decision-making patterns. In an embodiment, the data server 614 may store data input by an external device 612 or output generated by the computing device 602. Inan embodiment, the computing device 602 may be communicatively coupled with the data server 614 through the communication network 610.

[0107] In an embodiment, the communication network 610 may be a wired or a wireless network or a combination thereof. The communication network 610 can be implemented as one of the different types of networks, such as but not limited to, ethemet IP network, intranet, local area network (LAN), wide area network (WAN), or a Metropolitan Area Network (MAN). Various devices in the system 600 may be configured to connect to the communication network 610, in accordance with various wired and wireless communication protocols. Examples of such wired and wireless communication protocols may include, but are not limited to, a Transmission Control Protocol and Internet Protocol (TCP / IP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), Zig Bee, EDGE, IEEE 802.11, light fidelity (Li-Fi), 802.16, IEEE 802.11s, IEEE 802.11g, multi-hop communication, wireless access point (AP), device to device communication, cellular communication protocols, and Bluetooth (BT) communication protocols. Further, the communication network 610 can include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.

[0108] In an embodiment, the computing device 602 may receive a plurality of inputs from the external device 612 through the communication network 610. In an embodiment, the computing device 602 and the external device 612 may be a computing system, including but not limited to, a laptop computer, a desktop computer, a notebook, a workstation, a server, a portable computer, a handheld or a mobile device. In an embodiment, the computing device 602 may be, but not limited to, in-built into the external device 612 or may be a standalone computing device.

[0109] In an embodiment, the computing device 602 may perform various processing in order to predict decision-making patterns. By way of an example, the computing device 602 may receive demographic data and human thinking traits from structured input sources and unstructured input sources using a natural language processing technique. In an embodiment, the natural language processing technique may analyse the structured input sources and the unstructured input sources to extract demographic attributes and cognitive traits. In an embodiment, the natural language processing technique used for receiving the demographic data and the human thinking traits may include tokenization, named entity recognition, and sentiment analysis to extract the demographic attributes and the cognitive traits. The computingdevice 602 may receive a case scenario specifying conditions or events for prediction. In an embodiment, the case scenario may include parameters related to economic, business, or geopolitical contexts.

[0110] The computing device 602 may further categorize the received demographic data by processing the extracted demographic attributes using a classification algorithm. In an embodiment, the classification algorithm groups the demographic data into a corresponding demographic group selected from a plurality of predefined demographic groups based on learned patterns. In an embodiment, the demographic attributes may include race, sex, and cultural background. In an embodiment, the categorization of the received demographic data may be performed using a supervised machine learning model. In an embodiment, the categorization of the received demographic data may be performed using a supervised machine learning model. In an embodiment, the supervised machine learning model may be trained on labelled datasets that may include association of the demographic attributes and the cognitive traits.[OHl] The computing device 602 may further map the cognitive traits to the categorized demographic data by employing a correlation model. In an embodiment, the cognitive traits may include drive, focus, reasoning, and self-sufficiency. In an embodiment, the correlation model may establish associations between the cognitive traits and the categorized demographic groups to generate a predictive profile. The computing device 602 may further analyse the received case scenario using machine learning models and scenario-specific parameters to identify relevant cognitive traits and demographic data applicable to the case scenario. The computing device 602 may further generate decision-making simulations for one or more scenarios by applying machine learning-based trait analysis. In an embodiment, the machine learning-based trait analysis predicts behavioural patterns and potential responses based on the mapped cognitive traits and scenario-specific parameters. In an embodiment, the generation of decision-making simulations may be performed using a neural network. In an embodiment, the neural network may be trained on historical decision-making patterns of a plurality of demographic groups to identify behavioural trends and simulate decision-making outcomes.

[0112] The computing device 602 may further generate predictive insights by evaluating the simulated decision-making scenarios using a statistical modelling technique. In an embodiment, the statistical modelling technique analyses historical data and real-time data to determine potential future outcomes. In an embodiment, the predictive insights may includeforecasted trends, behavioural predictions, and recommended actions. In an embodiment, the predictive insights may be dynamically updated by integrating real-time behavioural data. In an embodiment, the real-time behavioural data may be collected from user interactions and external sources. The computing device 602 may further present the predictive insights through an interactive user interface of the I / O device 608.

[0113] In an exemplary scenario, a national transportation authority is tasked with predicting how different demographic groups will respond to the implementation of a new high-speed rail system in a metropolitan area. The system needs to forecast potential public reactions, identify areas of resistance, and recommend strategies to ensure the success of the project. The authority uses system 600, which includes computing device 602, to process relevant data and generate predictive insights. The computing device 602 receives demographic data and human thinking traits from various structured and unstructured sources. In an embodiment, structured sources may include, but are not limited to, Government census data, population registries, and transportation survey reports. In an embodiment, unstructured sources may include, but are not limited to, online forums, social media discussions, and news articles related to public transportation.

[0114] The natural language processing technique analyses these input sources to extract relevant demographic attributes (e.g., race, sex, and cultural background) and cognitive traits (e.g., drive, focus, reasoning, and self-sufficiency). The analysis uses techniques such as: Tokenization: Breaks down large textual data into smaller, meaningful units, Named Entity Recognition: Identifies key demographic terms, such as ethnicity, occupation, and geographic location, Sentiment Analysis: Determines public sentiment regarding transportation projects, identifying positive or negative reactions.

[0115] The computing device 602 receives a case scenario specifying the planned high-speed rail system implementation. The case scenario includes the following parameters: Economic Context: Projected costs, ticket prices, and potential economic benefits for the community, Geographic Context: The location of key stations and their proximity to residential and commercial areas, Political Context: Public and political support or opposition from different demographic groups. The computing device 602 processes the demographic data using a classification algorithm to assign individuals to corresponding demographic groups. For example, the algorithm may categorize individuals as “White Male,” “Hispanic Female,” or “East Asian Male” based on demographic attributes such as race, sex, and cultural background.The categorization process uses a supervised machine learning model trained on labelled datasets that associate demographic attributes with cognitive traits. These datasets include historical survey data and behavioural responses to previous public infrastructure projects. The computing device 602 maps cognitive traits to each categorized demographic group using a correlation model. For example, the “White Male” group may be mapped with traits such as assertiveness, risk-taking, and independence, and the “Hispanic Female” group may be mapped with traits such as empathy, patience, and sensitivity. The correlation model establishes associations between cognitive traits and demographic groups, generating a predictive profile for each group, detailing their likely responses to new infrastructure developments.

[0116] The computing device 602 further analyses the case scenario using machine learning models and scenario-specific parameters to identify which cognitive traits and demographic characteristics are most relevant to the project’s success. For example, the model may determine that individuals with high reasoning and patience traits are more likely to support long-term projects, even if initial disruptions occur. Groups characterized by high sensitivity may initially resist the project due to perceived environmental or financial risks.

[0117] The computing device 602 simulates how different demographic groups might react to various aspects of the high-speed rail project, such as changes in ticket pricing or environmental impact. The simulations use a neural network trained on historical decision-making patterns from previous infrastructure projects. For example: The computing device 602 may predict that a particular demographic group is likely to resist the project if ticket prices are perceived as unaffordable. Conversely, the computing device 602 groups with traits such as discipline and long-term reasoning may support the project due to expected future benefits.

[0118] The computing device 602 evaluates the decision-making simulations using a statistical modelling technique that integrates historical and real-time data. The predictive insights generated include Forecasted Trends: The likelihood of project acceptance or resistance across different demographic groups. Behavioural Predictions: Potential public reactions to specific changes, such as a temporary increase in construction noise. Recommended Actions: Strategies to address potential resistance, such as offering subsidies or community engagement initiatives. The predictive insights are dynamically updated as new real-time behavioural data is collected from online discussions, news updates, and community feedback.

[0119] The predictive insights are presented through an interactive user interface of the I / O device 608, which displays: Graphical representations of demographic group responses to key project variables, Behavioural trends over time, showing how public sentiment evolves as the project progresses, recommended strategies, such as tailoring public announcements to address specific concerns or highlighting long-term economic benefits. Based on the insights, the transportation authority decides to launch a community outreach program targeting demographic groups predicted to be resistant to the project. The program includes information sessions, financial aid packages for low-income communities, and environmentally friendly construction practices. These actions, informed by the predictive insights, help build public support and ensure the successful implementation of the high-speed rail system.

[0120] Referring now to FIG. 7, a functional block diagram 700 of the computing device 602 of the exemplary system of FIG. 6, is illustrated, in accordance with an embodiment of the present disclosure. The computing device 602 may include a receiving module 702, a data categorization module 704, a traits mapping module 706, a case scenario analysing module 708, a simulations generation module 710, an insights generation module 712, and a presentation module 714.

[0121] The receiving module 702 may receive demographic data and human thinking traits from structured input sources and unstructured input sources using a natural language processing technique. In an embodiment, the structured input sources may include, but are not limited to, government databases, enterprise databases, market research reports, financial records, social security or health records, etc. In an embodiment, the unstructured input sources may include, but are not limited to, social media posts, online product reviews, news articles and blogs, email communications, audio / video transcriptions, discussion forums, etc. In an embodiment, the natural language processing technique may analyse the structured input sources and the unstructured input sources to extract demographic attributes and cognitive traits. In an embodiment, the natural language processing technique used for receiving the demographic data and the human traits may include tokenization, named entity recognition, and sentiment analysis to extract the demographic attributes and the cognitive traits. The receiving module 702 may further receive a case scenario specifying conditions or events for prediction. In an embodiment, the case scenario may include parameters related to economic, business, or geopolitical contexts.

[0122] In an embodiment, the structured input sources may include databases or records that present information in an organized, predefined format. Examples of structured input sources may include, but are not limited to:Government Databases: Official databases containing census information, population statistics, or socioeconomic profiles that provide details such as age, race, gender, education, and occupation.Enterprise Databases: Customer relationship management (CRM) systems, human resource databases, and enterprise-level datasets that store structured information about individuals, including employment history, preferences, and geographic locations.Market Research Reports: Precompiled datasets containing consumer survey results, purchase patterns, or regional market preferences that are categorized based on demographic attributes.Financial Records: Structured data from banks, credit bureaus, or investment portfolios showing income, spending habits, credit scores, and financial stability, which can be linked to specific demographic groups.Social Security or Health Records: Government or institutional databases containing structured information about health, insurance, or benefits categorized by demographic factors such as gender, age, or ethnicity.

[0123] In an embodiment, the unstructured input sources may include textual or multimedia content that does not adhere to a predefined structure but contains valuable information about demographics and cognitive traits. Examples of unstructured input sources may include, but are not limited to:Social Media Posts: User-generated content from platforms such as Facebook, Twitter, and Instagram, where users’ express opinions, experiences, and preferences that can provide emotional and contextual insights into decision-making behaviour.Online Product Reviews: Consumer reviews on e-commerce platforms where users provide detailed feedback about their experiences, often revealing cognitive traits like satisfaction, disappointment, or loyalty.News Articles and Blogs: Online articles or blog posts discussing topics such as policy changes, economic conditions, or cultural events that can influence public opinion and behaviour.Email Communications: Text data from emails, surveys, or customer service interactions containing subjective feedback and information on user expectations or preferences.Audio / Video Transcriptions: Transcribed content from interviews, focus groups, or online discussions where individuals discuss preferences, opinions, and reactions.Discussion Forums: Text extracted from forums such as Reddit or Quora, where users participate in discussions that reflect collective opinions or personal experiences on a given topic.

[0124] In an embodiment, the natural language processing (NLP) technique analyses both structured and unstructured input sources to extract demographic attributes and cognitive traits relevant to decision-making predictions. The NLP technique processes large datasets to identify patterns, relationships, and contextual meaning within the data. The receiving module 702 may apply the following NLP methods to extract and process the input data:Tokenization: The NLP system divides unstructured text into individual words, phrases, or meaningful segments to facilitate further analysis. For example, tokenization breaks down sentences in product reviews to identify keywords related to user satisfaction or complaints.Named Entity Recognition (NER): The system identifies and classifies key entities within the text, such as names of locations, organizations, or demographic terms (e.g., age, race, or gender). For instance, NER can detect demographic references in news articles discussing public sentiment toward policy changes.Sentiment Analysis: The system analyses the emotional tone and sentiment of the content to classify user opinions as positive, negative, or neutral. For example, sentiment analysis can be applied to social media posts to determine public approval or resistance to specific products or policies.By combining these NLP techniques, the receiving module 702 extracts demographic attributes (such as age, gender, and cultural background) and cognitive traits (such as drive, focus, reasoning, and self-sufficiency) from diverse input sources, providing the foundation for accurate decision simulations.

[0125] In an embodiment, the receiving module 702 may also receive a case scenario specifying the conditions or events for prediction. In an embodiment, the case scenario includes parameters related to economic, business, or geopolitical contexts. For example, an economic case scenario may involve predicting consumer responses to a change in tax policy, while a geopolitical case scenario may involve forecasting public sentiment toward international trade agreements. The case scenario defines the contextual framework for processing the received demographic and cognitive data.

[0126] Further, the data categorization module 704 may categorize the received demographic data by processing the extracted demographic attributes using a classification algorithm. In an embodiment, the classification algorithm groups the demographic data into a corresponding demographic group selected from a plurality of predefined demographic groups based on learned patterns. In an embodiment, the demographic attributes may include race, sex, and cultural background. In an embodiment, the categorization of the received demographic data may be performed using a supervised machine learning model. In an embodiment, the categorization of the received demographic data may be performed using a supervised machine learning model. In an embodiment, the supervised machine learning model may be trained on labelled datasets that may include an association of the demographic attributes and the cognitive traits.

[0127] In an embodiment, the data categorization module 704 applies a classification algorithm to the demographic data. The algorithm processes demographic attributes, such as race, sex, and cultural background, to assign individuals to corresponding demographic groups. For example, the algorithm may categorize individuals as "White Male," "East Asian Female," or "Hispanic Male" based on the values of their demographic attributes. By grouping individuals with similar characteristics, the categorization step enables the system to identify patterns in behaviour and decision-making across demographic groups.

[0128] In an embodiment, the categorization of the demographic data is performed using a supervised machine learning model. The model is trained on labelled datasets that include associations between demographic attributes and cognitive traits. These datasets may consist of historical data collected from surveys, market studies, or behavioural observations, where each data point is labelled with its corresponding demographic classification. During the training phase, the machine learning model learns to recognize patterns and relationships between various demographic attributes and their associated cognitive traits. For example, themodel may learn that individuals from a particular cultural background tend to exhibit high levels of patience or risk aversion. Once trained, the model can accurately categorize new demographic data and assign individuals to their respective groups. In an embodiment, the demographic attributes processed by the data categorization module 704 may include, but are not limited to:Race: Categorized as White, Non-White, Asian, or other classifications as applicable.Sex: Male, Female, or other specified categories.Cultural Background: May include ethnicity, geographic origin, or religious affiliation, which influence the decision-making process.

[0129] In an embodiment, the supervised machine learning model is trained using a labelled dataset containing:Demographic Attributes: Such as race, sex, and cultural background.Cognitive Traits: Such as drive, focus, reasoning, and self-sufficiency.Behavioural Data: Collected from historical events, case studies, or decision-making scenarios.

[0130] For example, the labelled dataset may indicate that individuals from a particular demographic group tend to exhibit specific cognitive traits (e.g., individuals from a disciplined cultural background may exhibit higher patience and focus). The machine learning model uses this information to build an understanding of how demographic attributes are linked to cognitive traits, allowing it to generalize and accurately categorize new data.

[0131] In an exemplary use case, suppose a marketing agency aims to predict how different demographic groups will respond to a new advertising campaign. The agency provides demographic data, such as race, sex, and cultural background, for analysis. The data categorization module 704 processes the demographic attributes and categorizes individuals into groups such as “White Female,” “Asian Male,” and “Hispanic Female.” The supervised machine learning model, trained on past campaign responses, ensures accurate categorization. This enables the agency to generate predictive insights tailored to each group, optimizing the effectiveness of the advertising campaign.

[0132] Further, the traits mapping module 706 may map the cognitive traits to the categorized demographic data by employing a correlational model. In an embodiment, the cognitive traits may include drive, focus, reasoning, and self-sufficiency. In an embodiment, the correlation model may establish associations between the cognitive traits and the categorized demographic groups to generate a predictive profile.

[0133] In an embodiment, the traits mapping module 706 maps the following cognitive traits to the categorized demographic groups:Drive: Represents ambition, resilience, risk-taking, and determination.Focus: Includes traits like discipline, stubbornness, and the ability to avoid distractions.Reasoning: Captures logical thinking, emotional detachment, and decision-making based on facts and data.Self-Sufficiency: Encompasses independence and the capacity to operate without external validation or support.The correlation model establishes associations between these cognitive traits and the categorized demographic groups by analyzing historical and real-time data. For example, the model may associate individuals from a demographic group characterized by strong cultural discipline with high levels of focus and patience, while those from a more risk-tolerant group may exhibit high levels of drive and assertiveness.

[0134] In an embodiment, the correlation model generates a predictive profile for each categorized demographic group. The predictive profile includes:A list of cognitive traits that are dominant within the demographic group.Weightings or relevance scores that indicate the degree to which each trait influences decisionmaking.Context-specific adjustments based on scenario-specific parameters (e.g., increased risk tolerance during economic downturns).For example, individuals from a demographic group with high resilience and risk-taking may receive a predictive profile indicating that they are likely to adopt aggressive strategies in high-stakes business environments. Conversely, individuals from a group with strong emotional sensitivity may exhibit cautious or defensive behaviour in similar situations.

[0135] Further, the case scenario analysing module 708 may analyse the received case scenario using machine learning models and scenario-specific parameters to identify relevant cognitive traits and demographic data applicable to the case scenario.

[0136] In an embodiment, the case scenario analyzing module 708 processes the received case scenario using machine learning models and scenario-specific parameters. The case scenario may describe conditions related to economic, business, or geopolitical contexts, such as:An economic downturn requiring strategic investment decisions.A marketing campaign targeting specific customer segments.A geopolitical event that could impact supply chains or trade policies.

[0137] In an embodiment, the machine learning models analyse the scenario and identify the cognitive traits and demographic data most relevant to predicting behavioural outcomes. For example:In a scenario involving an economic downturn, the module may determine that traits related to patience and long-term focus are more relevant for predicting consumer spending patterns.In a marketing campaign scenario, the module may prioritize traits related to emotional sensitivity and social influence

[0138] In an embodiment, the machine learning models further refine the prediction by adjusting the weightings of the cognitive traits based on the context of the case scenario. For instance, in a scenario where individuals are faced with a high-risk investment decision, the system may increase the weight of the risk-taking trait in its predictive analysis, particularly for demographic groups that are historically inclined to take risks under similar conditions.

[0139] In an exemplary use case, a financial institution uses the system to predict how different demographic groups will respond to a new savings plan during an economic downturn. The traits mapping module 706 may associate traits like patience and long-term reasoning with specific demographic groups, while the case scenario analyzing module 708 evaluates the current economic context to adjust the relevance of these traits. As a result, the system generatespredictive profiles that enable the institution to tailor its marketing strategy to groups most likely to invest in the savings plan, maximizing engagement and participation.

[0140] Further, the simulations generation module 710 may generate decision-making simulations for one or more scenarios by applying machine learning-based trait analysis. In an embodiment, the machine learning-based trait analysis predicts behavioural patterns and potential responses based on the mapped cognitive traits and scenario-based trait analysis. In an embodiment, the machine learning-based trait analysis predicts behavioural patterns and potential responses based on the mapped cognitive traits and scenario-specific parameters. In an embodiment, the generation of decision-making simulations may be performed using a neural network. In an embodiment, the neural network may be trained on historical decisionmaking patterns of a plurality of demographic groups to identify behavioural trends and simulate decision-making outcomes.

[0141] In an embodiment, the simulations generation module 710 applies machine learningbased trait analysis to predict behavioural patterns and responses. The analysis uses the previously mapped cognitive traits, including drive, focus, reasoning, and self-sufficiency, and adjusts their relevance based on scenario-specific parameters. For example:Drive may be emphasized in scenarios involving competitive environments where ambition and resilience are key factors.Reasoning may play a dominant role in scenarios requiring logical decision-making, such as investment planning or resource allocation.The machine learning-based trait analysis combines these traits with external conditions specified by the scenario to generate dynamic and realistic simulations of individual or group behaviours.In an embodiment, the generation of decision-making simulations is performed using a neural network. The neural network processes the mapped cognitive traits and scenario-specific parameters to identify patterns, relationships, and behavioural outcomes. For example:The neural network may process input data such as an individual’s demographic profile, mapped traits, and the context of a financial crisis to simulate how the individual would respond to a sudden change in investment opportunities.The neural network leverages its knowledge of past decision-making trends to determine whether the individual would adopt a conservative savings strategy or take a risk for higher returns.The neural network is trained on historical decision-making patterns from a plurality of demographic groups. This training enables the network to identify behavioural trends and make accurate predictions based on the patterns it has learned. For instance, the system may predict that a demographic group known for high resilience and risk-taking is more likely to pursue aggressive strategies during a market downturn, while another group with traits emphasizing caution and long-term focus may adopt a more conservative approach.

[0142] In an embodiment, the simulations generation module 710 dynamically adjusts the weightings of the mapped cognitive traits based on scenario-specific parameters. These parameters define the external factors influencing decision-making, such as economic conditions, competitive environments, or political stability. For example: in a scenario involving a potential product recall, traits related to emotional sensitivity and reasoning may have higher weightings, as public reaction and rational decision-making are critical to understanding behavioural responses. In a scenario involving a high-risk business investment, traits related to ambition and risk tolerance may dominate the simulation, as they influence decisions to pursue or avoid the investment.

[0143] In an exemplary use case, a government agency uses the simulations generation module 710 to predict public responses to a proposed tax increase. The module processes demographic data and mapped cognitive traits, such as risk aversion and logical reasoning. The scenariospecific parameters include details about the tax structure, potential economic benefits, and public perception. The module generates simulations indicating how various demographic groups would respond to the policy change. The government uses these insights to tailor its messaging and design compensatory measures to mitigate public opposition, ensuring a smoother implementation of the tax policy.

[0144] Further, the insights generation module 712 may generate predictive insights by evaluating the simulated decision-making scenarios using a statistical modelling technique. In an embodiment, the statistical modelling technique analyses historical data and real-time data to determine potential future outcomes. In an embodiment, the predictive insights may include forecasted trends, behavioural predictions, and recommended actions. In an embodiment, thepredictive insights may be dynamically updated by integrating real-time behavioural data. In an embodiment, the real-time behavioural data may be collected from user interactions and external sources.

[0145] In an embodiment, the insights generation module 712 evaluates the outcomes of the decision-making simulations by applying a statistical modelling technique. The statistical model combines the following data sources: Historical Data: Includes past decision -making patterns, behavioural responses, and outcomes from similar scenarios. Real-Time Data: Includes current market conditions, user interactions, and external events that may influence decision-making. By integrating these data sources, the statistical model calculates probabilities, trends, and behavioural outcomes. For example, if a marketing campaign is being simulated, the model evaluates how similar campaigns in the past have influenced consumer behaviour and how real-time sentiment (e.g., social media reactions) may alter the predicted outcomes.

[0146] In an embodiment, the predictive insights generated by the insights generation module 712 may include: Forecasted Trends: Predictive trajectories of key variables, such as consumer purchasing behaviour, market demand, or public sentiment, over time. Behavioural Predictions: Likely responses of specific demographic groups or individuals to the case scenario, such as the adoption of a new product or resistance to a policy change. Recommended Actions: Suggested strategies or actions, such as adjusting pricing, modifying marketing messages, or providing financial incentives, to achieve desired outcomes. For example, in a scenario involving the launch of a new product, the predictive insights may forecast strong initial sales among certain demographic groups while identifying potential resistance from others. The recommended actions may suggest targeting resistant groups with promotional discounts or personalized marketing messages.

[0147] In an embodiment, the predictive insights are dynamically updated by continuously integrating real-time behavioural data collected from user interactions and external sources. Real-time data may include User Interactions: Feedback, responses, and engagement from users through online platforms, surveys, or feedback systems. External Sources: Data from news feeds, social media platforms, financial markets, and other relevant external events. For example, during a product launch, if real-time data indicates negative sentiment among a target demographic group, the insights generation module 712 dynamically updates its predictions and recommendations.

[0148] In an exemplary use case, a retail company uses the insights generation module 712 to predict the success of a holiday marketing campaign. The statistical model evaluates past sales data and real-time feedback from social media to generate predictive insights. The system forecasts high demand among younger consumers and recommends targeted promotions for older demographics, who are predicted to have lower engagement. As real-time data is collected during the campaign, the system dynamically updates the recommendations, suggesting price adjustments or new marketing strategies to maximize overall sales. Further, the presentation module 714 may present the predictive insights through an interactive user interface of the I / O device 708.

[0149] In an exemplary scenario, the receiving module 702 collects demographic data and cognitive traits from a combination of structured and unstructured input sources. The structured sources include government census databases, enterprise CRM systems, and market research reports containing information such as race, sex, and cultural background. Unstructured sources include social media posts, product reviews, and news articles, which provide insights into consumer sentiment and behaviour. The receiving module 702 processes this data using a natural language processing technique, which applies tokenization, named entity recognition, and sentiment analysis to extract key demographic attributes and cognitive traits. For example, the system identifies that individuals in a particular region exhibit traits of high ambition and low risk tolerance based on their historical responses to marketing campaigns. The data categorization module 704 categorizes the received demographic data using a classification algorithm. Based on learned patterns from a supervised machine learning model, the module groups individuals into predefined demographic categories such as “White Male,” “East Asian Female,” and “Hispanic Male.” The model has been trained on labelled datasets that associate demographic characteristics with corresponding cognitive traits and past behavioural patterns. For instance, individuals categorized under “East Asian Female” may be characterized by cognitive traits such as patience and long-term focus, whereas “White Male” individuals may exhibit assertiveness and strong reasoning.

[0150] The traits mapping module 706 maps cognitive traits including drive, focus, reasoning, and self-sufficiency to the categorized demographic data using a correlation model. The model establishes associations between traits and demographic groups based on historical trends and real-time behavioural feedback. For example, the mapping process reveals that the “Hispanic Male” group exhibits strong empathy and social influence, making them more receptive toemotionally engaging marketing messages. Meanwhile, the “East Asian Female” group may respond favourably to product reliability and long-term benefits. The case scenario analyzing module 708 processes a case scenario provided by the corporation, which describes the planned product launch and its key parameters. These parameters include pricing strategies, promotional events, and potential competition. The machine learning models in the module analyse the scenario and identify which cognitive traits and demographic data are most relevant for predicting the success of the product launch. For instance, the scenario analysis identifies that risk tolerance and reasoning traits are critical factors for predicting consumer adoption in a highly competitive market.

[0151] The simulations generation module 710 generates decision-making simulations for the product launch using machine learning-based trait analysis. The module simulates how different demographic groups will respond to various marketing strategies, product prices, and promotional campaigns. The simulations are performed using a neural network trained on historical decision-making patterns of multiple demographic groups. For example, the simulation predicts that the “White Male” group will show high engagement if the product is marketed with performance-oriented messaging, while the “Hispanic Female” group will respond positively to personalized, community -focused advertising.

[0152] The insights generation module 712 evaluates the outcomes of the simulations using a statistical modelling technique that integrates historical and real-time data. The module generates predictive insights, including:Forecasted Trends: Predicted consumer engagement levels and expected market share gains.Behavioural Predictions: Likely responses of key demographic groups, such as increased purchases by risk-tolerant individuals during promotional periods.Recommended Actions: Suggested marketing strategies, such as emphasizing long-term benefits for risk-averse groups and offering discounts for high-risk takers.The insights are dynamically updated as real-time data such as social media reactions and sales reports are collected during the product launch. For example, if early feedback from social media indicates concerns about pricing, the system automatically updates its recommendations to suggest targeted price reductions or bundled promotions.

[0153] The presentation module 714 displays the predictive insights through an interactive user interface. The interface provides graphical dashboards showing forecasted trends, behavioural predictions, and recommended actions. For example:A chart displays the projected engagement levels across different demographic groups over the next three months.A table lists recommended marketing actions, such as offering free trials to risk-averse customers and running limited-time promotions for highly ambitious individuals.The interface allows the corporation’s marketing team to explore different scenarios and refine their strategy in real-time, ensuring that the product launch maximizes its impact across all target demographics.

[0154] It should be noted that all such aforementioned modules 702-714 may be represented as a single module or a combination of different modules. Further, as will be appreciated by those skilled in the art, each of the modules 702-714 may reside, in whole or in parts, on one device or multiple devices in communication with each other. In some embodiments, each of the modules 702-714 may be implemented as a dedicated hardware circuit comprising custom application-specific integrated circuit (ASIC) or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. Each of the modules 702-714 may also be implemented in a programmable hardware device such as a field programmable gate array (FGPA), programmable array logic, programmable logic device, and so forth. Alternatively, each of the modules 702-714 may be implemented in software for execution by various types of processors (e.g. processor 704). An identified module of executable code may, for instance, include one or more physical or logical blocks of computer instructions, which may, for instance, be organized as an object, procedure, function, or other construct. Nevertheless, the executables of an identified module or component need not be physically located together but may include disparate instructions stored in different locations which, when joined logically together, include the module and achieve the stated purpose of the module. Indeed, a module of executable code could be a single instruction, or many instructions, and may even be distributed over several different code segments, among different applications, and across several memory devices.

[0155] As will be appreciated by one skilled in the art, a variety of processes may be employed for predicting decision-making patterns. For example, the exemplary system 600 and theassociated computing device 602 may predict decision-making patterns by the processes discussed herein. In particular, as will be appreciated by those of ordinary skill in the art, control logic and / or automated routines for performing the techniques and steps described herein may be implemented by the system 600 and the associated computing device 602 either by hardware, software, or combinations of hardware and software. For example, suitable code may be accessed and executed by the one or more processors on the system 600 to perform some or all of the techniques described herein. Similarly, application specific integrated circuits (ASICs) configured to perform some, or all of the processes described herein may be included in the one or more processors on the system 600.

[0156] Referring now to FIG. 8, a Graphical User Interface (GUI) 800 for predicting decisionmaking patterns, is disclosed, in accordance with an embodiment of the present disclosure. The GUI 800 serves as an interactive interface where users input demographic details, specify case scenarios, and receive predictive insights regarding potential decisions and behavioural responses. The GUI 800 is designed to facilitate seamless interaction between the user and the underlying prediction system by allowing for detailed input collection and dynamic feedback display.

[0157] The demographic input section 802 is configured to allow users to provide demographic details related to the person or group for which predictions are to be generated. The input fields include:Sex: The user selects the sex of the individual or group, such as "Male" or "Female."Race: The user specifies the race of the individual, for example, "White," "Asian," or "Hispanic."Religion: The user provides the religious background of the individual or group, such as "Christian," "Muslim," or "Hindu."These demographic inputs are critical as they allow the system to categorize the individual or group into predefined demographic categories, enabling accurate mapping of cognitive traits and subsequent predictive analysis.

[0158] The scenario description section 804 allows users to describe the detailed case scenario for which the prediction is sought. Users provide both the pros and cons of the scenario,ensuring that the system captures a comprehensive view of the situation. For example, in the illustrated case, the scenario involves a newly elected president facing decisions about ongoing wars initiated by a previous administration. The input includes details about political party pressures, strategic options, and potential risks or benefits associated with each decision. The system uses this scenario-specific information to identify relevant cognitive traits and contextual factors that may influence decision-making outcomes.

[0159] The key issues section 806 enables users to define specific questions or key issues related to the case scenario that the prediction should address. For example, in FIG. 8, the key issue presented is: “What will the newly elected president do when he takes office?” This input guides the system in focusing its analysis and generating targeted predictive insights based on the defined question. The key issues section helps ensure that the output is aligned with the user’s objectives and provides actionable recommendations.

[0160] The prediction controls and simulation options 808 allow users to trigger simulations and customize the analysis based on predefined personas or cognitive models. The options include:Forecast Now: Initiates the prediction process based on the entered demographic data and scenario details.Think Like Trump: Simulates decision-making based on a persona associated with specific traits, such as high ambition, assertiveness, and risk-taking.Think Like Kamala: Simulates decision-making based on a different persona, associated with traits such as empathy, collaboration, and cautious reasoning.These options provide flexibility in exploring different behavioural outcomes and tailoring predictions to fit various decision-making styles.

[0161] On the right-hand side of the interface, the GUI 800 displays the predictive insights generated by the system. In the example shown, the system analyses the newly elected president’s situation and predicts potential actions, taking into account the pressures from their political party, strategic risks, and personal values. The output includes a comprehensive explanation of the predicted behaviour and the key factors influencing the decision.

[0162] Referring now to FIG. 9, a flow diagram 900 of a methodology of predicting decisionmaking patterns, is illustrated, in accordance with an embodiment of the present disclosure. FIG. 9 is explained in conjunction with FIGs. 6-7. In an embodiment, the flow diagram 900may include a plurality of steps that may be performed by various modules of the computing device 602 so as to predict decision-making patterns.

[0163] At step 902, demographic data and human thinking traits may be received from structured input sources and unstructured input sources using a natural language processing technique. In an embodiment, the natural language processing technique may analyse the structured input sources and the unstructured input sources to extract demographic attributes and cognitive traits. In an embodiment, the natural language processing technique used for receiving the demographic data and the human thinking traits may include tokenization, named entity recognition, and sentiment analysis to extract the demographic attributes and the cognitive traits.

[0164] Further at step 904, a case scenario may be received specifying conditions or events for prediction. In an embodiment, the case scenario may include parameters related to economic, business, or geopolitical contexts.

[0165] Further at step 906, the received demographic data may be categorized by processing the extracted demographic attributes using a classification algorithm. In an embodiment, the classification algorithm groups the demographic data into a corresponding demographic group selected from a plurality of predefined demographic groups based on learned patterns. In an embodiment, the demographic attributes may include race, sex, and cultural background. In an embodiment, the categorization of the received demographic data may be performed using a supervised machine learning model. In an embodiment, the categorization of the received demographic data may be performed using a supervised machine learning model. In an embodiment, the supervised machine learning model may be trained on labelled datasets that may include association of the demographic attributes and the cognitive traits.

[0166] Further at step 908, the cognitive traits may be mapped to the categorized demographic data by employing a correlation model. In an embodiment, the cognitive traits may include drive, focus, reasoning, and self-sufficiency. In an embodiment, the correlation model may establish associations between the cognitive traits and the categorized demographic groups to generate a predictive profile.

[0167] Further at step 910, the received case scenario may be analysed using machine learning models and scenario-specific parameters to identify relevant cognitive traits and demographic data applicable to the case scenario. Further at step 912, decision-making simulations may begenerated for one or more scenarios by applying machine learning-based trait analysis. In an embodiment, the machine learning-based trait analysis predicts behavioural patterns and potential responses based on the mapped cognitive traits and scenario-specific parameters. In an embodiment, the generation of decision-making simulations may be performed using a neural network. In an embodiment, the neural network may be trained on historical decisionmaking patterns of a plurality of demographic groups to identify behavioural trends and simulate decision-making outcomes. Further at step 914, predictive insights may be generated by evaluating the simulated decision -making scenarios using a statistical modelling technique. In an embodiment, the statistical modelling technique analyses historical data and real-time data to determine potential future outcomes. In an embodiment, the predictive insights may include forecasted trends, behavioural predictions, and recommended actions. In an embodiment, the predictive insights may be dynamically updated by integrating real-time behavioural data. In an embodiment, the real-time behavioural data may be collected from user interactions and external sources. Further at step 916, the predictive insights may be presented through an interactive user interface of the I / O device 608.

[0168] Referring now to FIG. 10, an exemplary computing system 1000 for implementing embodiments consistent with the FIG. 6, is illustrated. The computing system 1000 may be employed to implement processing functionality for various embodiments (e.g., as a SIMD device, client device, server device, one or more processors, and the like). Those skilled in the relevant art will also recognize how to implement the invention using other computer systems or architectures. The computing system 1000 may represent, for example, a user device such as a desktop, a laptop, a mobile phone, a personal entertainment device, DVR, and so on, or any other type of special or general -purpose computing device as may be desirable or appropriate for a given application or environment. The computing system 1000 may include one or more processors, such as a processor 1002 that may be implemented using a general or special purpose processing engine such as, for example, a microprocessor, microcontroller, or other control logic. In this example, the processor 1002 is connected to a bus 1004 or other communication medium. In some embodiments, the processor 1002 may be an Artificial Intelligence (Al) processor, which may be implemented as a Tensor Processing Unit (TPU), or a graphical processor unit, or a custom programmable solution Field-Programmable Gate Array (FPGA).

[0169] The computing system 1000 may also include a memory 1006 (main memory), for example, Random Access Memory (RAM) or other dynamic memory, for storing information and instructions to be executed by the processor 1002. The memory 1006 also may be used for storing temporary variables or other intermediate information during the execution of instructions to be executed by the processor 1002. The computing system 1000 may likewise include a read-only memory (“ROM”) or other static storage device coupled to bus 1004 for storing static information and instructions for the processor 1002.

[0170] The computing system 1000 may also include a storage device 1008, which may include, for example, a media drive 1010 and a removable storage interface. The media drive 1010 may include a drive or other mechanism to support fixed or removable storage media, such as a hard disk drive, a floppy disk drive, a magnetic tape drive, an SD card port, a USB port, a micro-USB, an optical disk drive, a CD or DVD drive (R or RW), or other removable or fixed media drive. A storage media 1012 may include, for example, a hard disk, magnetic tape, flash drive, or other fixed or removable medium that is read by and written to by the media drive 1010. As these examples illustrate, the storage media 1012 may include a computer-readable storage medium having stored there in particular computer software or data.

[0171] In alternative embodiments, the storage devices 1008 may include other similar instrumentalities for allowing computer programs or other instructions or data to be loaded into computing system 1000. Such instrumentalities may include, for example, a removable storage unit 1014 and a storage unit interface 1016, such as a program cartridge and cartridge interface, a removable memory (for example, a flash memory or other removable memory module) and memory slot, and other removable storage units and interfaces that allow software and data to be transferred from the removable storage unit 1014 to the computing system 1000.

[0172] The computing system 1000 may also include a communications interface 1018. The communications interface 1018 may be used to allow software and data to be transferred between the computing system 1000 and external devices. Examples of the communications interface 1018 may include a network interface (such as an Ethernet or other NIC card), a communications port (such as for example, a USB port, a micro-USB port), Near field Communication (NFC), etc. Software and data transferred via the communications interface 1018 are in the form of signals which may be electronic, electromagnetic, optical, or other signals capable of being received by the communications interface 1018. These signals are provided to the communications interface 1018 via a channel 1020. The channel 1020 maycarry signals and may be implemented using a wireless medium, wire or cable, fiber optics, or other communications medium. Some examples of the channel 1020 may include a phone line, a cellular phone link, an RF link, a Bluetooth link, a network interface, a local or wide area network, and other communications channels.

[0173] The computing system 1000 may further include Input / Output (I / O) devices 1022. Examples may include, but are not limited to a display, keypad, microphone, audio speakers, vibrating motor, LED lights, etc. The I / O devices 1022 may receive input from a user and also display an output of the computation performed by the processor 1002. In this document, the terms “computer program product” and “computer-readable medium” may be used generally to refer to media such as, for example, the memory 1006, the storage devices 1008, the removable storage unit 1014, or signal(s) on the channel 1020. These and other forms of computer-readable media may be involved in providing one or more sequences of one or more instructions to the processor 1002 for execution. Such instructions, generally referred to as “computer program code” (which may be grouped in the form of computer programs or other groupings), when executed, enable the computing system 1000 to perform features or functions of embodiments of the present invention.

[0174] In an embodiment where the elements are implemented using software, the software may be stored in a computer-readable medium and loaded into the computing system 1000 using, for example, the removable storage unit 1014, the media drive 1010 or the communications interface 1018. The control logic (in this example, software instructions or computer program code), when executed by the processor 1002, causes the processor 1002 to perform the functions of the invention as described herein.

[0175] Thus, the disclosed method 900 and system 600 overcome the challenges associated with predictive decision-making, behavioural analysis, and strategic forecasting. Traditional predictive systems often suffer from limited accuracy due to a lack of comprehensive integration between diverse data sources, an inability to adapt to changing conditions, and insufficient context-specific analysis. The disclosed invention addresses these issues through combining structured and unstructured input sources, including government databases, enterprise records, social media data, and online reviews. Further, the disclosed method 900 and system 600 employ advanced natural language processing (NLP) techniques to extract meaningful demographic attributes and cognitive traits, ensuring that all relevant data is included in the prediction process. This comprehensive data integration overcomes thechallenge of incomplete or fragmented data analysis that traditional systems face. Further, the disclosed method 900 and system 600 categorize individuals into meaningful demographic groups and map cognitive traits (such as drive, focus, reasoning, and self-sufficiency) to these groups using a correlation model. By utilizing supervised machine learning models trained on historical datasets, the system ensures accurate demographic categorization and reliable mapping of traits, overcoming the challenge of generalized or inaccurate behavioural predictions.

[0176] Further, the disclosed method 900 and system 600 apply machine learning-based trait analysis to predict how individuals or groups will respond to given scenarios, such as economic changes, marketing campaigns, or policy shifts. The system dynamically adjusts the relevance of cognitive traits based on scenario-specific parameters, ensuring that simulations are tailored to the context. This feature addresses the limitation of conventional systems that produce static or non-adaptive predictions. Further, the disclosed method 900 and system 600 evaluate simulated outcomes using statistical modelling techniques that incorporate both historical and real-time behavioural data. The real-time data is continuously collected from user interactions, social media, market trends, and external sources, enabling dynamic updates to the predictive insights. This adaptive feature overcomes the challenge of outdated or static predictions by ensuring that insights reflect current conditions. The predictive insights include forecasted trends, behavioural predictions, and recommended actions.

[0177] Further, the disclosed method 900 and system 600 display this information through an interactive user interface, allowing decision-makers to quickly interpret the results and implement data-driven strategies. The ability to generate actionable recommendations overcomes the challenge of translating raw predictions into meaningful decisions. By addressing the limitations of conventional prediction systems, the disclosed method 900 and system 600 enable organizations to make accurate, adaptive, and context-sensitive decisions that lead to better strategic outcomes across various industries.

[0178] In light of the above-mentioned advantages and the technical advancements provided by the disclosed method and system, the claimed steps as discussed above are not routine, conventional, or well understood in the art, as the claimed steps enable the following solutions to the existing problems in conventional technologies. Further, the claimed steps bring an improvement in the functioning of the device itself as the claimed steps provide a technical solution to a technical problem.

[0179] The specification has described the method and system for predicting decision-making patterns. The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for the purpose of illustration and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.

[0180] Referring now to FIG. 11, a block diagram of an exemplary system 1100 for analyzing human emotional responses in a virtual communication session, is illustrated, in accordance with an embodiment of the present disclosure. The system 1100 may include a computing device 1102, an external device 1112, a data server 1114, and an image capturing device 1116 communicatively coupled to each other through a wired or wireless communication network 1110. The computing device 1102 may include a processor 1104, a memory 1106, and an input / output (I / O) device 1108.

[0181] In an embodiment, examples of processor(s) 1104 may include, but are not limited to, an Intel® Itanium® or Itanium 2 processor(s), or AMD® Opteron® or Athlon MP® processor(s), Motorola® lines of processors, Nvidia®, FortiSOC™, system on a chip processors or other future processors.

[0182] In an embodiment, the memory 1106 may store instructions that, when executed by the processor 1104, cause the processor 1104 to analyse human emotional responses in a virtual communication session, as will be discussed in greater detail herein below. In an embodiment, the memory 1106 may be a non-volatile memory or a volatile memory. In an embodiment, the memory 1106 may also store a single module or a combination of different modules to analyse human emotional responses in a virtual communication session. Examples of non-volatile memory may include, but are not limited to, a flash memory, a Read Only Memory (ROM), a Programmable ROM (PROM), Erasable PROM (EPROM), and Electrically EPROM (EEPROM) memory. Further, examples of volatile memory may include, but are not limitedto, Dynamic Random-Access Memory (DRAM), and Static Random-Access memory (SRAM).

[0183] In an embodiment, the I / O device 1108 may comprise a variety of interface(s), for example, interfaces for data input and output devices and the like. The I / O device 1108 may facilitate the inputting of instructions by a user communicating with the computing device 1102. In an embodiment, the I / O device 1108 may be wirelessly connected to the computing device 1102 through wireless network interfaces such as Bluetooth®, infrared, or any other wireless radio communication known in the art. In an embodiment, the I / O device 1108 may be connected to a communication pathway for one or more components of the computing device 1102 to facilitate the transmission of inputted instructions and output results of data generated by various components such as, but not limited to, processor(s) 1104 and memory 1106.

[0184] In an embodiment, the data server 1114 may be enabled in a remote cloud server or a co-located server and may include a secure database (not shown) to store any data necessary for the system 1100 to analyse human emotional responses in a virtual communication session. In an embodiment, the data server 1114 may store data input by an external device 1112 or output generated by the computing device 1102. In an embodiment, the computing device 1102 may be communicatively coupled with the data server 1114 through the communication network 1110.

[0185] In an embodiment, the image-capturing device 1116 may be any device capable of capturing video data in real time, such as a digital camera, a webcam, a smartphone camera, or a dedicated image sensor. The image-capturing device 1116 is configured to capture visual input, including facial expressions and movements, from participants during a virtual communication session.

[0186] In an embodiment, the communication network 1110 may be a wired or a wireless network or a combination thereof. The communication network 1110 can be implemented as one of the different types of networks, such as but not limited to ethemet IP network, intranet, local area network (LAN), wide area network (WAN), or a Metropolitan Area Network (MAN). Various devices in the system 1100 may be configured to connect to the communication network 1110, in accordance with various wired and wireless communication protocols. Examples of such wired and wireless communication protocols may include, but are not limitedto, a Transmission Control Protocol and Internet Protocol (TCP / IP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), Zig Bee, EDGE, IEEE 802.11, light fidelity (Li-Fi), 802.16, IEEE 802.11s, IEEE 802.11g, multi-hop communication, wireless access point (AP), device to device communication, cellular communication protocols, and Bluetooth (BT) communication protocols. Further the communication network 1110 can include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.

[0187] In an embodiment, the computing device 1102 may receive a plurality of inputs from the external device 1112 through the communication network 1110. In an embodiment, the computing device 1102 and the external device 1112 may be a computing system including, but not limited to, a laptop computer, a desktop computer, a notebook, a workstation, a server, a portable computer, a handheld, or a mobile device. In an embodiment, the computing device 1102 may be, but not limited to, in-built into the external device 1112 or may be a standalone computing device.

[0188] In an embodiment, the computing device 1102 may perform various processing in order to analyse human emotional responses in a virtual communication session. By way of an example, the computing device 1102 may receive a real-time video stream and an audio input of a subject captured via the image-capturing device 1116 during the virtual communication session. In an embodiment, the virtual communication session may refer to any form of interaction conducted between two or more participants using digital communication platforms that allow real-time exchange of video, audio, and / or text-based data. Examples of virtual communication sessions include video conferences, online meetings, remote learning classes, virtual interviews, telehealth consultations, webinars, and collaborative virtual work sessions. These sessions may be hosted on platforms such as Zoom®, Microsoft Teams®, Google Meet®, Webex®, or any proprietary video conferencing applications. In an embodiment, a subject, as referenced in this embodiment, may be any individual whose emotional responses are being monitored during the virtual communication session. The subject may be a participant actively engaging in the session, such as a presenter, speaker, student, patient, team member, or attendee. The subject may include passive participants who observe the virtual communication session but contribute occasionally, such as meeting observers or reviewers.

[0189] The computing device 1102 may process the real-time video stream using a computer vision model to detect facial expressions indicative of an emotional state of the subject from aplurality of predefined emotional states. In an embodiment, the emotional states may include happiness, sadness, anger, and confusion. In an embodiment, the computer vision model may include a convolutional neural network (CNN) model trained to recognize facial features corresponding to the detected emotional state.

[0190] The computing device 1102, using a trained machine learning model, may further analyse the detected emotional state to generate real-time emotion indicators for presentation to one or more participants of the virtual communication session. In an embodiment, the trained machine learning model may include a recurrent neural network (RNN) to track temporal changes in the detected emotional state. The computing device 1102 may further process the audio input of the subject using an automatic speech recognition (ASR) module to generate textual data corresponding to the audio input. The computing device 1102 may further analyse the textual data using a natural language processing (NLP) module to determine sentiment characteristics of the audio input. In an embodiment, the NLP module may include a transformer-based deep learning model to analyse linguistic patterns in the textual data to classify the sentiment characteristics. The computing device 1102 may further correlate the detected emotional state with the sentiment characteristics to generate an emotion-sentiment profile associated with the subject. The computing device 1102 may further store the emotionsentiment profile in the secure database enclosed in the data server 1114.

[0191] The computing device 1102 may further generate real-time visual feedback based on the correlated emotion-sentiment profile. The computing device 1102 may further determine one or more engagement insights to the subject based on the generated real-time visual feedback. The computing device 1102 may further generate an engagement report that may include the one or more engagement insights, the detected emotional state, and the sentiment characteristics corresponding to the subject.

[0192] In an exemplary scenario, virtual business meeting is held on a video conferencing platform, such as Zoom® or Microsoft Teams®, where a project manager presents a quarterly performance report to a team of employees and executives. The system 1100, implemented using the computing device 1102, monitors the emotional responses and engagement levels of the participants in real time using video and audio data. As the presentation begins, the imagecapturing device 1116 continuously captures the real-time video stream of each participant, including facial expressions, head movements, and micro-expressions. Simultaneously, the audio input of participants, such as questions or feedback during the meeting, is captured usinga built-in or external microphone. Further, the computing device 1102 processes the incoming video data using a computer vision model. In this case, the model is a convolutional neural network (CNN) that is pre-trained on large datasets of facial expressions to detect predefined emotional states such as happiness, sadness, anger, and confusion. For example, when a participant smiles and shows raised cheeks and crinkled eyes, the CNN classifies the emotion as happiness. Conversely, a participant with a furrowed brow and downturned lips may be classified as exhibiting confusion. The detected emotional states from the video stream are further analyzed by the computing device 1102 using a recurrent neural network (RNN). The RNN tracks temporal changes in the emotional state to understand how emotions evolve over time during the session. For instance, if a participant initially shows neutral expressions but gradually shifts to confusion and then to frustration, the RNN captures this progression and generates corresponding real-time emotion indicators. These indicators are presented to the presenter via the user interface.

[0193] Simultaneously, the computing device 1102 processes the audio input using an automatic speech recognition (ASR) module to convert spoken language into textual data. For example, when a participant asks, “Could you explain the revenue forecast again? I’m not sure I understand how it connects to the expenses,” the ASR module accurately transcribes the spoken words into text in real time. Further, the computing device 1102 analyses the transcribed text using a natural language processing (NLP) module. The NLP module includes a transformer-based deep learning model, such as BERT or GPT, which identifies sentiment characteristics from the text by analyzing linguistic patterns. In this example, the phrase “I’m not sure I understand” is classified as expressing negative sentiment, indicating potential confusion or concern about the financial data being discussed.

[0194] Thereafter, the computing device 1102 analyses the transcribed text using a natural language processing (NLP) module. The NLP module includes a transformer-based deep learning model, such as BERT or GPT, which identifies sentiment characteristics from the text by analyzing linguistic patterns. In this example, the phrase “I’m not sure I understand” is classified as expressing negative sentiment, indicating potential confusion or concern about the financial data being discussed. Further, the computing device 1102 correlates the detected emotional state from the video stream (e.g., confusion) with the sentiment characteristics extracted from the transcribed speech (e.g., negative sentiment). This correlation generates an emotion-sentiment profile associated with the participant, which reflects both the participant’sfacial expressions and their verbal feedback. In this example, the system identifies that the participant is confused and possibly concerned about the revenue forecast section of the presentation. The generated emotion-sentiment profile is securely stored in the data server 1114 using a secure database. The system employs encryption and access control mechanisms to protect sensitive information and ensure compliance with privacy regulations, such as the General Data Protection Regulation (GDPR). Further, the computing device 1102 generates real-time visual feedback based on the correlated emotion-sentiment profile. For example, the system may display a color-coded indicator on the presenter’s dashboard, where a yellow icon next to the name of the participant indicates confusion. Additionally, a sentiment bar showing a decline in sentiment over the past few minutes is displayed, prompting the presenter to address concerns of the participant.

[0195] The computing device 11102 analyses the real-time visual feedback and determines engagement insights for the participant. In this case, the module generates an insight indicating that the participant’s engagement level decreased during the revenue forecast section, possibly due to a lack of clarity or complexity in the data presentation. The system recommends that the presenter revisit or simplify this section. At the end of the session, the computing device 1102 compiles an engagement report summarizing the engagement levels, detected emotional states, and corresponding sentiment characteristics of the participant. In an embodiment, the presenter reviews the engagement report and uses the insights to adjust the presentation materials for future meetings. For example, the presenter decides to include more visual aids and simplified explanations for financial projections. The insights may also be shared with other team members via collaboration tools to enhance future team interactions.

[0196] Referring now to FIG. 12, a functional block diagram 1200 of the computing device 1102 of the exemplary system of FIG. 11, is illustrated, in accordance with an embodiment of the present disclosure. The computing device 1102 may include a receiving module 1202, a video stream processing module 1204, an emotional state analyzing module 1206, an audio input processing module 1208, a textual data analyzing module 1210, an emotional state correlating module 1212, a feedback generation module 1214, an insights determination module 1216, and an engagement report generation module 1218.

[0197] The receiving module 1202 may receive a real-time video stream and an audio input of a subj ect captured via the image-capturing device 1116 during a virtual communication session. In an embodiment, the virtual communication session may refer to any form of interactionconducted between two or more participants using digital communication platforms that allow real-time exchange of video, audio, and / or text-based data. Examples of virtual communication sessions include video conferences, online meetings, remote learning classes, virtual interviews, telehealth consultations, webinars, and collaborative virtual work sessions. These sessions may be hosted on platforms such as Zoom®, Microsoft Teams®, Google Meet®, Webex®, or any proprietary video conferencing applications. In an embodiment, a subject, as referenced in this embodiment, may be any individual whose emotional responses are being monitored during the virtual communication session. The subject may be a participant actively engaging in the session, such as presenter, speaker, student, patient, team member, or attendee. The subject may include passive participants who observer the virtual communication session but contribute occasionally, such as meeting observers or reviewers.

[0198] Further, the video stream processing module 1204 may process the real-time video stream using a computer vision model to detect facial expressions indicative of an emotional state of the subject from a plurality of predefined emotional states. In an embodiment, the emotional states may include, but are not limited to, happiness, sadness, anger, and confusion. In an embodiment, the computer vision model may be designed to analyse facial features of the subject, such as eye movements, mouth curvature, brow positioning, and cheek movements, which are critical indicators of the emotional state of the subject. For example, a smile with raised cheeks and tightened outer eye corners may indicate happiness, while downturned lips and furrowed brows may signal sadness. Similarly, a subject exhibiting tightly pressed lips, flared nostrils, and a furrowed brow may be classified as displaying anger, while raised eyebrows and widened eyes may be associated with confusion. The computer vision model may also be configured to detect additional emotions, such as surprise, fear, and disgust, depending on the specific application or training data. In an embodiment, the computer vision model may include a convolutional neural network (CNN) model trained to recognize and classify facial features corresponding to the detected emotional state based on large datasets containing labelled images of faces exhibiting various emotions. For instance, a CNN architecture such as ResNet or VGG16 may be used, where multiple layers of convolutional and pooling operations extract meaningful features from the video frames. These features may include the distance between key facial landmarks (e.g., eyes, nose, mouth) and the shape or movement patterns of facial muscles.

[0199] In an exemplary embodiment, the video stream processing module 1204 may receive a real-time video feed of a subject during a virtual meeting. The module segments the video into individual frames and passes each frame through the CNN model. The CNN model extracts and compares the spatial relationships between key facial landmarks, identifying distinctive patterns that correspond to specific emotional states. For instance, if eyes of a subject are narrowed, lips are pursed, and eyebrows are furrowed, the CNN model may classify the emotional state of the subject as anger.

[0200] Thereafter, the emotional state analyzing module 1206, using a trained machine learning model, may analyse the detected emotional state to generate real-time emotion indicators for presentation to one or more participants of the virtual communication session. In an embodiment, the trained machine learning model may include a recurrent neural network (RNN) to track temporal changes in the detected emotional state. RNNs are capable of retaining information from previous time steps, allowing the emotional state analyzing module 1206 to observe and understand how an emotional state of the subject evolves over time. For instance, the emotional state analyzing module 1206 may detect an initial neutral emotional state that gradually transitions to confusion and then to frustration. In an exemplary embodiment, during a virtual business meeting, the emotional state analyzing module 1206 may receive a continuous stream of detected emotional states from the video stream processing module 1204. Suppose the initial state indicates “neutral,” and over the course of the next several seconds, the RNN observes a progression through “confusion” and finally “anger.”

[0201] Further, the audio input processing module 1208 may process the audio input of the subject using an automatic speech recognition (ASR) module to generate textual data corresponding to the audio input. The ASR module may be responsible for converting spoken words into machine-readable text in real time to provide a foundation for further sentiment analysis and contextual understanding of the conversion. In an embodiment, the ASR module may utilize a deep learning-based speech recognition system, such as a recurrent neural network (RNN) with connect! onist temporal classification (CTC) or a transformer-based model like Whisper or DeepSpeech. These models are trained on large audio datasets containing diverse speech patterns, accents, and environments to ensure high accuracy even under varying conditions, such as background noise or different speaking speeds. In an exemplary embodiment, the ASR module may be configured to handle domain-specific vocabulary. For example, in a telehealth consultation, the ASR module may be pre-trained or fine-tuned torecognize medical terminology such as “hypertension,” “prescription,” or “diagnosis,” ensuring that the generated textual data accurately captures the context of the conversation. Similarly, in an online educational setting, the ASR module may recognize subject-specific terms like “integrals,” “photosynthesis,” or “quantum states” to support accurate text conversion for further analysis.

[0202] Thereafter, the textual data analyzing module 1210 may analyse the textual data using a natural language processing (NLP) module to determine sentiment characteristics of the audio input. In an embodiment, the NLP module is designed to process the textual data and identify emotional and contextual cues from the textual data. In an embodiment, the NLP module may include a transformer-based deep learning model to analyse linguistic patterns in the textual data to classify the sentiment characteristics as positive, negative, or neutral or further subcategories as required by the application. In an embodiment, the NLP module may include a transformer-based deep learning model such as BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-trained Transformer), or similar architectures. These models are well-suited for understanding linguistic patterns due to their ability to capture contextual dependencies within sentences and between different parts of the text. For instance, BERT can analyse phrases while considering the surrounding context, ensuring that the sentiment classification is not limited to individual words but reflects the overall meaning of the sentence or passage.

[0203] In an exemplary embodiment, during a virtual business meeting, the textual data analyzing module 1210 may receive a transcribed phrase from the ASR module, such as, “I am concerned about the project timeline.” The NLP module processes this text and identifies “concerned” as a key indicator of negative sentiment. When combined with contextual words like “project” and “timeline,” the NLP module classifies the sentiment as negative and highlights potential project management concerns. This sentiment is passed to the emotional state correlating module 1212 for further processing.

[0204] Further, the emotional state correlating module 1212 may correlate the detected emotional state with the sentiment characteristics to generate an emotion-sentiment profile associated with the subject. In an embodiment, the emotion-sentiment profile represents a comprehensive analysis of the subject’s emotional and linguistic responses during the virtual communication session. The emotional state correlating module 1212 may also store the detected emotion-sentiment profile in the secure database enclosed in the data server 1114. Inan embodiment, the emotional state correlating module 1212 may receive input from the video stream processing module 1204 (which detects emotional states based on facial expressions) and the textual data analyzing module 1210 (which determines sentiment characteristics from transcribed speech). For example, the emotional state correlating module 1212 may receive a detected emotional state of confusion and a corresponding sentiment characteristic of negative sentiment derived from a statement like, “I’m not sure how this applies to our project.” The module correlates these inputs and generates an emotion-sentiment profile indicating that the subject is confused and possibly frustrated, which is stored in the system for real-time feedback or post-session analysis.

[0205] In an embodiment, the correlation process involves analyzing temporal and contextual alignment between the emotional state and the sentiment. For instance, if the subject displays a happy facial expression while discussing a topic with a positive sentiment, the correlation strengthens the conclusion that the subject is engaged and satisfied with the topic. Conversely, if the detected emotional state is neutral or confused, but the sentiment analysis indicates a negative sentiment, the module may highlight this inconsistency, prompting further analysis to identify underlying causes.

[0206] The feedback generation module 1214 may further generate real-time visual feedback based on the correlated emotion-sentiment profile. This feedback provides actionable insights to presenters and participants, thereby enabling them to dynamically adjust their communication style, content, or pacing to better engage their audience and address potential concerns or confusion in real time. In an exemplary embodiment, the feedback generation module 1214 may generate various visual cues, such as emotion indicators, sentiment bars, or engagement heat maps displayed on the user interface of the computing device. For example, during a virtual meeting, if the emotion-sentiment profile of a subject indicates confusion or frustration, the computing device 1102 may display a red or yellow warning icon next to the subject’s name, thereby indicating that further clarification of the ongoing discussion may be necessary. In an embodiment, the feedback generation module 1214 may provide individual feedback for each subject as well as aggregated feedback for the entire group. For instance, if several participants display negative sentiment during a specific segment of a presentation, the module may generate group-level feedback indicating a need to address potential concerns or re-evaluate the content being presented. In an exemplary embodiment, during an online educational session, if the system detects that several students are displaying confusion andnegative sentiment when a specific concept is being taught, the feedback generation module 1214 may present the instructor with a real-time pop-up notification suggesting a review or a simplified explanation of the topic. The instructor may also view individual student indicators, showing which students are struggling the most.

[0207] The insights determination module 1216 may further determine one or more engagement insights to the subject based on the generated real-time visual feedback. In an embodiment, engagement insights are derived by analyzing the emotional responses, the sentiment characteristics, and the behavioural patterns of the subject over the course of the virtual communication session. These insights help identify level of engagement, understanding, interest, and potential areas of confusion or disengagement. In an embodiment, the insights determination module 1216 may analyse visual feedback generated by the feedback generation module 1214 and extract specific metrics such as engagement scores, attention spans, and emotional fluctuations. For example, the insights determination module may calculate an engagement score for the subject based on the duration and intensity of positive sentiment and emotional states such as happiness or interest. If the subject consistently displays signs of positive engagement (e.g., smiles, nods, or affirmative language), the insights determination module may generate an insight indicating high engagement and satisfaction. In an embodiment, during a virtual classroom session, the insights determination module 1216 may determine that a student exhibits high engagement during discussions on familiar topics but shows signs of confusion or reduced attention when new material is introduced. The insights determination module 1216 may generate an insight recommending additional support or a review session for the student to reinforce their understanding of the new material.

[0208] The engagement report generation module 1218 may generate an engagement report that may include the one or more engagement insights, the detected emotional state, and the sentiment characteristics corresponding to the subject. In an embodiment, the engagement report provides a comprehensive overview of level of participation, emotional responses, and overall sentiment of the subject during a virtual communication session. In an exemplary embodiment, the engagement report may consist of real-time insights gathered during the session and summary metrics computed at the conclusion of the session. The report may include Time-stamped emotional states, Sentiment characteristics, Engagement insights. In an embodiment, the Time-stamped emotional states such as, a log of detected emotional states (e.g., happiness, confusion, sadness) corresponding to specific time intervals within thesession. In an embodiment, the sentiment characteristics such as classification of transcribed speech as positive, negative, or neutral, with detailed examples of key phrases that influenced sentiment analysis. In an embodiment, the engagement insights such as High-level insights derived from real-time visual feedback, such as moments of peak engagement or instances of confusion requiring further attention.

[0209] In an exemplary embodiment, during a virtual training session, the engagement report generation module 1218 may generate a report indicating that a trainee exhibited positive engagement and happiness when interacting with hands-on exercises but displayed confusion and neutral sentiment during theoretical explanations. The report may recommend reviewing specific portions of the session where confusion was detected to ensure the trainee fully comprehends the material.

[0210] It should be noted that all such aforementioned modules 1202-1218 may be represented as a single module or a combination of different modules. Further, as will be appreciated by those skilled in the art, each of the modules 1202-1218 may reside, in whole or in parts, on one device or multiple devices in communication with each other. In some embodiments, each of the modules 1202-1218 may be implemented as a dedicated hardware circuit comprising custom application-specific integrated circuit (ASIC) or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. Each of the modules 1202-1218 may also be implemented in a programmable hardware device such as a field programmable gate array (FGPA), programmable array logic, programmable logic device, and so forth. Alternatively, each of the modules 1202-1218 may be implemented in software for execution by various types of processors (e.g., processor 1104). An identified module of executable code may, for instance, include one or more physical or logical blocks of computer instructions, which may, for instance, be organized as an object, procedure, function, or other construct. Nevertheless, the executables of an identified module or component need not be physically located together but may include disparate instructions stored in different locations which, when joined logically together, include the module and achieve the stated purpose of the module. Indeed, a module of executable code could be a single instruction or many instructions and may even be distributed over several different code segments, among different applications, and across several memory devices.

[0211] As will be appreciated by one skilled in the art, a variety of processes may be employed for analyzing human emotional responses in a virtual communication session. For example, theexemplary system 1100 and the associated computing device 1102 may analyse human emotional responses in a virtual communication session by the processes discussed herein. In particular, as will be appreciated by those of ordinary skill in the art, control logic and / or automated routines for performing the techniques and steps described herein may be implemented by the system 1100 and the associated computing device 1102 either by hardware, software, or combinations of hardware and software. For example, suitable code may be accessed and executed by the one or more processors on the system 1100 to perform some or all of the techniques described herein. Similarly, application-specific integrated circuits (ASICs) configured to perform some or all of the processes described herein may be included in the one or more processors on the system 1100.

[0212] Referring now to FIG. 13, a flow diagram 1300 of a methodology of analysing human emotional responses in a virtual communication session, is illustrated, in accordance with an embodiment of the present disclosure. FIG. 13 is explained in conjunction with FIGs. 11-12. In an embodiment, the flow diagram 1300 may include a plurality of steps that may be performed by various modules of the computing device 1102 so as to analyse human emotional responses in a virtual communication session.

[0213] At step 1302, a real-time video stream and an audio input of a subject captured via an image-capturing device 1116 is received during the virtual communication session. In an embodiment, the virtual communication session may refer to any form of interaction conducted between two or more participants using digital communication platforms that allow real-time exchange of video, audio, and / or text-based data. Examples of virtual communication sessions include video conferences, online meetings, remote learning classes, virtual interviews, telehealth consultations, webinars, and collaborative virtual work sessions. These sessions may be hosted on platforms such as Zoom®, Microsoft Teams®, Google Meet®, Webex®, or any proprietary video conferencing applications. In an embodiment, a subject, as referenced in this embodiment, may be any individual whose emotional responses are being monitored during the virtual communication session. The subject may be a participant actively engaging in the session, such as presenter, speaker, student, patient, team member, or attendee. The subject may include passive participants who observer the virtual communication session but contribute occasionally, such as meeting observers or reviewers.

[0214] Further at step 1304, the real-time video stream may be processed using a computer vision model to detect facial expressions indicative of an emotional state of the subject from aplurality of predefined emotional states. In an embodiment, the emotional states may include happiness, sadness, anger, and confusion. In an embodiment, the computer vision model may include a convolutional neural network (CNN) model trained to recognize facial features corresponding to the detected emotional state.

[0215] Further at step 1306, the detected emotional state is analysed using a trained machine learning model to generate real-time emotion indicators for presentation to one or more participants of the virtual communication session. In an embodiment, the trained machine learning model may include a recurrent neural network (RNN) to track temporal changes in the detected emotional state.

[0216] Further at step 1308, the audio input of the subject is processed using an automatic speech recognition (ASR) module to generate textual data corresponding to the audio input. Further at step 1310, the textual data may be analysed using a natural language processing (NLP) module to determine sentiment characteristics of the audio input. In an embodiment, the NLP module may include a transformer-based deep learning model to analyse linguistic patterns in the textual data to classify the sentiment characteristics.

[0217] Further at step 1312, the detected emotional state may be correlated with the sentiment characteristics to generate an emotional-sentiment profile associated with the subject. Further at step 1314, real-time visual feedback is generated based on the correlated emotion-sentiment profile. Further at step 1316, one or more engagement insights is determined to the subject based on the generated real-time visual feedback.

[0218] Referring now to FIG. 14, an exemplary computing system 1400 for implementing embodiments consistent with the FIG. 11, is illustrated. The computing system 1400 may be employed to implement processing functionality for various embodiments (e.g., as a SIMD device, client device, server device, one or more processors, and the like). Those skilled in the relevant art will also recognize how to implement the invention using other computer systems or architectures. The computing system 1400 may represent, for example, a user device such as a desktop, a laptop, a mobile phone, a personal entertainment device, DVR, and so on, or any other type of special or general -purpose computing device as may be desirable or appropriate for a given application or environment. The computing system 1400 may include one or more processors, such as a processor 1402 that may be implemented using a general or special purpose processing engine such as, for example, a microprocessor, microcontroller, orother control logic. In this example, the processor 1402 is connected to a bus 1404 or other communication medium. In some embodiments, the processor 1402 may be an Artificial Intelligence (Al) processor, which may be implemented as a Tensor Processing Unit (TPU), or a graphical processor unit, or a custom programmable solution Field-Programmable Gate Array (FPGA).

[0219] The computing system 1400 may also include a memory 1406 (main memory), for example, Random Access Memory (RAM) or other dynamic memory, for storing information and instructions to be executed by the processor 1402. The memory 1406 may also be used for storing temporary variables or other intermediate information during the execution of instructions to be executed by the processor 1402. The computing system 1400 may likewise include a read-only memory (“ROM”) or other static storage device coupled to bus 1404 for storing static information and instructions for the processor 1402.

[0220] The computing system 1400 may also include a storage device 1408, which may include, for example, a media drive 1410 and a removable storage interface. The media drive 1410 may include a drive or other mechanism to support fixed or removable storage media, such as a hard disk drive, a floppy disk drive, a magnetic tape drive, an SD card port, a USB port, a micro-USB, an optical disk drive, a CD or DVD drive (R or RW), or other removable or fixed media drive. A storage media 1412 may include, for example, a hard disk, magnetic tape, flash drive, or other fixed or removable medium that is read by and written to by the media drive 1410. As these examples illustrate, the storage media 1412 may include a computer-readable storage medium having stored there in particular computer software or data.

[0221] In alternative embodiments, the storage devices 1408 may include other similar instrumentalities for allowing computer programs or other instructions or data to be loaded into the computing system 1400. Such instrumentalities may include, for example, a removable storage unit 1414 and a storage unit interface 1416, such as a program cartridge and cartridge interface, a removable memory (for example, a flash memory or other removable memory module) and memory slot, and other removable storage units and interfaces that allow software and data to be transferred from the removable storage unit 1414 to the computing system 1400.

[0222] The computing system 1400 may also include a communications interface 1418. The communications interface 1418 may be used to allow software and data to be transferred between the computing system 1400 and external devices. Examples of the communications interface 1418 may include a network interface (such as an Ethernet or other NIC card), acommunications port (such as for example, a USB port, a micro-USB port), Near field Communication (NFC), etc. Software and data transferred via the communications interface 1418 are in the form of signals, which may be electronic, electromagnetic, optical, or other signals capable of being received by the communications interface 1418. These signals are provided to the communications interface 1418 via a channel 1420. The channel 1420 may carry signals and may be implemented using a wireless medium, wire or cable, fiber optics, or other communications medium. Some examples of the channel 1420 may include a phone line, a cellular phone link, an RF link, a Bluetooth link, a network interface, a local or wide area network, and other communications channels.

[0223] The computing system 1400 may further include Input / Output (I / O) devices 1422. Examples may include, but are not limited to, a display, keypad, microphone, audio speakers, vibrating motor, LED lights, etc. The I / O devices 1422 may receive input from a user and also display an output of the computation performed by the processor 1402. In this document, the terms “computer program product” and “computer-readable medium” may be used generally to refer to media such as, for example, the memory 1406, the storage devices 1408, the removable storage unit 1414, or signal(s) on the channel 1420. These and other forms of computer-readable media may be involved in providing one or more sequences of one or more instructions to the processor 1402 for execution. Such instructions, generally referred to as “computer program code” (which may be grouped in the form of computer programs or other groupings), when executed, enable the computing system 1400 to perform features or functions of embodiments of the present invention.

[0224] In an embodiment where the elements are implemented using software, the software may be stored in a computer-readable medium and loaded into the computing system 1400 using, for example, the removable storage unit 1414, the media drive 1410, or the communications interface 1418. The control logic (in this example, software instructions or computer program code), when executed by the processor 1402, causes the processor 1402 to perform the functions of the invention as described herein. For example, the software may execute the following processes: Processing video streams using a computer vision model (e.g., CNN) to detect facial expressions, analyzing audio input using ASR to convert speech to text, correlating emotional states and sentiment data to generate an emotion-sentiment profile, providing real-time feedback and engagement insights through a user-friendly interface, generating engagement reports summarizing key insights for post-session review.

[0225] Thus, the disclosed method 1300 and system 1100 overcome the challenges associated with detecting, analyzing, and interpreting emotional responses and engagement levels in real time during virtual communication sessions. Traditional video conferencing systems often rely on subj ective or delayed feedback, making it difficult for presenters, instructors, or team leaders to gauge participant understanding or engagement effectively. The disclosed method 1300 and system 1100 address these limitations by integrating computer vision, automatic speech recognition (ASR), natural language processing (NLP), and machine learning-based emotionsentiment correlation into a single, streamlined system.

[0226] One significant challenge in virtual communication is the inability to observe nonverbal cues, such as facial expressions or body language, which play a vital role in understanding participant reactions. The disclosed method 1300 and system 1100 overcome this issue by using a computer vision model (e.g., CNN) to detect and classify facial expressions, such as happiness, sadness, anger, or confusion, in real time. Additionally, by tracking temporal changes in emotional states using a recurrent neural network (RNN), the disclosed method 1300 and system 1100 provide dynamic emotion indicators, ensuring that even subtle changes in engagement are captured and addressed promptly.

[0227] As will be appreciated by those skilled in the art, the techniques described in the various embodiments discussed above are not routine, or conventional, or well -understood in the art. The techniques discussed above provide for analyzing human emotional responses in a virtual communication session.

Claims

CLAIMSWhat is Claimed is:

1. A computer-implemented method for analyzing human decision-making patterns based on emotional responses, the method comprising: receiving, by a processor and via a capturing device, facial expressions of a subject while the subject interacts with displayed content, wherein the displayed content is displayed on an interactive user interface; processing, by the processor and using a computer vision algorithm, the captured facial expressions to identify emotional states of the subject; extracting, by the processor and using a text categorization module, contextual data from the displayed content to determine topics associated with the emotional states of the subject; correlating, by the processor, the identified emotional states with the determined topics to generate an emotional response profile; and analyzing, by the processor, the emotional response profile using a machine learning model to identify patterns in decision-making behaviour of the subject.

2. The method of claim 1, further comprising: generating, by the processor, predictive insights regarding financial decision-making tendencies of the subject based on the identified patterns; and presenting, by the processor, the predictive insights through the interactive user interface.

3. The method of claim 1, further comprising: storing, by the processor, the emotional response profile in a person-specific database, wherein the person-specific database comprises demographic, socio-economic, and educational information of the subject.

4. The method of claim 1, wherein the processing of the captured facial expressions comprises: detecting, by the processor, micro-expressions indicative of specific emotional states by applying a feature extraction algorithm on the captured facial expressions;normalizing, by the processor, the detected micro-expressions based on historical facial expression data stored in the person-specific database; and assigning, by the processor, confidence scores to the identified emotional states to improve accuracy in decision-making analysis.

5. The method of claim 1, further comprising: ranking, by the processor, the correlated emotional states and the determined topics based on an emotional impact score; determining, by the processor using an artificial intelligence model, an optimized order of priority for addressing needs of the subject based on the emotional impact score; generating, by the processor, a decision-making model that prioritized ranked needs of the subject in alignment with cultural and socio-economic influences extracted from the person-specific database.

6. The method of claim 3, further comprising: segmenting, by the processor, the stored emotional data based on cultural, regional, and contextual factors; retrieving, by the processor, the segmented data for comparison with aggregate emotional response patterns across a plurality of demographic groups; and updating, by the processor, the person-specific database with new emotional response data to refine predictive accuracy over time.

7. A system for analyzing human decision-making patterns based on emotional responses, comprising: a capturing device; a processor; and a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, cause the processor to: receive, via the capturing device, facial expressions of a subject while the subject interacts with displayed content, wherein the displayed content is displayed on an interactive user interface; process, using a computer vision algorithm, the captured facial expressions to identify emotional states of the subject;extract, using a text categorization module, contextual data from the displayed content to determine topics associated with the emotional states of the subject; correlate the identified emotional states with the determined topics to generate an emotional response profile; and analyse the stored emotional response profile using a machine learning model to identify patterns in decision-making behaviour of the subject.

8. The system as claimed in claim 7, wherein the processor-executable instructions, which, on execution, further cause the processor to: generate predictive insights regarding financial decision-making tendencies of the subject based on the identified patterns; and present the predictive insights through the interactive user interface.

9. The system as claimed in claim 7, wherein the processor-executable instructions, which, on execution, cause the processor to: store the emotional response profile in a person-specific database, wherein the person-specific database comprises demographic, socio-economic, and educational information of the subject.

10. The system as claimed in claim 7, wherein to process the captured facial expressions, the processor-executable instructions, on execution, cause the processor to: detect micro-expressions indicative of specific emotional states by applying a feature extraction algorithm on the captured facial expressions; normalize the detected micro-expressions based on historical facial expression data stored in the person-specific database; and assign confidence scores to the identified emotional states to improve accuracy in decision-making analysis.

11. The system as claimed in claim 7, wherein the processor-executable instructions, which, on execution, cause the processor to: rank the correlated emotional states and topics based on an emotional impact score; determine, using an artificial intelligence model, an optimized order of priority for addressing needs of the subject based on the emotional impact score;generate a decision-making model that prioritized ranked needs of the subject in alignment with cultural and socio-economic influences extracted from the person-specific database.

12. The system as claimed in claim 9, wherein the processor-executable instructions, on execution, further cause the processor to: segment the stored emotional data based on cultural, regional, and contextual factors; retrieve the segmented data for comparison with aggregate emotional response patterns across a plurality of demographic groups; and update the person-specific database with new emotional response data to refine predictive accuracy over time.

13. A computer-implemented method for predicting decision-making patterns, the method comprising: receiving, by a processor, demographic data and human thinking traits from structured input sources and unstructured input sources using a natural language processing technique, wherein the natural language processing technique analyses the structured input sources and the unstructured input sources to extract demographic attributes and cognitive traits; receiving, by the processor, a case scenario specifying conditions or events for prediction; categorizing, by the processor, the received demographic data by processing the extracted demographic attributes using a classification algorithm; mapping, by the processor, the cognitive traits to the categorized demographic data by employing a correlation model; analyzing, by the processor, the received case scenario using machine learning models and scenario-specific parameters to identify relevant cognitive traits and demographic data applicable to the case scenario; and generating, by the processor, decision-making simulations for one or more scenarios by applying machine learning-based trait analysis.

14. The method of claim 13, wherein the case scenario comprises parameters related to economic, business, or geopolitical contexts.

15. The method of claim 13, wherein the classification algorithm groups the demographic data into a corresponding demographic group selected from a plurality of predefined demographic groups based on learned patterns.

16. The method of claim 13, wherein the demographic attributes comprising race, sex, and cultural background.

17. The method of claim 13, wherein the cognitive traits comprising drive, focus, reasoning, and self-sufficiency.

18. The method of claim 13, wherein the correlation model establishes associations between the cognitive traits and the categorized demographic groups to generate a predictive profile.

19. The method of claim 13, wherein the machine learning-based trait analysis predicts behavioural patterns and potential responses based on the mapped cognitive traits and scenariospecific parameters.

20. The method of claim 13, further comprising: generating, by the processor, predictive insights by evaluating the simulated decisionmaking scenarios using a statistical modelling technique, wherein the statistical modelling technique analyses historical data and realtime data to determine potential future outcomes, and wherein the predictive insights comprising forecasted trends, behavioural predictions, and recommended actions; and presenting, by the processor, the predictive insights through an interactive user interface.

21. The method of claim 13, wherein the natural language processing technique used for receiving the demographic data and the human thinking traits comprises tokenization, named entity recognition, and sentiment analysis to extract the demographic attributes and the cognitive traits.

22. The method of claim 13, wherein the categorization of the received demographic data is performed using a supervised machine learning model, and wherein the supervised machine learning model is trained on labelled datasets comprises association of the demographic attributes and the cognitive traits.

23. The method of claim 13, wherein the generation of decision-making simulations is performed using a neural network, and wherein the neural network is trained on historical decision-making patterns of a plurality of demographic groups to identify behavioural trends and simulate decision-making outcomes.

24. The method of claim 13, wherein the predictive insights are dynamically updated by integrating real-time behavioural data, and wherein the real-time behavioural data is collected from user interactions and external sources.

25. A system for predicting decision-making patterns, comprising: a processor; and a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, cause the processor to: receive demographic data and human thinking traits from structured input sources and unstructured input sources using a natural language processing technique, wherein the natural language processing technique analyses the structured input sources and the unstructured input sources to extract demographic attributes and cognitive traits; receive a case scenario specifying conditions or events for prediction; categorize the received demographic data by processing the extracted demographic attributes using a classification algorithm; map the cognitive traits to the categorized demographic data by employing a correlation model; analyse the received case scenario using machine learning models and scenariospecific parameters to identify relevant cognitive traits and demographic data applicable to the case scenario; andgenerate decision-making simulations for one or more scenarios by applying machine learning-based trait analysis.

26. The system as claimed in claim 25, wherein the case scenario comprises parameters related to economic, business, or geopolitical contexts.

27. The system as claimed in claim 25, wherein the classification algorithm groups the demographic data into a corresponding demographic group selected from a plurality of predefined demographic groups based on learned patterns.

28. The system as claimed in claim 25, wherein the demographic attributes comprising race, sex, and cultural background.

29. The system as claimed in claim 25, wherein the cognitive traits comprising drive, focus, reasoning, and self-sufficiency.

30. The system claimed in claim 25, wherein the correlation model establishes associations between the cognitive traits and the categorized demographic groups to generate a predictive profile.

31. The system as claimed in claim 25, wherein the machine learning-based trait analysis predicts bhavioural patterns and potential responses based on the mapped cognitive traits and scenario-specific parameters.

32. The system as claimed in claim 25, wherein the processor-executable instructions, which, on execution, further cause the processor to: generate predictive insights by evaluating the simulated decision-making scenarios using a statistical modelling technique, wherein the statistical modelling technique analyses historical data and realtime data to determine potential future outcomes, and wherein the predictive insights comprising forecasted trends, behavioural predictions, and recommended actions; and present the predictive insights through an interactive user interface.

33. The system of claim 25, wherein the natural language processing technique used for receiving the demographic data and the human thinking traits comprises tokenization, named entity recognition, and sentiment analysis to extract the demographic attributes and the cognitive traits.

34. The system of claim 25, wherein the categorization of the received demographic data is performed using a supervised machine learning model, and wherein the supervised machine learning model is trained on labelled datasets comprises association of the demographic attributes and the cognitive traits.

35. The system of claim 25, wherein the generation of decision-making simulations is performed using a neural network, and wherein the neural network is trained on historical decision-making patterns of a plurality of demographic groups to identify behavioural trends and simulate decision-making outcomes.

36. The system of claim 25, wherein the predictive insights are dynamically updated by integrating real-time behavioural data, and wherein the real-time behavioural data is collected from user interactions and external sources.

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