1. An artificial intelligence-based system (100) for assessment and enhancement of psychological capital, the system (100) comprising: a camera (102) configured to capture facial affective and behavioral indicators of a user in real-time; a wearable device (104) configured to collect real-time physiological and biometric data associated with the user; an audio capture device (106) configured to record real-time audio signals of the user; a user interface (108) integrated into a user device (112) and configured to receive the real-time multimodal behavioral and physiological data obtained from the camera (102), the wearable device (104) and the audio capture device (106); a communication network (114) configured to establish a communication link for seamless data transmission within the system (100); a processing unit (116) operatively coupled to the camera (102), the wearable device (104), the audio capture device (106) and the user device (112) via the communication network (112) and configured to process and analyze the real-time multimodal behavioral and physiological data to generate a quantified psychological capital profile of the user and facilitate personalized adaptive enhancement, wherein the processing unit (116) further comprises: a data acquisition module (120) configured to acquire the real-time multimodal behavioral and physiological data from the user device (112); a preprocessing module (122) configured to perform preprocessing operations on the acquired data for subsequent analysis; a feature extraction module (124) configured to extract latent multimodal feature representations from the preprocessed data; a psychological capital inference module (126) configured to analyze the extracted features and predict a plurality of quantified psychological capital components; a profile generation module (128) configured to generate a psychological capital profile vector based on the predicted quantified psychological capital components; an intervention computation module (130) configured to compute personalized interventions based on the generated psychological capital profile vector; and an output module (138) configured to transmit the computed personalized interventions to the user interface (108) of the user device (112).

2. The system (100) as claimed in claim 1, wherein the system (100) further comprises a cloud database (118) configured to store the multimodal behavioral and physiological data and the computed personalized interventions to enable analysis, adaptive learning and subsequent retrieval of the data.

3. The system (100) as claimed in claim 1, wherein the user interface (108) further comprises a chatbot (110) configured to receive textual inputs from the user to facilitate assessment and adaptive enhancement of the psychological capital.

4. The system (100) as claimed in claim 1, wherein the latent multimodal feature representations are extracted by employing natural language processing algorithms and convolutional neural network-based computer vision models.

5. The system (100) as claimed in claim 1, wherein the plurality of quantified psychological capital components include but not limited to hope, self-efficacy, resilience and optimism and predicted by employing a multi-layer artificial neural network.

6. The system (100) as claimed in claim 1, wherein the processing unit (116) comprises a feedback module (132) configured to receive feedback from the user and update the psychological capital profile and associated personalized interventions based on the received feedback.

7. The system (100) as claimed in claim 1, wherein the processing unit (116) comprises an adaptive learning module (134) configured to monitor user responses, long term trends and interaction metrics to implement a closed-loop learning mechanism and enhance the accuracy and adaptability of the system (100) over time.

8. The system (100) as claimed in claim 1, wherein the processing unit (116) further comprises a training and testing module (136) configured to split data into training and testing datasets and train the functional modules and algorithms using training dataset.

9. The system (100) as claimed in claim 1, wherein the output module (138) is further configured to display gamified progress reports, charts, psychological scores, indicate areas of strength and areas for improvement and longitudinal changes in the quantified psychological capital profile and the efficacy of the personalized interventions.

10. A method (200) for an artificial intelligence-based system (100) for assessment and enhancement of psychological capital, the method (200) comprising: capturing facial affective and behavioral indicators of a user in real-time via a camera (102); collecting real-time physiological and biometric data associated with the user via a wearable device (104); recording real-time audio signals of the user via an audio capture device (106); receiving the real-time multimodal behavioral and physiological data obtained from the camera (102), the wearable device (104) and the audio capture device (106) via a user interface (108) of a user device (112); establishing a communication link for seamless data transmission within the system (100) via a communication network (114); processing and analyzing the real-time multimodal behavioral and physiological data to generate a quantified psychological capital profile of the user and facilitating personalized adaptive enhancement via a processing unit (116); acquiring the real-time multimodal behavioral and physiological data from the user device (112) via a data acquisition module (120); performing preprocessing operations on the acquired data for subsequent analysis via a preprocessing module (122); extracting latent multimodal feature representations from the preprocessed data via a feature extraction module (124); analyzing the extracted features and predicting a plurality of quantified psychological capital components via a psychological capital inference module (126); generating a psychological capital profile vector based on the predicted quantified psychological capital components via a profile generation module (128); computing personalized interventions based on the generated psychological capital profile vector via an intervention computation module (130); and transmitting the computed personalized interventions to the user interface (108) of the user device (112) via an output module (138).

Description:FIELD OF DISCLOSURE
[0001] The present disclosure generally relates to the field of artificial intelligence, behavioral analytics, and digital psychological assessment systems, and more particularly, to an intelligent, artificial intelligence-based multimodal psychological capital assessment and enhancement system. The disclosure is particularly directed to adaptive analytical frameworks that integrate machine learning-based inference models, natural language processing, computer vision techniques, acoustic signal analysis, and physiological data analytics to process multimodal behavioral data and generate structured, interpretable psychological capital profiles and personalized intervention recommendations.
BACKGROUND OF THE DISCLOSURE
[0002] Psychological capital represents a measurable psychological construct associated with human behavior, performance and adaptive development across diverse contexts. In educational institutions, corporate environments, clinical settings and personal development contexts there is growing emphasis on measuring and enhancing these psychological constructs to improve productivity, mental health, adaptability and sustained engagement. With the rapid advancement of digital technologies, various mobile applications, wearable devices and online assessment tools have been introduced to monitor aspects of human behavior, emotional states and physiological responses. However, most existing digital systems focus on isolated metrics such as mood tracking, stress monitoring or self-reported questionnaire responses without providing an integrated and continuous framework for comprehensive psychological capital evaluation and enhancement. Furthermore, traditional psychological assessments are typically administered periodically through standardized surveys or manual evaluations conducted by trained professionals. These approaches are often time-consuming, subjective and limited in frequency, thereby restricting real-time monitoring and adaptive intervention capabilities. As human behavior and emotional states are dynamic and context-dependent the absence of continuous and multimodal data integration results in fragmented insights and delayed identification of psychological capital deficiencies. These limitations underscore the need for a systematic, data-driven mechanism capable of objectively quantifying and enhancing psychological capital through continuous and adaptive analysis.
[0003] Conventional methods for psychological assessment primarily rely on self-report instruments, static questionnaires or standalone behavioral analytics tools. While such tools may provide baseline measurements for psychological capital they are inherently susceptible to response bias, recall inaccuracies and situational subjectivity. Existing digital well-being platforms may incorporate basic sentiment analysis or wearable-derived physiological indicators. However, these systems generally operate in isolation and lack a unified architecture for multimodal data fusion. Additionally, current approaches fail to implement advanced artificial intelligence-based predictive models capable of correlating facial affective signals, physiological biomarkers, acoustic features and textual expressions within a single analytical framework. The absence of synchronized temporal alignment and cross-modal feature integration restricts the accuracy and reliability of psychological inference. Moreover, conventional platforms are not configured to dynamically adapt intervention strategies based on real-time user responses and longitudinal psychological trends. Most existing systems provide static recommendations without incorporating adaptive learning mechanisms to refine predictive parameters over time. The lack of continuous feedback integration and model recalibration results in limited personalization reduced predictive robustness and suboptimal long-term psychological development support.
[0004] The present invention overcomes these limitations by providing an artificial intelligence-based multimodal psychological capital assessment and enhancement system configured to enable real-time, objective and adaptive psychological evaluation. Unlike traditional systems that rely solely on self-reported data or isolated behavioral metrics the present invention integrates facial analysis, physiological and biometric signals, acoustic features and textual inputs within a unified analytical architecture. The system aggregates and harmonizes heterogeneous multimodal data streams into structured numerical feature representations suitable for neural network-based inference. By implementing machine learning-based predictive modeling techniques, the invention quantifies psychological capital components through probabilistic scoring and confidence metrics. The system further incorporates adaptive intervention computation mechanisms that generate personalized and dynamically adjustable developmental pathways based on evolving psychological capital profiles. Longitudinal tracking and feedback-driven adaptive learning modules continuously refine model parameters and intervention strategies, thereby enhancing personalization accuracy and system performance under changing contextual conditions. Accordingly, the present invention provides a scalable, intelligent and continuously self-improving framework capable of transforming psychological capital assessment from periodic static evaluation to real-time, data-driven enhancement, thereby effectively addressing the deficiencies inherent in conventional methodologies.
[0005] Thus, in light of the above-stated discussion, there exists a need for an artificial intelligence-based system for assessment and enhancement of psychological capital.
SUMMARY OF THE DISCLOSURE
[0006] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensures and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0007] According to illustrative embodiments, the present disclosure focuses on an artificial intelligence-based system for assessment and enhancement of psychological capital which overcomes the above-mentioned disadvantages or provides the users with a useful or commercial choice.
[0008] An objective of the present disclosure is to provide an artificial intelligence-based system for assessment and enhancement of psychological capital.
[0009] An objective of the present disclosure is to provide an integrated analytical framework configured to acquire and process heterogeneous multimodal data including behavioral, physiological, audio, and textual inputs within a unified architecture.
[0010] An objective of the present disclosure is to enable objective and quantifiable measurement of psychological capital components using machine learning-based predictive models.
[0011] An objective of the present disclosure is to reduce reliance on subjective self-report assessments by incorporating automated multimodal data analysis techniques.
[0012] An objective of the present disclosure is to compute personalized and adaptive intervention strategies based on dynamically inferred psychological capital levels.
[0013] An objective of the present disclosure is to implement a closed-loop feedback mechanism configured to continuously refine predictive models and intervention parameters.
[0014] An objective of the present disclosure is to provide real-time monitoring and longitudinal tracking of psychological development across multiple time horizons.
[0015] An objective of the present disclosure is to improve transparency and interpretability of psychological assessments through structured scoring mechanisms and actionable feedback outputs.
[0016] An objective of the present disclosure is to provide scalable and modular system architecture capable of deployment across individual, educational, organizational, and clinical environments.
[0017] An objective of the present disclosure is to transform psychological capital evaluation from a static, periodic assessment model into a continuously adaptive and self-improving enhancement framework.
[0018] In light of the above, in one aspect of the present disclosure an artificial intelligence-based system for assessment and enhancement of psychological capital is disclosed herein. The system comprises a camera configured to capture facial affective and behavioral indicators of a user in real-time. The system includes a wearable device configured to collect real-time physiological and biometric data associated with the user. The system also includes an audio capture device configured to record real-time audio signals of the user. The system also includes a user interface integrated into a user device and configured to receive the real-time multimodal behavioral and physiological data obtained from the camera, the wearable device and the audio capture device. The system also includes a communication network configured to establish a communication link for seamless data transmission within the system. The system also includes a processing unit operatively coupled to the camera, the wearable device, the audio capture device and the user device via the communication network and configured to process and analyze the real-time multimodal behavioral and physiological data to generate a quantified psychological capital profile of the user and facilitate personalized adaptive enhancement, wherein the processing unit further comprises a data acquisition module configured to acquire the real-time multimodal behavioral and physiological data from the user device, a preprocessing module configured to perform preprocessing operations on the acquired data for subsequent analysis, a feature extraction module configured to extract latent multimodal feature representations from the preprocessed data, a psychological capital inference module configured to analyze the extracted features and predict a plurality of quantified psychological capital components, a profile generation module configured to generate a psychological capital profile vector based on the predicted quantified psychological capital components, an intervention computation module configured to compute personalized interventions based on the generated psychological capital profile vector and an output module configured to transmit the computed personalized interventions to the user interface of the user device.
[0019] In one embodiment, the system further comprises a cloud database configured to store the multimodal behavioral and physiological data and the computed personalized interventions to enable analysis, adaptive learning and subsequent retrieval of the data.
[0020] In one embodiment, the user interface further comprises a chatbot configured to receive textual inputs from the user to facilitate assessment and adaptive enhancement of the psychological capital.
[0021] In one embodiment, the latent multimodal feature representations are extracted by employing natural language processing algorithms and convolutional neural network-based computer vision models.
[0022] In one embodiment, the plurality of quantified psychological capital components include but not limited to hope, self-efficacy, resilience and optimism and predicted by employing a multi-layer artificial neural network.
[0023] In one embodiment, the processing unit comprises a feedback module configured to receive feedback from the user and update the psychological capital profile and associated personalized interventions based on the received feedback.
[0024] In one embodiment, the processing unit comprises an adaptive learning module configured to monitor user responses, long term trends and interaction metrics to implement a closed-loop learning mechanism and enhance the accuracy and adaptability of the system over time.
[0025] In one embodiment, the processing unit further comprises a training and testing module configured to split data into training and testing datasets and train the functional modules and algorithms using training dataset.
[0026] In one embodiment, the output module is further configured to display gamified progress reports, charts, psychological scores, indicate areas of strength and areas for improvement and longitudinal changes in the quantified psychological capital profile and the efficacy of the personalized interventions.
[0027] In light of the above, in one aspect of the present disclosure, a method for an artificial intelligence-based system for assessment and enhancement of psychological capital is disclosed herein. The method comprises capturing facial affective and behavioral indicators of a user in real-time via a camera. The method includes collecting real-time physiological and biometric data associated with the user via a wearable device. The method also includes recording real-time audio signals of the user via an audio capture device. The method also includes receiving the real-time multimodal behavioral and physiological data obtained from the camera, the wearable device and the audio capture device via a user interface of a user device. The method also includes establishing a communication link for seamless data transmission within the system via a communication network. The method also includes processing and analyzing the real-time multimodal behavioral and physiological data to generate a quantified psychological capital profile of the user and facilitating personalized adaptive enhancement via a processing unit. The method also includes acquiring the real-time multimodal behavioral and physiological data from the user device via a data acquisition module. The method also includes performing preprocessing operations on the acquired data for subsequent analysis via a preprocessing module. The method also includes extracting latent multimodal feature representations from the preprocessed data via a feature extraction module. The method also includes analyzing the extracted features and predicting a plurality of quantified psychological capital components via a psychological capital inference module. The method also includes generating a psychological capital profile vector based on the predicted quantified psychological capital components via a profile generation module. The method also includes computing personalized interventions based on the generated psychological capital profile vector via an intervention computation module. The method also includes transmitting the computed personalized interventions to the user interface of the user device via an output module.
[0028] These and other advantages will be apparent from the present application of the embodiments described herein.
[0029] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0030] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0031] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0032] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0033] FIG. 1 illustrates a block diagram of an artificial intelligence-based system for assessment and enhancement of psychological capital, in accordance with an exemplary embodiment of the present disclosure.
[0034] FIG. 2 illustrates a method for an artificial intelligence-based system for assessment and enhancement of psychological capital, in accordance with an exemplary embodiment of the present disclosure.
[0035] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0036] An artificial intelligence-based system for assessment and enhancement of psychological capital is illustrated in the accompanying drawings, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0037] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0038] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0039] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0040] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0041] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0042] Referring now to FIG. 1 and FIG. 2 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a block diagram of an artificial intelligence-based system 100 for assessment and enhancement of psychological capital, in accordance with an exemplary embodiment of the present disclosure.
[0043] The system 100 may include a camera 102, a wearable device 104, an audio capture device 106, a user interface 108, a user device 112, a communication network 114 and a processing unit 116.
[0044] In one embodiment of the present invention, the system 100 is designed to implement an artificial intelligence-driven multimodal analytical framework for assessment and adaptive enhancement of psychological capital of a user.
[0045] The camera 102 is configured to capture facial affective and behavioral indicators of a user in real-time.
[0046] In one embodiment of the present invention, the facial affective and behavioral indicators include but not limited to facial micro-expressions, emotional expressions, smiles, frowns, stress indicators and other visual behavioral cues.
[0047] The wearable device 104 is configured to collect real-time physiological and biometric data associated with the user.
[0048] In one embodiment of the present invention, the physiological and biometric data include but not limited to heart rate, skin conductance, activity levels and other biometric signals.
[0049] The audio capture device 106 is configured to record real-time audio signals of the user.
[0050] In one embodiment of the present invention, the audio signals include but not limited to voice recordings characterized by analyzing tone, pace, pitch, prosody and emotional attributes.
[0051] The user interface 108 integrated into a user device 112 and is configured to receive the real-time multimodal behavioral and physiological data obtained from the camera 102, the wearable device 104 and the audio capture device 106.
[0052] In one embodiment of the present invention, the user interface 108 further comprises a chatbot 110 configured to receive textual inputs from the user to facilitate assessment and adaptive enhancement of the psychological capital.
[0053] In one embodiment of the present invention, the textual inputs include but not limited to responses to prompts, chat messages, journal entries and responses to survey-based questions.
[0054] In one embodiment of the present invention, the user device 112 may include but not limited to smart-phone, tablet, laptop, computer and many other.
[0055] In one embodiment of the present invention, the user interface 104 is further configured to improve user accessibility and operational efficiency of the system 100 by facilitating intuitive interaction and informed decision-making without requiring specialized technical expertise.
[0056] The communication network 114 is configured to establish a communication link for seamless data transmission within the system 100.
[0057] In one embodiment of the present invention, the communication network 114 is further configured to enable secure and consistent data exchange across the system 100 by maintaining data integrity and uninterrupted communication.
[0058] In one embodiment of the present invention, the communication network 114 includes but not limited to a wired network, a wireless network, a cellular communication network, a bluetooth-based network and an internet-based communication network.
[0059] The processing unit 116 operatively coupled to the camera 102, the wearable device 104, the audio capture device 106 and the user device 112 via the communication network 114 and is configured to process and analyze the real-time multimodal behavioral and physiological data to generate a quantified psychological capital profile of the user and facilitate personalized adaptive enhancement. The processing unit 116 further comprises several modules including a data acquisition module 120, a preprocessing module 122, a feature extraction module 124, a psychological capital inference module 126, a profile generation module 128, an intervention computation module 130 and an output module 138.
[0060] In one embodiment of the present invention, the processing unit 116 is further configured to implement secure data encryption, anonymization and access control mechanisms to ensure confidentiality and integrity of the data.
[0061] In one embodiment of the present invention, the processing unit 116 may include, but not limited to a microcontroller, a microprocessor, a computing device, a development board, an application-specific integrated circuit, a system-on-chip.
[0062] In one embodiment of the present invention, the system 100 further comprises a cloud database 118 configured to store the multimodal behavioral and physiological data and the computed personalized interventions to enable analysis, adaptive learning and subsequent retrieval of the data.
[0063] The data acquisition module 120 is configured to acquire the real-time multimodal behavioral and physiological data from the user device 112.
[0064] In one embodiment of the present invention, the data acquisition module 120 is further configured to aggregate, synchronize and validate the real-time multimodal behavioral and physiological data prior to downstream processing.
[0065] In one embodiment of the present invention, the data acquisition module 120 obtains diverse multimodal behavioral and physiological data from the user device 112 and transmits the acquired data to the processing unit 116 for subsequent processing, correlation and comparative analysis within the system 100.
[0066] The preprocessing module 122 is configured to perform preprocessing operations on the acquired data for subsequent analysis.
[0067] In one embodiment of the present invention, the preprocessing operations include but not limited to data cleaning, normalization, noise removal and standardization of data formats.
[0068] In one embodiment of the present invention, the preprocessing module 122 is further configured to perform data transformation, feature scaling, missing data imputation, temporal alignment of multimodal data streams and segmentation of the acquired data.
[0069] The feature extraction module 124 is configured to extract latent multimodal feature representations from the preprocessed data.
[0070] In one embodiment of the present invention, the latent multimodal feature representations are extracted by employing natural language processing algorithms and convolutional neural network-based computer vision models.
[0071] In one embodiment of the present invention, the natural language processing algorithms are employed to analyze textual sentiment, word frequency patterns and emotional content and the convolutional neural network-based computer vision models are employed to analyze facial expressions and visual features.
[0072] In one embodiment of the present invention, the acoustic feature extraction techniques are employed to extract audio features including pitch, rhythm and energy.
[0073] In one embodiment of the present invention, the feature extraction module 124 is further configured to transform the preprocessed data into structured numerical feature vectors suitable for input.
[0074] The psychological capital inference module 126 is configured to analyze the extracted features and predict a plurality of quantified psychological capital components.
[0075] In one embodiment of the present invention, the plurality of quantified psychological capital components include but not limited to hope, self-efficacy, resilience and optimism and predicted by employing a multi-layer artificial neural network.
[0076] In one embodiment of the present invention, the psychological capital inference module 126 is further configured to determine probabilistic scores and confidence metrics corresponding to the predicted psychological capital components.
[0077] The profile generation module 128 is configured to generate a psychological capital profile vector based on the predicted quantified psychological capital components.
[0078] In one embodiment of the present invention, the psychological capital profile vector represents psychological capital of the user in a quantifiable form and transforms the multimodal behavioral signals into objective, real-time psychological scores.
[0079] The intervention computation module 130 is configured to compute personalized interventions based on the generated psychological capital profile vector.
[0080] In one embodiment of the present invention, the computed personalized interventions include but not limited to motivational prompts, resilience-building exercises, optimism-focused exercises and self-efficacy enhancement challenges.
[0081] In one embodiment of the present invention, the intervention computation module 130 is further configured to dynamically adapt the type, intensity, frequency and delivery timing of the personalized interventions based on real-time user responses, engagement metrics and historical psychological capital data.
[0082] In one embodiment of the present invention, the processing unit 116 comprises a feedback module 132 configured to receive feedback from the user and update the psychological capital profile and associated personalized interventions based on the received feedback.
[0083] In one embodiment of the present invention, feedback module 132 is further configured to transform the psychological capital assessment into actionable recommendations by providing a tailored developmental pathway for the user and dynamically adapts to current psychological capital deficiencies and user engagement levels.
[0084] In one embodiment of the present invention, the processing unit 116 comprises an adaptive learning module 134 configured to monitor user responses, long term trends and interaction metrics to implement a closed-loop learning mechanism and enhance the accuracy and adaptability of the system 100 over time.
[0085] In one embodiment of the present invention, the adaptive learning module 134 is further configured to enable the system 100 to iteratively refine the predictive models based on accumulated user data and feedback to enhance personalization, accuracy and performance of the system 100 over time.
[0086] In one embodiment of the present invention the processing unit 116 further comprises a training and testing module 136 configured to split data into training and testing datasets and train the functional modules and algorithms using training dataset.
[0087] In one embodiment of the present invention, the training and testing module 136 is further configured to train and update the model weights to improve prediction accuracy, intervention effectiveness and personalization performance.
[0088] The output module 138 is configured to transmit the computed personalized interventions to the user interface 108 of the user device 112.
[0089] In one embodiment of the present invention, the output module 138 is further configured to display gamified progress reports, charts, psychological scores, indicate areas of strength and areas for improvement and longitudinal changes in the quantified psychological capital profile and the efficacy of the personalized interventions.
[0090] In one embodiment of the present invention, the system 100 is configured to enhance user engagement and motivation, render psychological growth measurable and quantifiable and enable dynamic, personalized and continuous psychological development to provide a self-improving adaptive framework for psychological capital enhancement.
[0091] FIG. 2 illustrates a method for an artificial intelligence-based system 100 for assessment and enhancement of psychological capital.
[0092] The method 200 may include the following steps:
[0093] At step 202, facial affective and behavioral indicators of a user in real-time are captured via a camera 102.
[0094] At step 204, real-time physiological and biometric data associated with the user is collected via a wearable device 104.
[0095] At step 206, real-time audio signals of the user are recorded via an audio capture device 106.
[0096] At step 208, the real-time multimodal behavioral and physiological data obtained from the camera 102, the wearable device 104 and the audio capture device 106 is received via a user interface 108 of a user device 112.
[0097] At step 210, a communication link for seamless data transmission is established within the system 100 via a communication network 114.
[0098] At step 212, the real-time multimodal behavioral and physiological data is processed and analyzed to generate a quantified psychological capital profile of the user and personalized adaptive enhancement is facilitated via a processing unit 116.
[0099] At step 214, the real-time multimodal behavioral and physiological data is acquired from the user device 112 via a data acquisition module 120.
[0100] At step 216, preprocessing operations are performed on the acquired data for subsequent analysis via a preprocessing module 122.
[0101] At step 218, latent multimodal feature representations are extracted from the preprocessed data via a feature extraction module 124.
[0102] At step 220, the extracted features are analyzed and a plurality of quantified psychological capital components is predicted via a psychological capital inference module 126.
[0103] At step 222, a psychological capital profile vector is generated based on the predicted quantified psychological capital components via a profile generation module 128.
[0104] At step 224, personalized interventions are computed based on the generated psychological capital profile vector via an intervention computation module 130.
[0105] In one embodiment of the present invention, feedback from the user is received and the psychological capital profile and associated personalized interventions are updated based on the received feedback via a feedback module 132.
[0106] In one embodiment of the present invention, user responses, long term trends and interaction metrics are monitored to implement a closed-loop learning mechanism and the accuracy and adaptability of the system 100 is enhanced over time via an adaptive learning module 134.
[0107] In one embodiment of the present invention, the data is splitted into training and testing datasets and the functional modules and algorithms are trained using training dataset via a training and testing module 136.
[0108] At step 226, the computed personalized interventions are transmitted to the user interface 108 of the user device 112 via an output module 138.
[0109] In the best mode of operation, the system 100 is initiated through the user device 112 by obtaining the real-time multimodal behavioral and physiological data obtained from the camera 102, the wearable device 104, the audio capture device 106 and the chatbot 110 received through the user interface 108 from the user and transmitted to the processing unit 116 via the communication network 114 for integrated analytical evaluation. The multimodal behavioral and physiological data is acquired through the data acquisition module 120 and preprocessing operations including data cleaning, normalization, noise reduction, missing data imputation, feature scaling and temporal alignment are performed on the acquired data by the preprocessing module 122 to ensure structured and consistent downstream analysis. The preprocessed data is then provided to the feature extraction module 124 to extract latent multimodal feature representations using natural language processing techniques for textual analysis, convolutional neural network-based computer vision models for facial and visual analysis. The extracted features are transformed into unified numerical feature vectors by the psychological capital inference module 126 to predict the plurality of quantified psychological capital components by employing multi-layer artificial neural network model. The psychological capital profile vector is subsequently generated via the profile generation module 128 based on the predicted quantified psychological capital components and the intervention computation module 130 computes personalized interventions tailored to the identified psychological capital levels and deficiencies. The feedback module 132 converts assessment and intervention outcomes into actionable recommendations and tailored developmental pathways and the adaptive learning module 134 continuously refines predictive models and intervention parameters by incorporating accumulated user data and feedback to enhance personalization, prediction accuracy and the system 100 performance over time. The output module 138 transmits updated psychological capital assessments, intervention recommendations, and analytical insights to the user interface 108 of the user device 112 for real-time user access, monitoring, and continued engagement, thereby enabling a continuously adaptive and self-improving psychological capital enhancement framework.
[0110] The system 100 offers significant advantages by providing an artificial intelligence-based, data-driven framework for real-time assessment and enhancement of psychological capital. The system 100 integrates multimodal data acquisition, natural language processing, computer vision, and acoustic signal processing techniques within a unified architecture to analyze complex behavioral, physiological, audio, and textual inputs and generate quantified psychological capital metrics. By employing multimodal feature fusion, neural network-based inference models, and adaptive intervention computation, the system 100 enables structured identification and measurement of psychological capital components including hope, self-efficacy, resilience, and optimism. Further, the system 100 enhances assessment objectivity, reduces reliance on subjective self-reporting, and supports continuous refinement through feedback-driven and adaptive learning mechanisms. The system 100 improves transparency through interpretable psychological capital profiles, quantified scoring mechanisms, and personalized intervention pathways, facilitating measurable and trackable psychological development.
[0111]
[0112] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0113] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0114] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0115] Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0116] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. An artificial intelligence-based system (100) for assessment and enhancement of psychological capital, the system (100) comprising:
a camera (102) configured to capture facial affective and behavioral indicators of a user in real-time;
a wearable device (104) configured to collect real-time physiological and biometric data associated with the user;
an audio capture device (106) configured to record real-time audio signals of the user;
a user interface (108) integrated into a user device (112) and configured to receive the real-time multimodal behavioral and physiological data obtained from the camera (102), the wearable device (104) and the audio capture device (106);
a communication network (114) configured to establish a communication link for seamless data transmission within the system (100);
a processing unit (116) operatively coupled to the camera (102), the wearable device (104), the audio capture device (106) and the user device (112) via the communication network (112) and configured to process and analyze the real-time multimodal behavioral and physiological data to generate a quantified psychological capital profile of the user and facilitate personalized adaptive enhancement, wherein the processing unit (116) further comprises:
a data acquisition module (120) configured to acquire the real-time multimodal behavioral and physiological data from the user device (112);
a preprocessing module (122) configured to perform preprocessing operations on the acquired data for subsequent analysis;
a feature extraction module (124) configured to extract latent multimodal feature representations from the preprocessed data;
a psychological capital inference module (126) configured to analyze the extracted features and predict a plurality of quantified psychological capital components;
a profile generation module (128) configured to generate a psychological capital profile vector based on the predicted quantified psychological capital components;
an intervention computation module (130) configured to compute personalized interventions based on the generated psychological capital profile vector; and
an output module (138) configured to transmit the computed personalized interventions to the user interface (108) of the user device (112).
2. The system (100) as claimed in claim 1, wherein the system (100) further comprises a cloud database (118) configured to store the multimodal behavioral and physiological data and the computed personalized interventions to enable analysis, adaptive learning and subsequent retrieval of the data.
3. The system (100) as claimed in claim 1, wherein the user interface (108) further comprises a chatbot (110) configured to receive textual inputs from the user to facilitate assessment and adaptive enhancement of the psychological capital.
4. The system (100) as claimed in claim 1, wherein the latent multimodal feature representations are extracted by employing natural language processing algorithms and convolutional neural network-based computer vision models.
5. The system (100) as claimed in claim 1, wherein the plurality of quantified psychological capital components include but not limited to hope, self-efficacy, resilience and optimism and predicted by employing a multi-layer artificial neural network.
6. The system (100) as claimed in claim 1, wherein the processing unit (116) comprises a feedback module (132) configured to receive feedback from the user and update the psychological capital profile and associated personalized interventions based on the received feedback.
7. The system (100) as claimed in claim 1, wherein the processing unit (116) comprises an adaptive learning module (134) configured to monitor user responses, long term trends and interaction metrics to implement a closed-loop learning mechanism and enhance the accuracy and adaptability of the system (100) over time.
8. The system (100) as claimed in claim 1, wherein the processing unit (116) further comprises a training and testing module (136) configured to split data into training and testing datasets and train the functional modules and algorithms using training dataset.
9. The system (100) as claimed in claim 1, wherein the output module (138) is further configured to display gamified progress reports, charts, psychological scores, indicate areas of strength and areas for improvement and longitudinal changes in the quantified psychological capital profile and the efficacy of the personalized interventions.
10. A method (200) for an artificial intelligence-based system (100) for assessment and enhancement of psychological capital, the method (200) comprising:
capturing facial affective and behavioral indicators of a user in real-time via a camera (102);
collecting real-time physiological and biometric data associated with the user via a wearable device (104);
recording real-time audio signals of the user via an audio capture device (106);
receiving the real-time multimodal behavioral and physiological data obtained from the camera (102), the wearable device (104) and the audio capture device (106) via a user interface (108) of a user device (112);
establishing a communication link for seamless data transmission within the system (100) via a communication network (114);
processing and analyzing the real-time multimodal behavioral and physiological data to generate a quantified psychological capital profile of the user and facilitating personalized adaptive enhancement via a processing unit (116);
acquiring the real-time multimodal behavioral and physiological data from the user device (112) via a data acquisition module (120);
performing preprocessing operations on the acquired data for subsequent analysis via a preprocessing module (122);
extracting latent multimodal feature representations from the preprocessed data via a feature extraction module (124);
analyzing the extracted features and predicting a plurality of quantified psychological capital components via a psychological capital inference module (126);
generating a psychological capital profile vector based on the predicted quantified psychological capital components via a profile generation module (128);
computing personalized interventions based on the generated psychological capital profile vector via an intervention computation module (130); and
transmitting the computed personalized interventions to the user interface (108) of the user device (112) via an output module (138).