Rendering multimedia content with branched storylines based on user demography and emotions

Abstract

An electronic device and method for rendering of multimedia content with branched storyline based on user demography and emotions. A first electronic device receives multimedia content including a set of branched storylines associated with the multimedia content. The first electronic device receives demographic information and emotion information associated with a set of users of a second electronic device. The first electronic device applies a first artificial intelligence (AI) model on the demographic information and the emotion information and selects a set of media segments from the multimedia content, based on the applied first AI model. The set of media segments corresponds to one or more first branched storylines from the set of branched storylines. The first electronic device transmits the set of media segments to the second electronic device. The second electronic device controls the display device to render of the set of media segments.

Description

RENDERING MULTIMEDIA CONTENT WITH BRANCHED STORYLINES BASED ON USER DEMOGRAPHY AND EMOTIONSCROSS-REFERENCE TO RELATED APPLICATIONS / INCORPORATION BY REFERENCE

[0001] This application claims priority to Indian Provisional Application No. IN202411032497, filed April 24, 2024, which is hereby incorporated by reference in its entirety.FIELD

[0002] Various embodiments of the disclosure relate to rendering of multimedia content. More specifically, various embodiments of the disclosure relate to an electronic device and method for rendering of multimedia content with branched storyline based on user demography and emotions.BACKGROUND

[0003] Audio visual content consumption has become increasingly prevalent with the proliferation of streaming services and smart devices. Users can now access a wide variety of content on-demand from various locations. This advancement has led to greater flexibility in how and when people consume media. Traditional content rating systems provide broad categorization but may not capture the nuanced emotional experiences of individual viewers. Content providers typically employ techniques such as age-based ratings or content warnings to help users make viewing decisions. However, these approaches have limitations and may not address the diverse sensitivities and preferences of audience members. Some viewers may find certain scenes or themes disturbing, while others may be unaffected. Previous solutions often lack granularity and personalization in filtering content, potentially leading to unexpected exposure to unwanted material or overly restrictive limitations on viewable content.

[0004] Limitations and disadvantages of conventional and traditional approaches willbecome apparent to one of skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.SUMMARY

[0005] An electronic device and method for rendering of multimedia content with branched storyline based on user demography and emotions is provided substantially as shown in, and / or described in connection with, at least one of the figures, as set forth more completely in the claims.

[0006] These and other features and advantages of the present disclosure may be appreciated from a review of the following detailed description of the present disclosure, along with the accompanying figures in which like reference numerals refer to like parts throughout.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] FIG. 1 is a block diagram that illustrates an exemplary network environment for rendering of multimedia content with branched storyline based on user demography and emotions, in accordance with an embodiment of the disclosure.

[0008] FIG. 2A is a block diagram that illustrates an exemplary first electronic device of FIG. 1 , in accordance with an embodiment of the disclosure.

[0009] FIG. 2B is a block diagram that illustrates an exemplary second electronic device of FIG. 1 , in accordance with an embodiment of the disclosure.

[0010] FIG. 3 is a block diagram that illustrates an exemplary processing pipeline for transmission of set of media segments of multimedia content with branched storyline from first electronic device, based on user demography and emotions, in accordance with an embodiment of the disclosure.

[0011] FIG. 4 is a block diagram that illustrates an exemplary processing pipeline torender set of media segments of multimedia content with branched storyline at second electronic device, based on user demography and emotions, in accordance with an embodiment of the disclosure.

[0012] FIG. 5 is a block diagram that illustrates an exemplary processing pipeline to render of set of media segments of multimedia content with branched storyline at second electronic device, based on user mind map, in accordance with an embodiment of the disclosure.

[0013] FIG. 6 is a diagram that illustrates an exemplary scenario of branched storyline, in accordance with an embodiment of the disclosure.

[0014] FIG. 7 is a flowchart that illustrates operations of an exemplary method for transmission of set of media segments of multimedia content with branched storyline from first electronic device, based on user demography and emotions, in accordance with an embodiment of the disclosure.

[0015] FIG. 8 is a flowchart that illustrates operations of an exemplary method to render of set of media segments of multimedia content with branched storyline at second electronic device, based on user demography and emotions, in accordance with an embodiment of the disclosure.DETAILED DESCRIPTION.

[0016] The following described implementation may be found in a first electronic device, a second electronic device, and method for rendering of multimedia content with branched storyline based on user demography and emotions. Exemplary aspects of the disclosure may provide a first electronic device and a second electronic device (for example, a server, a desktop, a smartwatch, a smartphone, a display, a television, a laptop, or a personal computer) that may control a display device to render of multimedia content with branched storyline based on user demography and emotions. The first electronic device may receivemultimedia content including a set of branched storylines associated with the multimedia content. The first electronic device may receive from the second electronic device, demographic information and emotion information associated with a set of users of the second electronic device. The first electronic device may apply a first artificial intelligence (Al) model on the demographic information and the emotion information. The first electronic device may select a set of media segments from the multimedia content, based on the application of the first Al model. The set of media segments may correspond to one or more first branched storylines from the set of branched storylines. The first electronic device may transmit the set of media segments to the second electronic device. The second electronic device may be configured to control the display device to render the set of media segments.

[0017] The second electronic device may detect a set of users associated with the second electronic device. The second electronic device may capture sensor data associated with the set of users based on the detection of the set of users. The second electronic device may apply a second artificial intelligence (Al) model on the sensor data. The second electronic device may determine demographic information and emotion information associated with the set of users, based on the application of the second Al model. The second electronic device may transmit to the first electronic device, the demographic information and the emotion information. The second electronic device may receive, from the first electronic device, a set of media segments of the multimedia content, based on the transmitted demographic information and the transmitted emotion information. The multimedia content includes a set of branched storylines, and the set of media segments corresponds to one or more first branched storylines from the set of branched storylines. The second electronic device may control the display device to render the set of media segments.

[0018] Typically, the digital content systems primarily rely on user input to enhance playback experience or accelerate download speeds and may focus on technical optimizations rather than content personalization. The traditional systems typically do not influence the storyline or incorporate cognitive functions for path selection, resulting in a one-size-fits-all approach to content delivery. As a result, the traditional systems may often fail to tailor the viewing experience to the specific interests and emotional states of individual viewers. This limitation underscores the need for more advanced solutions that can dynamically adapt content in real-time, to provide a more personalized and engaging experience without requiring constant user intervention.

[0019] In order to address the requirements, the present disclosure introduces a system and method that may eliminate the need for user input to make branching choices and may thereby provide a seamless and immersive viewing experience. Based on incorporation of different genres and multiple endings within a single storyline, the disclosed system may cater to a wide range of viewer preferences and ensure that each viewing session feels unique and engaging. The adaptive streaming capability, which adjusts content based on the viewer’s age, ensures age-appropriate and safe content delivery, may enhance content suitability for all audience groups. Additionally, the polymorphic nature of the story may provide a rich variety of content, to keep viewers intrigued and entertained with diverse narrative possibilities. Based on incorporation of multiple genres together and dynamic adaptation to the viewers’ likes and emotional states, the disclosed system may not only enhance viewer satisfaction but also maximize engagement, which may make the content more likeable and personalized. The disclosed technique may improve content relevance across diverse demographic groups and enable adaptation of content to viewers' emotional states in real-time, which may potentially reduce exposure to unwanted or distressing content. The present disclosure not onlyenhances the viewer’s experience but also refines the content preparation process, based on an adjustment of elements such as background colors to boost engagement.

[0020] FIG. 1 is a block diagram that illustrates an exemplary network environment for rendering of multimedia content with branched storyline based on user demography and emotions, in accordance with an embodiment of the disclosure. With reference to FIG. 1 , there is shown a network environment 100. The network environment 100 may include a first electronic device 102, a set of sensors 104B, a server 106, a database 108, and a communication network 112. With reference to FIG. 1 , there is further shown a first artificial intelligence (Al) model 102A associated with the first electronic device 102, and a second artificial intelligence (Al) model 104A associated with the second electronic device 104. The database 108 may include references to multimedia content 110. The first electronic device 102, the second electronic device 104, and the server 106 may be communicatively coupled to one another, via the communication network 112. In FIG. 1 , there is further shown a set of users 114 associated with the second electronic device 104. For example, users in the set of users 114 may be part of a human audience of the multimedia content 110, such as, a movie or a series on a television.

[0021] The first electronic device 102 may include suitable logic, circuitry, interfaces, and / or code that may be configured to receive multimedia content (e.g., the multimedia content 110) including a set of branched storylines associated with the multimedia content 110. The first electronic device 102 may receive from the second electronic device 104, demographic information and emotion information associated with a set of users 114 of the second electronic device 104. The first electronic device 102 may apply the first Al model 102A on the demographic information and the emotion information. The first electronic device 102 may select a set of media segments from the multimedia content 110, based on the application of the first Al mode 102AI. The set of media segments maycorrespond to one or more first branched storylines from the set of branched storylines. The first electronic device 102 may transmit the set of media segments to the second electronic device 104. The second electronic device 104 may be configured to control the display device to render the set of media segments.

[0022] In an embodiment, each branched storyline of the set of branched storylines corresponds to at least one of the demographic information associated with the set of users or the emotion information associated with the set of users. In an embodiment, the demographic information of the set of users 114 may include at least one of an age, a gender, a race, an ethnicity, a user profile indicative of an education level, an employment status, a marital status, a geographic location, a religion, or health conditions associated with the set of users 114. In an embodiment, the emotion information of the set of users may include at least one of a mood, emotional triggers, an emotional history, stress levels, coping mechanisms, an emotional intelligence, an emotional support, or a mental health associated with the set of users 114.

[0023] In an embodiment, the first electronic device 102 may detect a branch point of the one or more first branched storylines of the set of branched storylines associated with the multimedia content 110. The set of media segments may be selected from the multimedia content 110 further based on the branch point. The branch point may correspond to a media segment of the multimedia content 110 at which a storyline of the set of branched storylines splits into the one or more first branched storylines.

[0024] In an embodiment, the first electronic device 102 may receive from the second electronic device 104, a mind map of a cognitive state of at least a first user from the set of users 114. The first electronic device 102 may apply the first Al model 102A on the mind map of the cognitive state to select a first media segment from the multimedia content 110, based on the application of the first Al model 102A on the mind map of the cognitive state.The first electronic device 102 may transmit the first media segment to the second electronic device 104. The second electronic device 104 may be configured to control the display device to render the first media segment for at least the first user. As used herein, the term “mind map” may refer to a structured representation of the user cognitive state. The cognitive state may include the thoughts, emotions, preferences, and mental patterns. The mind map may capture the user mental state, allow the first electronic device 102 to interpret and analyze the cognitive processes. Examples of the first electronic device 102 may include, but are not limited to, a computing device, a server a smartphone, a cellular phone, a display device, a television, a mobile phone, a gaming device, a mainframe machine, a computer workstation, a consumer electronic (CE) device and / or the likes.

[0025] The first Al model 102A may be a combination of machine learning (ML) techniques that may be configured to analyze the multimedia content 110 and determine a plurality of media segments from the multimedia content 110 to select a set of media segments from the multimedia content 110. In an embodiment, the first Al model 102A may applied on mind map of the cognitive state received from the second electronic device 104 to select the first media segment from the multimedia content 110. In an embodiment, the first Al model 102A may correspond to at least one of a natural language processing (NLP) model, a neural language model, a sentiment analysis model, an emotional recognition model, a demographic analysis model, a recommendation system, or a classification machine learning (ML) model.

[0026] The first Al model 102A may be a hybrid network, which may include multiple neural networks. Each of the multiple neural networks may be a computational network or a system of artificial neurons, arranged in a plurality of layers, as nodes. The plurality of layers of the neural network may include an input layer, one or more hidden layers, and an output layer. Each layer of the plurality of layers may include one or more nodes (orartificial neurons). Outputs of all nodes in the input layer may be coupled to at least one node of hidden layer(s). Similarly, inputs of each hidden layer may be coupled to outputs of at least one node in other layers of the neural network. Outputs of each hidden layer may be coupled to inputs of at least one node in other layers of the neural network. Node(s) in the final layer may receive inputs from at least one hidden layer to output a result. The number of layers and the number of nodes in each layer may be determined from hyperparameters of the neural network. Such hyper-parameters may be set before or after training the neural network on the training dataset.

[0027] Each neural network of the neural networks may include electronic data, which may be implemented as, for example, a software component of an application executable on the first electronic device 102. Each of the neural networks may rely on libraries, external scripts, or other logic / instructions for execution by a processing device, such as the first electronic device 102. Each of the neural networks may rely on code and routines to enable a computing device, such as the first electronic device 102 to perform one or more operations, such as, analysis of the multimedia content 110 to determine plurality of media segments associated with a split storyline in the multimedia content 110. In some embodiments, each of the neural networks may be implemented using hardware including a processor, a microprocessor (e.g., to perform or control performance of one or more operations), a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). Alternatively, in some embodiments, each of the neural networks may be implemented using a combination of hardware and software.

[0028] The second electronic device 104 may include suitable logic, circuitry, interfaces, and / or code that may be configured to detect the set of users 114 associated with the second electronic device 104. The second electronic device 104 may capture sensor data associated with the set of users 114 based on the detection of the set of users 114. In anembodiment, the second electronic device 104 may extract a user profile associated with the set of users 114 based on the detection of the set of users 114. For example, the user profile may be collection of data that represents the characteristics, preferences, and behaviors of the set of users 114. The user profile may include details such as, but not limited to, the education level, the employment status, the marital status, the geographic location, the religion, health conditions, identity information, usage patterns, interaction history, and device settings associated with the set of users 114. The second electronic device 104 may apply the second Al model 104A on the sensor data. The second electronic device 104 may determine demographic information and emotion information associated with the set of users 114, based on the application of the second Al model 104A. In an embodiment, the second electronic device 104 may determine demographic information and emotion information associated with the set of users 114, based on the user profile. The second electronic device 104 may transmit to the first electronic device 102, the demographic information and the emotion information. The second electronic device 104 may receive from the first electronic device 102, the set of media segments of the multimedia content 110, based on the transmitted demographic information and the transmitted emotion information. The multimedia content 110 may include a set of branched storylines, and the set of media segments corresponds to one or more first branched storylines from the set of branched storylines. The second electronic device 104 may control the display device to render the set of media segments.

[0029] In an embodiment, the second electronic device 104 may determine a first color- grading / quality of the set of media segments of the multimedia content 110. The second electronic device 104 may change the first color-grading / quality to a second color- grading / quality of the set of media segments based on the emotion information associated with the set of users 114. The second electronic device 104 may control the display deviceto render the set of media segments based on the second color-grading / quality. In an embodiment, the sensor data may be captured by the set of sensors 104B associated with the second electronic device 104. Examples of the second electronic device 104 may include, but are not limited to, a computing device, a server, a smartphone, a cellular phone, a mobile phone, a gaming device, a display device, a television, a mainframe machine, a computer workstation, a consumer electronic (CE) device and / or the likes.

[0030] The second Al model 104A may be a combination of machine learning (ML) techniques that may be configured to perform operations, such as preprocessing of sensor data and feature extraction, for determination of demographic information and emotion information. The sensor data collection may include data such as heart rate, body temperature, movement patterns, location, accelerometer, gyroscope, microphone, facial expressions, body language, ambient light, noise levels, and the likes. The sensor data preprocessing may include removal of noise and irrelevant data from the sensor data, standardization of the sensor data, and segmentation of the sensor data based on determined patterns. The feature extraction may include determination of demographic information based on age, gender, and location of the set of users 114, and determination of emotion information based on facial feature, voice tone, and physiological signals of the set of users 114. The second Al model 104A may be deployed to analyze captured sensor data in real-time and infer demographic information and emotion information for each user of the set of users 114, based on the sensor data. In an embodiment, the second Al model 104A may corresponds to at least one of a supervised learning model, an unsupervised learning model, a semi-supervised learning model, a self-supervised learning model, a deep learning model, a reinforced learning model, or an anomaly detection model.

[0031] The second Al model 104A may be a hybrid network, which may include multiple neural networks. Each of the multiple neural networks may be a computational network ora system of artificial neurons, arranged in a plurality of layers, as nodes. The plurality of layers of the neural network may include an input layer, one or more hidden layers, and an output layer. Each layer of the plurality of layers may include one or more nodes (or artificial neurons). Outputs of all nodes in the input layer may be coupled to at least one node of hidden layer(s). Similarly, inputs of each hidden layer may be coupled to outputs of at least one node in other layers of the neural network. Outputs of each hidden layer may be coupled to inputs of at least one node in other layers of the neural network. Node(s) in the final layer may receive inputs from at least one hidden layer to output a result. The number of layers and the number of nodes in each layer may be determined from hyperparameters of the neural network. Such hyper-parameters may be set before or after training the neural network on the training dataset.

[0032] Each neural network of the neural networks may include electronic data, which may be implemented as, for example, a software component of an application executable on the second electronic device 104. Each of the neural networks may rely on libraries, external scripts, or other logic / instructions for execution by a processing device, such as the second electronic device 104. Each of the neural networks may rely on code and routines to enable a computing device, such as the second electronic device 104 to perform one or more operations, such as, analysis of the sensor data to determine the demographic information and the emotion information of users. In some embodiments, each of the neural networks may be implemented using hardware including a processor, a microprocessor (e.g., to perform or control performance of one or more operations), a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). Alternatively, in some embodiments, each of the neural networks may be implemented using a combination of hardware and software.

[0033] In an embodiment, the second electronic device 104 may detect at least a firstuser from the set of users 114 associated with the second electronic device 104. The second electronic device 104 may capture neurological data associated with at least the first user from the set of users 114. For instance, the neurological data may be physiological or neural information captured from brain and nervous system of the user. The neurological data may reflect the cognitive state or the mental state of the user. The neurological data may include signals such as brainwave patterns (e.g., EEG), neural activity, and other metrics related to how the user's brain processes information, emotions, or stimuli. The second electronic device 104 may apply a third artificial intelligence (Al) model on the neurological data to generate a mind map of a cognitive state of at least the first user from the set of users 114. In an embodiment, the third Al model (not shown in FIG. 1 ) may be similar to the first Al model 102A or the second Al model 104A. The second electronic device 104 may transmit to the first electronic device 102, the mind map of the cognitive state of at least the first user from the set of users 114.

[0034] In an embodiment, the second electronic device 104 may receive from the first electronic device 102, a first media segment from the multimedia content 110, based on the mind map of the cognitive state. The first media segment may be selected from the multimedia content, based on the mind map of the cognitive state. The second electronic device 104 may control the display device to render the first media segment to the first user.

[0035] The set of sensors 104B may include suitable logic, circuitry, and interfaces, and / or code that may be configured to capture sensor data associated with the set of users 114 based on the detection of the set of users 114 by the second electronic device 104. The set of sensors 104B may include at least one of a video sensor, a wearable sensor, a social sensor, a facial recognition sensor, an Electrodermal Activity (EDA) sensor, a heart rate monitor, an audio sensor, a temperature sensor, a gesture or posture recognitionsensor, or an environment sensor. For example, the sensor data captured by the set of sensors 104B may be associated with determining the demographic information and the emotion information associated with the set of users 114 based on the application of the second Al model 104A on the sensor data.

[0036] For example, the set of sensors 104B may detect the set of users 114 periodically at a certain time interval. The capture of the sensor data may be based on the detection of the set of users 114. The captured sensor data may be associated with attributes or metadata associated the demographic information and the emotion information associated with the set of users 114. The sensor data may be transmitted to the server 106 or to the first electronic device 102.

[0037] The server 106 may include suitable logic, circuitry, and interfaces, and / or code that may be configured to execute operations, such as data / file storage, multimedia content rendering, or the mind map rendering. In one or more embodiments, the server 106 may store the multimedia content 110 and may execute at least one operation associated with the first electronic device 102 or the second electronic device 104. The server 106 may be implemented as a cloud server and may execute operations through web applications, cloud applications, HTTP requests, repository operations, file transfer, and the like. Other example implementations of the server 106 may include, but are not limited to, a database server, a file server, a web server, a media server, an application server, a mainframe server, a Content Delivery Network (CDN) or a cloud computing server. The CDN may be a distributed system of servers strategically placed across different geographical locations to efficiently deliver digital content, such as websites, videos, images, and applications, to users. By caching and routing data through the nearest available server, a CDN reduces latency, improves load times, and enhances security by mitigating cyber threats like Distributed Denial of Service (DDoS) attacks. Forexample, a global streaming platform uses a CDN to ensure seamless video playback by delivering content from servers closest to the viewer, minimizing buffering and maximizing performance.

[0038] In at least one embodiment, the server 106 may be implemented as a plurality of distributed cloud-based resources by use of several technologies that are well known to those ordinarily skilled in the art. A person with ordinary skill in the art will understand that the scope of the disclosure may not be limited to the implementation of the server 106, the first electronic device 102 and the second electronic device 104, as three separate entities. In certain embodiments, the functionalities of the server 106 can be incorporated in its entirety or at least partially in the first electronic device 102 or the second electronic device 104 without a departure from the scope of the disclosure. In certain embodiments, the server 106 may host the database 108. Alternatively, the server 106 may be separate from the database 108 and may be communicatively coupled to the database 108.

[0039] The database 108 may include suitable logic, interfaces, and / or code that may be configured to store the multimedia content 110 or the sensor data associated with the set of users 114. For example, the sensor data may include data captured from at least one of the video sensors, the wearable sensor, the social sensor, the facial recognition sensor, the Electrodermal Activity (EDA) sensor, the heart rate monitor, the audio sensor, the temperature sensor, the gesture or posture recognition sensor, or the environment sensor. The database 108 may be stored or cached on a device, such as a server (e.g., the server 106), the first electronic device 102, or the second electronic device 104. The device storing the database 108 may be configured to receive a query for the multimedia content 110 or the sensor data. In response, the device that stores the database 108 may retrieve and provide the multimedia content 110 or the sensor data to the first electronic device 102, or to the second electronic device 104.

[0040] In some embodiments, the database 108 may be hosted on a plurality of servers stored at the same or different locations. The operations of the database 108 may be executed using hardware, including a processor, a microprocessor (e.g., to perform or control performance of one or more operations), a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). In some other instances, the database 108 may be implemented using software.

[0041] The communication network 112 may include a communication medium through which the first electronic device 102, the second electronic device 104, and the server 106 may communicate with one another. The communication network 112 may be one of a wired connection or a wireless connection. Examples of the communication network 112 may include, but are not limited to, the Internet, a cloud network, Cellular or Wireless Mobile Network (such as Long-Term Evolution and 5thGeneration (5G) New Radio (NR)), a satellite network (e.g., a network of low earth orbit satellites), a Wireless Fidelity (Wi-Fi) network, a Personal Area Network (PAN), a Local Area Network (LAN), or a Metropolitan Area Network (MAN). Various devices in the network environment 100 may be configured to connect to the communication network 112 in accordance with various wired and wireless communication protocols. Examples of such wired and wireless communication protocols may include, but are not limited to, at least one of 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 802.11 g, multi-hop communication, wireless access point (AP), device to device communication, cellular communication protocols, and Bluetooth (BT) communication protocols.

[0042] In operation, the first electronic device 102 may be configured to receive multimedia content 110 including the set of branched storylines associated with themultimedia content 110. By way of example, and not limitation, the multimedia content 110 may be a podcast, a video, an audio, a webinar, a music piece, an infographic sequence, animation, or a virtual reality (VR) experience. Further, the branched storylines may be associated with storytelling structures where the plot I story diverges at branch points, which may offer multiple paths and outcomes based on the set of users 114. The branched storylines may commonly be used in interactive fiction, video games, and / or choose-your- own-adventure books.

[0043] In an exemplary embodiment, each branched storyline of the set of branched storylines corresponds to at least one of the demographic information associated with the set of users 114 or the emotion information associated with the set of users 114. The first electronic device 102 may be configured to receive from the second electronic device 104, the demographic information and the emotion information associated with the set of users 114 of the second electronic device 104. The demographic information of the set of users 114 may include, but is not limited to, at least one of age, gender, race, ethnicity, education level, employment status, marital status, geographic location, religion, and health conditions associated with the set of users 114. Also, the emotion information of the set of users 114 may include, but is not limited to, at least one of mood, emotional triggers, emotional history, stress levels, coping mechanisms, emotional intelligence, emotional support, and mental health associated with the set of users 114. For example, in case the one or more of the first branched storylines from the set of branched storylines is horror multimedia content, the demographic information (associated with the users 114 for the horror multimedia content) may be age safe and the emotion information may be mental health of the users 114.

[0044] In another embodiment, the first electronic device 102 may receive from the second electronic device 104, a mind map of a cognitive state of at least a first user fromthe set of users 114. The mind map of the cognitive state may be a visual representation of factors and / or components that may influence the mental process of the first user from the set of users 114, or the emotional wellbeing of the first user. In an example, the mind map may be used to understand complex interactions between emotions, thought processes, perception, memory, attention, behavior, physiological factors, and environmental influences associated with the set of users 114.

[0045] The first electronic device 102 may apply the first Al model 102A on the demographic information and the emotion information. By way of example, and not limitation, the first Al model 102A may correspond to at least one of the NLP models, the neural language model, the sentiment analysis model, the emotional recognition model, the demographic analysis model, the recommendation system, or the classification machine learning (ML) model. For example, the sentiment analysis model may be used to determine and interpret the sentiment or the emotion of the set of users 114. Further, the emotional recognition model may identify emotions of the set of users 114 based on the sensors data captured from the set of sensors 104B associated with the second electronic device 104. The emotional recognition model may determine the facial expressions, the voice tones, the text, and / or the physiological signals associated with the set of users 114, based on the sensor data. Also, the emotional recognition model may leverage machine learning and deep learning techniques to accurately identify and interpret the human emotions. The demographic analysis model may analyze sensor data to infer demographic information such as the age, the gender, the income level, the education, and / or other attributes associated with the set of users 114. In another embodiment, the first electronic device 102 may apply the first Al model 102A on the mind map of the cognitive state.

[0046] The first electronic device 102 may select the set of media segments from the multimedia content 110, based on the application of the first Al model 102A. The set ofmedia segments may correspond to one or more first branched storylines from the set of branched storylines. In another embodiment, the first electronic device 102 may select a first media segment from the multimedia content 110, based on the application of the first Al model 102A on the mind map of the cognitive state.

[0047] In an embodiment, the first Al model 102A may receive the demographic information, the emotion information associated with the set of users 114, and the mind map of the cognitive state of at least a first user from the set of users 114. The first Al model 102A may analyze patterns or features within the demographic information, the emotion information, and the mind map to determine the current needs or preferences for at least the first user from the set of users 114. For example, if the first Al model 102A recognizes that the first user is feeling stressed, then the first Al model 102A may select relaxing music or calming visuals. If the first user is focused or in a learning state, the first Al model 102A may select educational or motivational content. For example, the first Al model 102A may act as a decision-making engine and map the cognitive state of the set of users 114 to the set of media segments based on an evaluation of an emotional alignment, relevance, or context.

[0048] The first Al model 102A may perform a mapping or recommendation task, that uses a machine learning techniques (such as neural networks or recommendation systems) to interpret the cognitive state the set of users 114 from the mind map, and match or rank the set of media segments in the multimedia content 110 based on relevance to the cognitive state the set of users 114. The first Al model 102A may select a matched media segment that may align with the cognitive state, emotional state or preferences of the set of users 114..

[0049] The first electronic device 102 may transmit the set of media segments to the second electronic device 104. In another embodiment, the first electronic device 102 maytransmit the first media segment to the second electronic device 104. The second electronic device 104 may be configured to control the display device (such as the display device 210A or the display device 21 OB) to render the set of media segments. In another embodiment, the first electronic device 102 may control the display device to render the first media segment for at least the first user.

[0050] The second electronic device 104 may detect the set of users 114 associated with the second electronic device 104. The second electronic device 104 may detect the set of users 114 based on predefined conditions or mechanisms that govern the activation of the set of sensors 104B. In an embodiment, the set of sensors 104B may be configured to continuously track changes in an environment (associated with the second electronic device 104) to detect the set of users 114. The changes may be associated with, but not limited to, temperature, pressure, motion sensors. In another embodiment, the second electronic device 104 may detect the set of users 114, based on an event trigger. The event trigger may be a stimuli, such as motion, sound, or light intensity crossing a threshold. In another embodiment, the second electronic device 104 may detect the set of users 114 based on at least one of a scheduled collection, an external command, or a threshold interval. For example, the scheduled collection may be associated with predefined intervals such as, but not limited to, every second, hour, or day, based on the application's requirements. The external command may be associated with instructions from the first electronic device 102 or the second electronic device 104, to initiate data collection. The external command may be such as, but not limited to, an loT controller command, user command, or the likes. The threshold interval may be associated with a particular threshold being met, such as, but not limited to, temperature exceeding a limit, detecting sound above a specific decibel, or the likes. For example, the set of users 114 may be a human audience of media content, such as a movie, displayed on the secondelectronic device 104, such as a television. In accordance with an embodiment, the second electronic device 104 may detect at least the first user from the set of users 114 associated with the second electronic device 104.

[0051] The second electronic device 104 may capture sensor data associated with the set of users 114 based on the detection of the set of users 114. The set of sensors 104B may capture the sensor data associated with the second electronic device 104. The set of sensors 104B may include at least one of the video sensors, the wearable sensor, the social sensor, the facial recognition sensor, the Electrodermal Activity (EDA) sensor, the heart rate monitor, the audio sensor, the temperature sensor, the gesture or posture recognition sensor, or the environment sensor. In accordance with an embodiment, the second electronic device 104 may capture neurological data associated with at least the first user from the set of users 114. The set of sensors 104B associated with the second electronic device 104 may capture neurological data. The set of sensors 104B for capturing the neurological data may include at least one of an Electroencephalography (EEG), a Functional Magnetic Resonance Imaging (fMRI), a Magnetoencephalography (MEG), a Near-Infrared Spectroscopy (NIRS), Wearable Sensors, or an Electromyography (EMG).

[0052] The second electronic device 104 may apply the second Al model 104A on the sensor data. By way of example, and not limitation, the second Al model 104A may corresponds to at least one of a supervised learning model, an unsupervised learning model, a semi-supervised learning model, a self-supervised learning model, a deep learning model, a reinforced learning model, or an anomaly detection model. In another embodiment, the second electronic device 104 may apply the third Al model on the neurological data.

[0053] The second electronic device 104 may determine the demographic information and the emotion information associated with the set of users 114, based on the applicationof the second Al model 104A. In another embodiment, the second electronic device 104 may generate mind map of the cognitive state of at least the first user from the set of users 114, based on the application of third Al model.

[0054] The second electronic device 104 may transmit to the first electronic device 102, the demographic information and the emotion information. In another embodiment, the second electronic device 104 may transmit to the first electronic device 102, the mind map of the cognitive state of at least the first user from the set of users 114.

[0055] The second electronic device 104 may receive from the first electronic device 102, the set of media segments of the multimedia content 110, based on the transmitted demographic information and the transmitted emotion information. The multimedia content may include the set of branched storylines, and the set of media segments may correspond to one or more first branched storylines from the set of branched storylines. In another embodiment, the second electronic device 104 may receive from the first electronic device 102, the first media segment from the multimedia content 110, based on the mind map of the cognitive state. The first media segment may be selected from the multimedia content 110, based on the mind map of the cognitive state.

[0056] The second electronic device 104 may determine a first color-grading / quality of the set of media segments of the multimedia content. Further, the second electronic device 104 may change the first color-grading / quality to a second color-grading / quality of the set of media segments based on the emotion information associated with the set of users 114. The first color-grading / quality and the second color-grading / quality may correspond to a process of enhancing color, contrast, brightness of the multimedia content 110 based on the demographic information and the emotion information associated with the set of users 114. For example, the conversion of the color-grading / quality from the first color- grading / quality to the second color-grading / quality may be associated with a technique ofanalyzing and adjusting the color palette and picture quality of media content in real-time, based on the set of users 114 emotional state and personal characteristics.

[0057] The second electronic device 104 may control the display device to render the set of media segments. Also, the second electronic device 104 may control the display device to render the set of media segments based on the second color-grading / quality. In another embodiment, the second electronic device 104 may control the display device to render the first media segment to the first user. For example, the second electronic device 104 may be controlled to display the set of media segments of the multimedia content 110 such as movie based on the user age, gender, emotions, and the likes.

[0058] In an embodiment, the second electronic device 104 may receive a user input from a second user of the set of users. The user input may be indicative of first demographic information and first emotion information of a first user of the set of users. The second electronic device 104 may transmit to the first electronic device 102, the first demographic information and the first emotion information. The second electronic device 104 may receive from the first electronic device 104, a set of first media segments of the multimedia content, based on the transmitted first demographic information and the transmitted first emotion information. The set of first media segments may corresponds to one or more second branched storylines from the set of branched storylines. The second electronic device 104 may control a display device (e.g., the display device 210B) to render the set of first media segments.

[0059] For example, the second electronic device 104 may control the display device (such as the display device 210A, the display device 210B, or the display device associated with another user device) to render a set of first media segments. For example, the rendered set of first media segments may be media segments that may be associated with gender and first emotion safe playback 606B for a first user of the set of users. Forexample, the first user may be a human-male in a sad emotional state. The second electronic device 104 may receive a request for display of the set of first media segments associated with the gender and first emotion safe playback 606B of the first user, from a second user. For example, the second user may be a human-female with a happy emotional state (herein, the human-female may want to watch the multimedia content the human-male may have watched). Further, the second electronic device 104 may transmit first demographic information (i.e. , a male user) and first emotional information (i.e. , a sad emotional state) of the first user to the first electronic device 102 and receive the set of first media segments associated with the male gender and first emotion safe playback 606B for the first user from the first electronic device 102. Further, the second electronic device 104 may control the display device (such as the display device 210A, the display device 210B, or the display device associated with another user device) to render the received set of first media segments associated with the gender and first emotion safe playback 606B for the second user.

[0060] Typically, the digital content systems primarily rely on user inputs to enhance playback experience or accelerate download speeds, focusing on technical optimizations rather than content personalization. Such systems typically do not influence the storyline or incorporate cognitive functions for path selection, which may result in a one-size-fits-all approach to content delivery. As a result, the traditional systems may often fail to tailor the viewing experience to the specific interests and emotional states of individual viewers. Thus, there is a need for a solution for dynamic adaptation of content in real-time, which may provide a bespoke and engaging user experience without constant user intervention.

[0061] In order to address the requirements, the present disclosure introduces a technique for provision of seamless and immersive viewing experience to users based on automatic storyline branching, which may be tailored to user-specific factors, such as, userbackground and emotions. Based on an incorporation of different genres and multiple endings within a single storyline, the disclosed technique may cater to a wide range of viewer preferences, such that each viewing session feels unique and engaging. The adaptive streaming capability, which adjusts content based on the viewer’s age, may ensure age-appropriate and safe content delivery, and thereby enhance a suitability of the adapted content for all audience groups. Additionally, the polymorphic nature of the story may allow for a rich variety of content, which may keep viewers intrigued and entertained with diverse narrative possibilities. Based on a combination of multiple genres together with an ability of the dynamic adaptation to the viewers’ likes and emotional states, the disclosed technique not only enhances viewer satisfaction but also maximizes engagement. The disclosed technique may not only enhance the viewer’s experience but also refine the content preparation process, by potential adjustment to elements such as background colors to boost engagement.

[0062] FIG. 2A is a block diagram that illustrates an exemplary first electronic device of FIG. 1 , in accordance with an embodiment of the disclosure. FIG. 2A is explained in conjunction with elements from FIG. 1. With reference to FIG. 2A, there is shown the first electronic device 102. The first electronic device 102 may include a circuitry 202A, a memory 204A, an input / output (I / O) device 206A, a network interface 208A, and the first Al model 102A. The input / output (I / O) device 206A may include a display device 210A.

[0063] The circuitry 202A may include suitable logic, circuitry, and / or interfaces that may be configured to execute program instructions associated with different operations to be executed by the first electronic device 102. For example, the various operations may include multimedia content reception, demographic information and emotion information reception, first Al model application, media segment selection, and media segment transmission. The circuitry 202A may include one or more processing units, which may beimplemented as a separate processor. In an embodiment, the one or more processing units may be implemented as an integrated processor or a cluster of processors that perform the functions of the one or more specialized processing units, collectively. The circuitry 202A may be implemented based on a number of processor technologies known in the art. Examples of implementations of the circuitry 202A may be an X86-based processor, a Graphics Processing Unit (GPU), a Reduced Instruction Set Computing (RISC) processor, an Application-Specific Integrated Circuit (ASIC) processor, a Complex Instruction Set Computing (CISC) processor, a microcontroller, a central processing unit (CPU), and / or other control circuits.

[0064] The memory 204A may include suitable logic, circuitry, interfaces, and / or code that may be configured to store one or more instructions to be executed by the circuitry 202. The memory 204A may be configured to store the multimedia content 110 and the sensor data associated with the set of users 114. Examples of implementation of the memory 204A may include, but are not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Hard Disk Drive (HDD), a Solid-State Drive (SSD), a CPU cache, and / or a Secure Digital (SD) card.

[0065] The I / O device 206A may include suitable logic, circuitry, interfaces, and / or code that may be configured to receive an input and provide an output based on the received input. For example, the I / O device 206A may receive the sensor data associated with the set of users 114. The I / O device 206A may also receive the multimedia content 110. The I / O device 206A may be further configured to control the display device (such as the display device 210A) to render media segments associated with the multimedia content 110. The I / O device 206A may include the display device 210A. Examples of the I / O device 206A may include, but are not limited to, a touch screen, a keyboard, a mouse, a joystick,a microphone, a haptic device, or a speaker.

[0066] The network interface 208A may include suitable logic, circuitry, interfaces, and / or code that may be configured to facilitate communication between the first electronic device 102 and the server 106 via the communication network 112. The network interface 208A may be implemented by use of various known technologies to support wired or wireless communication of the first electronic device 102 with the communication network. The network interface 208A may include, but is not limited to, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a coder-decoder (CODEC) chipset, a subscriber identity module (SIM) card, or a local buffer circuitry.

[0067] The network interface 208A may be configured to communicate via wireless communication with networks, such as the Internet, an Intranet, a wireless network, a cellular telephone network, a wireless local area network (LAN), or a metropolitan area network (MAN). The wireless communication may be configured to use one or more of a plurality of communication standards, protocols and technologies, such as Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), wideband code division multiple access (W-CDMA), Long Term Evolution (LTE), 5thGeneration (5G) New Radio (NR), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Wireless Fidelity (Wi-Fi) (such as IEEE 802.11 a, IEEE 802.11 b, IEEE 802.11 g or IEEE 802.11 n), voice over Internet Protocol (VoIP), light fidelity (Li-Fi), Worldwide Interoperability for Microwave Access (Wi-MAX), a protocol for email, instant messaging, the CDN, and a Short Message Service (SMS). The CDN may be a distributed system of servers strategically placed across different geographical locations to efficiently deliver digital content, such as websites, videos, images, and applications, to users. By caching and routing data through the nearest available server, the CDN reduces latency,improves load times, and enhances security by mitigating cyber threats like DDoS attacks. For example, the global streaming platform uses the CDN to ensure seamless video playback by delivering content from servers closest to the viewer, minimizing buffering and maximizing performance.

[0068] The display device 210A may include suitable logic, circuitry, and interfaces that may be configured to display the sensor data, and the media segment (such as the set of media segments or the first media segment) associated with the multimedia content 110 (after processing). The display device 210A may be a touch screen which may enable a user to provide a user-input via the display device 210A. The touch screen may be at least one of a resistive touch screen, a capacitive touch screen, or a thermal touch screen. The display device 210A may be realized through several known technologies such as, but not limited to, at least one of a Liquid Crystal Display (LCD) display, a Light Emitting Diode (LED) display, a plasma display, or an Organic LED (OLED) display technology, or other display devices. In accordance with an embodiment, the display device 210A may refer to a display screen of a head mounted device (HMD), a smart-glass device, a see-through display, a projection-based display, an electro-chromic display, or a transparent display. Various operations of the circuitry 202A for control of the display device (such as the display device 210A) to render the first electronic device 102 are described further, for example, in FIG. 3.

[0069] FIG. 2B is a block diagram that illustrates an exemplary second electronic device 104 of FIG. 1 , in accordance with an embodiment of the disclosure. FIG. 2B is explained in conjunction with elements from FIG. 1 and FIG. 2A. With reference to FIG. 2B, there is shown the second electronic device 104. The second electronic device 104 may include a circuitry 202B, a memory 204B, an input / output (I / O) device 206B, a network interface 208B, and the second Al model 104A. The input / output (I / O) device 206B may include adisplay device 21 OB.

[0070] The circuitry 202B may include suitable logic, circuitry, and / or interfaces that may be configured to execute program instructions associated with different operations to be executed by the second electronic device 104. For example, the various operations may include user detection, sensor data capture, second Al model application, demographic information and emotion information determination and transmission, media segment reception, and control of media segment rendering. The circuitry 202B may include one or more processing units, which may be implemented as a separate processor. In an embodiment, the one or more processing units may be implemented as an integrated processor or a cluster of processors that perform the functions of the one or more specialized processing units, collectively. The circuitry 202B may be implemented based on a number of processor technologies known in the art. Examples of implementations of the circuitry 202B may be an X86-based processor, a Graphics Processing Unit (GPU), a Reduced Instruction Set Computing (RISC) processor, an Application-Specific Integrated Circuit (ASIC) processor, a Complex Instruction Set Computing (CISC) processor, a microcontroller, a central processing unit (CPU), and / or other control circuits.

[0071] The memory 204B may include suitable logic, circuitry, interfaces, and / or code that may be configured to store one or more instructions to be executed by the circuitry 202. The memory 204B may be configured to store the multimedia content 110 and sensor data associated with the set of users 114. Examples of implementation of the memory 204B may include, but are not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Hard Disk Drive (HDD), a Solid-State Drive (SSD), a CPU cache, and / or a Secure Digital (SD) card.

[0072] The I / O device 206B may include suitable logic, circuitry, interfaces, and / or codethat may be configured to receive an input and provide an output based on the received input. For example, the I / O device 206B may receive the sensor data associated with the set of users 114. The I / O device 206B may be further configured to receive and render media segment associated with the multimedia content 110. The I / O device 206B may include the display device 21 OB. Examples of the I / O device 206B may include, but are not limited to, a touch screen, a keyboard, a mouse, a joystick, a microphone, a haptic device, or a speaker.

[0073] The network interface 208B may include suitable logic, circuitry, interfaces, and / or code that may be configured to facilitate communication between the second electronic device 104, the first electronic device 102, and the server 106 via the communication network 112. The network interface 208B may be implemented by use of various known technologies to support wired or wireless communication of the second electronic device 104 with the communication network. The network interface 208B may include, but is not limited to, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a coder-decoder (CODEC) chipset, a subscriber identity module (SIM) card, or a local buffer circuitry.

[0074] The network interface 208B may be configured to communicate via wireless communication with networks, such as the Internet, an Intranet, a wireless network, a cellular telephone network, a wireless local area network (LAN), or a metropolitan area network (MAN). The wireless communication may be configured to use one or more of a plurality of communication standards, protocols and technologies, such as Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), wideband code division multiple access (W-CDMA), Long Term Evolution (LTE), 5thGeneration (5G) New Radio (NR), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Wireless Fidelity (Wi-Fi) (such as IEEE 802.11 a, IEEE 802.11 b, IEEE802.11 g or IEEE 802.11 n), voice over Internet Protocol (VoIP), light fidelity (Li-Fi), Worldwide Interoperability for Microwave Access (Wi-MAX), a protocol for email, instant messaging, the CDN, and a Short Message Service (SMS).

[0075] The display device 21 OB may include suitable logic, circuitry, and interfaces that may be configured to display the sensor data, and the media segment (such as the set of media segments or the first media segment) associated with the multimedia content 110 (after processing). The display device 210B may be a touch screen which may enable a user to provide a user-input via the display device 210B. The touch screen may be at least one of a resistive touch screen, a capacitive touch screen, or a thermal touch screen. The display device 210B may be realized through several known technologies such as, but not limited to, at least one of a Liquid Crystal Display (LCD) display, a Light Emitting Diode (LED) display, a plasma display, or an Organic LED (OLED) display technology, or other display devices. In accordance with an embodiment, the display device 210B may refer to a display screen of a head mounted device (HMD), a smart-glass device, a see-through display, a projection-based display, an electro-chromic display, or a transparent display. Various operations of the circuitry 202B for control of rendering on the second electronic device 104 are described further, for example, in FIG. 4 and FIG. 5.

[0076] FIG. 3 is a block diagram that illustrates an exemplary processing pipeline for transmission of set of segments of multimedia content with branched storyline from the first electronic device, based on user demography and emotions, in accordance with an embodiment of the disclosure. FIG. 3 is explained in conjunction with elements from FIG. 1 , FIG. 2A, and FIG. 2B. With reference to FIG. 3, there is shown an exemplary processing pipeline 300 that illustrates exemplary operations from 302 to 312. The exemplary operations 302 to 312 may be executed by any computing system, for example, by the first electronic device 102 of FIG. 1 or by the circuitry 202A of FIG. 2A or the secondelectronic device 104 of FIG. 1 or by the circuitry 202B of FIG. 2B. The exemplary processing pipeline 300 further illustrates user information such as demographic Information 304A, emotion Information 304B, and a mind map 304C associated with the set of users 114. The user information shown in FIG. 3 is presented merely as an example and such an example should not be construed to limit the scope of the disclosure.

[0077] At 302, media content reception may be performed. The circuitry 202A may receive the multimedia content 110 including the set of branched storylines associated with the multimedia content 110. By way of example, and not limitation, the multimedia content 110 may be a podcast, a video, an audio, a webinar, a music piece, a movie, an infographic sequence, an animation, or a virtual reality (VR) experience. The branched storylines may be associated with storytelling structures where the plot I story diverges at branch points, which may offer multiple paths and outcomes based on the set of users 114. The branched storylines may commonly be used in interactive fiction, video games, and / or choose-your-own-adventure books. In an embodiment, each branched storyline of the set of branched storylines may correspond to at least one of the demographic information associated with the set of users 114 or the emotion information associated with the set of users 114. In an exemplary embodiment, the multimedia content 110 received may be a horror movie, including a first set of branched storylines, and a second set of branched storylines. The first set of branched storylines may be relevant for a normal playback, whereas the second set of branched storylines may be relevant for gender or age safe playback.

[0078] At 304, user information reception may be performed. The circuitry 202A may receive the user information from the second electronic device 104. The user information may include the demographic Information 304A, the emotion Information 304B, and the mind map 304C. The user information may include information associated with the set ofusers 114 of the second electronic device 104. In an example, the user information may be received from the second electronic device 104 (e.g., a television). The user information may indicate that a user who watches the television is an old, aged female (say, 60+ years), has an emotional state of a scared and nervous emotion, and a mind map related to highly conscious or sad psychological state.

[0079] In an embodiment, the demographic information 304A of the set of users 114 may include at least one of an age, a gender, a race, an ethnicity, an education level, an employment status, a marital status, a geographic location, a religion, and health conditions associated with the set of users. The circuitry 202A or the circuitry 202B may employ clustering algorithms to group users into meaningful segments based on shared characteristics, to allow more targeted content selection. The circuitry 202A or the circuitry 202B may employ collaborative filtering techniques to infer missing demographic attributes based on similarities between attributes of users. The circuitry 202A or the circuitry 202B may use Bayesian inference techniques to continuously update and refine user demographic profiles based on observed behaviors and content interactions.

[0080] In an embodiment, the emotion information 304B of the set of users 114 may include at least one of a mood, emotional triggers, an emotional history, stress levels, coping mechanisms, an emotional intelligence, an emotional support, and a mental health associated with the set of users 114. In some aspects, the circuitry 202A or the circuitry 202B may utilize computer vision techniques to analyze facial micro-expressions captured by the set of sensors 104B, such as, cameras, to provide real-time emotional state assessment during content consumption. The circuitry 202A or the circuitry 202B may perform a time series analysis of physiological data from wearable devices (for example, the set of sensors 104B) associated with the set of users 114, to detect trends and patterns in emotional responses over extended viewing periods. In certain implementations, thevoice data captured during content interaction may be analyzed acoustically to detect emotional valence and arousal levels.

[0081] In an embodiment, the mind map 304C of the set of users 114 may include at least one of emotions, thought processes, perception, memory, attention, behavior, physiological factors, learning, reasoning, consciousness, and environmental influences associated with the set of users 114. The mind map 304C may be a visual representation of factors and / or components that may influence the mental process or the emotional wellbeing of the set of users 114. In an embodiment, the mind map 304C of cognitive state of the set of users 114 may be determined based on the sensor data captured by the set of sensors 104B, such as, for example, but not limited to, the EEG, the fMRI, the MEG, the HRV, the EAS, a Electrodermal Activity (EDA), and GSR sensors. The circuitry 202A or the circuitry 202B may utilize association rule mining to discover patterns in content consumption behavior and identify relationships between seemingly unrelated preferences or viewing habits.

[0082] At 306, a branch point detection may be performed. The circuitry 202A is further configured to detect a branch point of the one or more first branched storylines of the set of branched storylines associated with the multimedia content 110. The set of media segments may be selected from the multimedia content 110 further based on the branch point. In an embodiment, the branch point may correspond to a media segment of the multimedia content 110 at which a storyline of the set of branched storylines splits into the one or more first branched storylines. Thus, a branch point is a frame within the multimedia content 110 where the narrative diverges into multiple potential paths. The branch points serve as decision nodes for content adaptation based on viewer characteristics and engagement levels. Branch points may occur at various granularities within the content structure. In some cases, major branch points may represent significant plot divergences,such as alternative endings or character fate decisions. In other implementations, microbranches may offer subtle variations in dialogue, pacing, or scene composition to fine-tune the viewing experience. In an example, the multimedia content 110 may be split into two branched storylines at a branch point that may be at the 23rdminute of a horror movie. A first branched storyline may include a complete version of the movie along with the climax and the end. A second branched storyline may include an incomplete version of the movie which may end at a cliffhanger.

[0083] The circuitry 202A may employ multiple techniques to detect and classify branch points. Examples of such techniques may include metadata analysis, scene transition detection, narrative structure analysis, and engagement pattern recognition. For example, content creators may embed markers indicating potential branching locations and associated viewer criteria in the multimedia content 110. Metadata analysis may be used to retrieve such markers and detect the branch points. Scene transitions may be used to detect break points in the visual flow of the multimedia content 110, by using computer vision techniques. Also, the narrative structure analysis may be performed by using various natural language processing models to analyze script content of the multimedia content 110 and detect pivotal moments suitable for branching. In addition, engagement pattern recognition may be used to optimize points for content divergence based on historical viewer interaction data.

[0084] After the branch points may be detected, the branch points may be categorized based on factors such as narrative impact, emotional intensity, and demographic relevance. This classification may be used for Al-based decision-making (for example, by the first Al model 102A) to select appropriate content paths. Examples of branch points may include a mystery series where viewers can choose which suspect to investigate further, an educational documentary that adapts its explanatory depth based on theviewer's detected comprehension level, a romantic comedy that offers alternative scenarios tailored to the viewer's relationship status, or a sports broadcast that dynamically adjusts commentary style and statistical overlay complexity based on the viewer's expertise level.

[0085] At 308, application of the first Al model may be performed. The circuitry 202A may apply the first Al model 102A on the user information including the demographic Information 304A, the emotion Information 304B, and the mind map 304C. For example, the circuitry 202A may apply the first Al model 102A to analyze demographic information 304A that includes not only age and gender but also factors such as educational background, occupation, and cultural affiliations. The emotion information 304B may encompass a wide range of emotional states, including subtle variations like mild amusement, contemplative melancholy, or cautious optimism. The mind map 304C may provide insights into the user's thought processes, cognitive associations, and decisionmaking patterns. In an embodiment, the circuitry 202A may apply the first Al model 102A on the user profile associated with the set of users 114 to determine the user information such as the demographic Information 304A, the emotion Information 304B, and the mind map 304C. For example, the user profile may be collection of data that represents the characteristics, preferences, and behaviors of the set of users 114. In a scenario involving a group viewing experience, the first Al model 102A may process collective demographic data of multiple users, such as a family unit with members of different age groups. The emotion information may reflect the varied emotional responses within the group, while the mind map data may reveal shared interests or conflicting preferences among family members.

[0086] The first Al model 102A may incorporate several specialized sub-models, each designed to handle specific aspects of the content adaptation process: For example, thefirst Al model 102A may include a natural language processing (NLP) model, a neural language model, an emotional recognition model, a demographic analysis model, a recommendation system, and a classification machine learning (ML) model. The NLP model may analyze user comments, reviews, or social media posts to gauge audience reception and preferences. The neural language model may predict user engagement levels with different narrative structures or dialogue patterns. The sentiment analysis model may evaluate the emotional impact of specific scenes or storylines on different user groups. The emotional recognition model may interpret facial expressions, voice intonations, or physiological data to assess real-time viewer reactions. The demographic analysis model may identify content preferences and viewing patterns across various demographic groups. The recommendation system may suggest personalized content paths or scene selections based on individual and collective user profiles. The classification ML model may categorize content segments according to their suitability for different viewer types or emotional states.

[0087] Such sub-models may work in concert, with outputs from one being fed as inputs to another, to determine a comprehensive profile of user preferences and optimize the content adaptation process. The first Al model 102A may also employ transfer learning techniques, to apply insights gained from one user group to enhance content selection for others with similar profiles. In some implementations, the first Al model 102A may utilize reinforcement learning algorithms to continuously refine its content selection strategies based on user feedback and engagement metrics. Such an adaptive approach may enable the first Al model 102A to improve its performance over time, which may potentially lead to more accurate content recommendations for diverse viewer groups.

[0088] At 310, media segment selection may be performed. The circuitry 202A may select a set of media segments from the multimedia content 110, based on the applicationof the first Al model 102A. Herein, the set of media segments may include at least one first media segment. However, there may be multiple media segments in the set of media segment without departure from the scope of the disclosure. In an embodiment, the circuitry 202A may select the set of media segments from the multimedia content 110, based on the application of the first Al model 102A on the demographic Information 304A, and the emotion Information 304B (and / or the mind map 304C of the cognitive state) of the user.

[0089] For instance, the first Al model 102A may select media segments with a more dynamic pace and vibrant visuals for younger viewers and choose segments with deeper character development and nuanced dialogue for older audiences. In cases where the emotion state of the viewers indicates a state of stress or anxiety, the first Al model 102A may prioritize segments with calming elements or uplifting themes.

[0090] The selected media segments typically correspond to one or more first branched storylines from the set of branched storylines. Such an approach may enable a creation of personalized narrative experiences. For example, in an interactive crime drama, the first Al model 102A may choose storylines that focus on forensic analysis for viewers with a scientific background and select more action-oriented plot threads for users who have shown a preference for high-intensity content.

[0091] The disclosed technique of dynamic user demography and emotion-based storyline selection may have numerous applications across different content types, for example, educational programming, news and documentary content, and interactive storytelling. In case of educational programming, the disclosed technique may be used to adapt a difficulty and presentation style of educational material based on a learner's progress and cognitive state. For news and documentary content, the disclosed technique may be used to select segments that provide balanced perspectives while being alignedto viewer's areas of interest. In case of interactive storytelling, the disclosed technique may be used to create unique narrative paths that respond to the emotional journey of the viewer. The customized storyline selection of the disclosed technique may be used in content distribution systems to improve user engagement and provide relevant multimedia experiences adapted in real-time to each user's unique profile, preferences, and emotional state.

[0092] In an embodiment, the circuitry 202A may be configured to apply a fourth Al model on the multimedia content 110. The fourth Al model may be a generative Al model that may be configured to create new content, such as text, images, music, or code, by learning patterns from large datasets. The generative Al model may often be based on deep learning architectures like neural networks, generate human-like outputs by predicting the next logical sequence in data. Examples of the generative Al model may include, but not limited to, language models that write essays, image-generation models that create artwork from descriptions, and Al-powered music composition tools. Further, the circuitry 202A may be configured to generate the set of media segments of the multimedia content 110 based on the application of the fourth Al model.

[0093] At 312, the media segment transmission may be performed. The circuitry 202A may transmit the media segments including the set of media segments or the first media segment to the second electronic device 104. The second electronic device 104 may further be configured to control the display device (such as the display device 210A or the display device 210B) to render the media segments to at least the first user of the set of users 114. In an embodiment, the circuitry 202A may employ adaptive streaming technologies to ensure smooth delivery of the selected media content segments. This may involve real-time quality adjustments based on network conditions and device (i.e., the second electronic device 104) capabilities. The transmission process may also includepredictive caching of potential future media segments to minimize latency in content delivery, especially at branch points where multiple narrative options are available.

[0094] FIG. 4 is a block diagram that illustrates an exemplary processing pipeline to render of set of multimedia content with branched storyline at second electronic device, based on user demography and emotions, in accordance with an embodiment of the disclosure. FIG. 4 is explained in conjunction with elements from FIG. 1 , FIG. 2A, FIG. 2B, and FIG. 3. With reference to FIG. 4, there is shown an exemplary processing pipeline 400 that illustrates exemplary operations from 402 to 418. The exemplary operations 402 to 418 may be executed by any computing system, for example, by the second electronic device 104 of FIG. 1 or by the circuitry 202A of FIG. 2A or the second electronic device 104 of FIG. 1 or by the circuitry 202B of FIG. 2B. The exemplary processing pipeline 400 further illustrates user information such as the demographic Information 304A, the emotion Information 304B, and the mind map 304C associated with the set of users 114. The user information shown in FIG. 4 is presented merely as an example and such an example should not be construed to limit the scope of the disclosure.

[0095] At 402, user detection may be performed. The circuitry 202B of the second electronic device 104 may be configured to detect the set of users 114 associated with the second electronic device 104. For instance, facial recognition algorithms may identify individual users within a group setting, while voice recognition software may distinguish between different speakers. In a smart home environment, the circuitry 202B may control the set of sensors 104B, such as a combination of cameras, microphones, and proximity sensors, to track user movements and interactions with the second electronic device 104.For example, in a family viewing scenario, the circuitry 202B may detect multiple users of different ages, and identify parents and children based on their physical characteristics and behavioral patterns.

[0096] At 404, sensor data may be captured. The circuitry 202B may capture, through the set of sensors 104B, a wide array of sensor data associated with the set of users 114 based on the detection of the set of users 114. The sensor data may include, but is not limited to, facial recognition data for user identification and emotional state analysis, audio characteristic data for voice pattern recognition and tone analysis, wearable data from smart devices such as, but not limited to, a smart watch, a mobile phone, a VR headsets, a smart jewelry, a web-enabled glasses, a headsets, or the likes, for physiological monitoring, location data to understand viewing context, facial expression data for microexpression analysis, voice analysis data for sentiment detection, eye-tracking data to measure attention and interest, physiological signals such as heart rate and skin conductance, EEG data for cognitive state assessment, HRV data for stress level evaluation, posture or movement data to gauge comfort and engagement, speech and tone data for linguistic analysis, and electrodermal activity (EDA) data for arousal level measurement. For instance, during a horror movie viewing, the circuitry 202B may simultaneously track elevated heart rates, increased skin conductance, and subtle facial micro-expressions to gauge fear responses across different users.

[0097] At 406, the second Al model may be applied. The circuitry 202B may apply the second Al model 104A on the captured sensor data. The second Al model 104A may remove noise and irrelevant data from the sensor data and standardize the sensor data format for feature or signal extraction. For example, in a noisy living room environment, the second Al model 104A may filter out ambient sounds to focus on user vocalizations. The second Al model 104A may standardize the diverse data streams into a unified format suitable for analysis. Also, the second Al model 104A may utilize a range of machine learning (ML) techniques for feature extraction and classification. Such ML techniques may include Convolutional Neural Networks (CNN), Natural Language Processing (NLP)models, clustering models, Recurrent Neural Networks (RNN), facial recognition models, multimodal models, and transformer models. The CNNs may be used for image and video analysis to recognize complex facial expressions or body language cues. The NLP models may be employed to analyze speech content and patterns and detect sarcasm or emotional undertones in user comments. The clustering models might group users with similar viewing behaviors or emotional responses. The RNNs may be used to analyze time-series data, such as changes in physiological signals throughout a viewing session. The facial recognition models may track individual user reactions over time, while the multimodal models may integrate data from multiple sensor types for a comprehensive analysis. The transformer models may be employed for context-aware interpretation of user behaviors and preferences.

[0098] At 408, user information determination may be performed. Based on the second Al model's analysis, the circuitry 202B may determine comprehensive user information associated with the set of users 114. The user information may encompass the demographic information 304A, including precise age brackets (e.g., 7-9, 13-17, 25-34, 65+), cultural backgrounds with specific regional affiliations, education levels ranging from primary education to advanced degrees, socioeconomic indicators, language preferences, and longitudinal viewing patterns across multiple content categories. For instance, the circuitry 202B might identify a household containing both first-generation immigrants and their second-generation children, each with distinct cultural reference points and content preferences. The user information may include the emotion information 304B that may further include a spectrum of affective states, and tracking complex emotions like cognitive dissonance, wistful nostalgia, moral elevation, vicarious pride, or anticipatory anxiety. The circuitry 202B may detect subtle emotional transitions, such as a viewer shifting from mild amusement to genuine empathy during a character's development arc. The userinformation may further include the mind map 304C that may construct detailed cognitive profiles of the set of users 114, mapping attention distribution across visual elements, narrative comprehension patterns, information processing speeds, critical thinking engagement, and decision-making tendencies. For example, during a science documentary viewing, the circuitry 202B may detect that senior viewers focus primarily on narrated information with sustained attention, while younger viewers engage more actively with visual demonstrations but exhibit frequent attention shifts. This may trigger adaptive content delivery that maintains the core educational material with a variation of presentation methods for different cognitive profiles.

[0099] At 410, the user information transmission may be performed. The circuitry 202B may transmit the user information (including the demographic Information 304A, the emotion Information 304B, and the mind map 304C of the cognitive state of the set of users 114) to the first electronic device 102. This transmission may include encrypted packets of the demographic information 304A, the emotion information 304B (in real-time), and dynamic updates of the mind map 304C. The circuitry 202B may employ advanced data compression techniques to efficiently transmit large volumes of sensor-derived information, for example, using adaptive bitrate streaming to optimize data transfer based on network conditions.

[0100] At 412, the media segment reception may be performed. The circuitry 202B may receive from the first electronic device 102, the set of media segments of the multimedia content 110, based on the transmitted user information. The multimedia content 110 may include the set of branched storylines, and the set of media segments may correspond to one or more first branched storylines from the set of branched storylines. For instance, in an interactive drama, the circuitry 202B may receive scenes with more emotional depth tousers who exhibit high empathy scores and provide action-oriented sequences to those who show preferences for high-energy content.

[0101] At 414, first color-grading / quality determination may be performed. The circuitry 202B may analyze the received set of media segments to determine a first color-grading and quality parameters. This process may involve an assessment of factors such as color temperature, saturation levels, contrast ratios, and overall image fidelity. For example, the circuitry 202B might identify a scene with cool, muted tones originally intended to convey a somber mood.

[0102] At 416, a color-grading / quality adjustment may be performed. Based on the emotion information 304B associated with the set of users 114, the circuitry 202B may dynamically alter the color-grading and quality of the media segments to the second color- grading / quality. This adjustment may be fine-tuned to enhance or modulate the viewers' emotional experience. For instance, if the emotion information 304B indicates heightened anxiety among viewers during a suspenseful scene, the circuitry 202B may subtly shift the color palette towards cooler tones and softer contrasts to mitigate the stress levels. Conversely, for users showing low engagement, the circuitry 202 may enhance vibrancy and contrast to stimulate interest. In a group viewing scenario, a balance may be maintained with tense tone for adult viewers with a slightly softened visual tone for younger audience members.

[0103] At 418, rendering may be controlled. The circuitry 202B may control the display device to render the set of media segments. In an embodiment, the circuitry 202B may control the display device (such as the display device 210A or the display device 210B) to render the set of media segments based on the second color-grading / quality. The circuitry 202B may manage the final rendering of the media segments, based on incorporation all previous adjustments and adaptations. This process may involve real-time renderingtechniques that seamlessly blend the adjusted color-grading and quality settings with the content narrative. The circuitry 202B may also adapt playback parameters such as frame rate or resolution based on the detected viewing conditions and user preferences. For example, in a scenario where users are detected to be viewing content on a large screen in a dimly lit room, the circuitry 202B might optimize contrast ratios and color depth to enhance the cinematic experience and thereby may ensure a visual comfort for the viewers.

[0104] FIG. 5 is a block diagram that illustrates an exemplary processing pipeline to render set of media segments of multimedia content with branched storyline, at second electronic device, based on user mind map, in accordance with an embodiment of the disclosure. FIG. 5 is explained in conjunction with elements from FIG. 1 , FIG. 2A, FIG. 2B, FIG. 3, and FIG. 4. With reference to FIG. 5, there is shown an exemplary processing pipeline 500 that illustrates exemplary operations from 502 to 516. The exemplary operations 502 to 516 may be executed by any computing system, for example, by the second electronic device 104 of FIG. 1 or by the circuitry 202A of FIG. 2A or the second electronic device 104 of FIG. 1 or by the circuitry 202B of FIG. 2B. The exemplary processing pipeline 500 further illustrates neurological data associated with the set of users 114. The neurological data shown in FIG. 5 is presented merely as an example and such an example should not be construed as limiting the disclosure.

[0105] At 502, a first user detection may be performed. The circuitry 202B may further be configured to detect at least a first user from the set of users 114 associated with the second electronic device 104, by use of the set of sensors 104B. Details related to the detection of at least the first user are explained further, for example, in FIG. 4 (at 402).

[0106] At 504, neurological data may be captured. The circuitry 202B may be further be configured to capture neurological data associated with at least the first user from the setof users 114, using the set of sensors 104B. This data acquisition may utilize an array of non-invasive neuroimaging and biosensing technologies, such as, high-density electroencephalography (EEG) with dry electrodes for comfort and ease of use, functional near-infrared spectroscopy (fNIRS) to measure localized brain activity, magnetoencephalography (MEG) for high temporal resolution neural recordings, eyetracking systems to correlate gaze patterns with neural responses, and galvanic skin response (GSR) sensors to measure autonomic nervous system activity. The circuitry 202B may employ adaptive sampling rates and real-time signal processing to optimize data quality and minimize computational overhead.

[0107] At 506, application of a third Al model may be performed. The circuitry 202B may further configured to apply the third Al model on the neurological data. The third Al model may incorporate deep learning architectures optimized for time-series analysis of neural signals, transfer learning techniques to leverage pre-trained models on large neurological datasets, attention mechanisms to focus on the most relevant aspects of the neural data, and explainable Al components to provide insights into the model's decision-making process. For instance, the third Al model may identify patterns indicative of heightened attention, emotional engagement, or cognitive load during specific scenes in a movie.

[0108] In an embodiment, the third Al model may be configured to apply at least one of a deep leaning technique, a transfer learning technique, an attention mechanism, an explainable Al technique, or the likes. The deep learning architectures may optimize the captured neurological data for analyzing time-series neural signals to determine temporal patterns. The transfer learning techniques may pre-trained on the neurological data to adapt knowledge and improve performance for the specific user context. The attention mechanisms may apply attention layers to focus on the relevant aspects of the neurological data, ensuring precise analysis. The explainable Al may provide interpretableinsights into the third Al model decision-making process. The circuitry 202B may apply the third Al model to the captured neurological data to identify patterns indicative of cognitive states, such as heightened attention, emotional engagement, the cognitive state during specific scenarios, like watching scenes in a movie, or the likes.

[0109] At 508, a mind map may be generated. The circuitry 202B may generate the mind map 304C of the cognitive state of at least the first user from the set of users 114, based on the application of third Al model on the neurological data. The mind map 304C may visualize neural activation patterns across different brain regions, represent temporal dynamics of cognitive processes during content consumption, highlight associations between content elements and emotional responses, and indicate levels of comprehension or confusion related to narrative elements. For example, when viewing an educational documentary, the mind map 304C may reveal areas of high engagement with scientific concepts versus sections where the user's attention wanes.

[0110] In an embodiment, the mind map 304C may include neural activation patterns that visualize activity across different brain regions, temporal dynamics that represents the cognitive processes evolved during content consumption, and content-emotion associations that may highlight relationships between the multimedia content 110 and the emotional information associated with the set of users. The mind map 304C may include a level of comprehension that Indicates whether the user fully understands or is confused by certain narrative elements of the multimedia content 110.

[0111] At 510, the mind map may be transmitted. The circuitry 202B may transmit to the first electronic device 102, the mind map 304C of the cognitive state of at least the first user from the set of users 114. This transmission may include encrypted packets of updates of the mind map 304C. The circuitry 202B may employ data compression techniques to efficiently transmit large volumes of sensor-derived information. Forexample, the circuitry 202B may use adaptive bitrate streaming to optimize data transfer based on network conditions.

[0112] At 512, a first media segment may be received. The circuitry 202B may receive from the first electronic device 102, the first media segment from the multimedia content 110, based on the mind map 304C of the cognitive state. The first media segment may be selected from the multimedia content 110, based on the mind map 304C of the cognitive state. For example, in case of an educational content, the first user may be presented with a theory of a certain topic. In such a scenario, if the mind map 304C indicates that the first user is in a confused state or is in an uncomfortable state of mind with the respect to topic, the first media segment may include content that may explain the topic in simpler language or with more examples. However, if the mind map 304C indicates that the first user has lower cognitive load and understands the concept well, the first media segment may present advanced concepts or difficult questions on the topic.

[0113] At 514, rendering may be controlled. The circuitry 202B may control the display device to render the first media segment to the first user. In an embodiment, the circuitry 202B may control the display device (such as the display device 210A or the display device 210B) to render the first media segment to the first user. The received first media segment may be presented to the user. This may be suited to the first user’s current cognitive state. The cognitive state of the first user may be continuously monitored and new media segments (associated with different content branching) may be retrieved for the first user based on the updated cognitive state.

[0114] FIG. 6 is a diagram that illustrates an exemplary scenario of branched storyline, in accordance with an embodiment of the disclosure. FIG. 6 is described in conjunction with elements from FIG. 1 , FIG. 2A, FIG. 2B, FIG. 3, FIG. 4, and FIG. 5. With reference to FIG. 6, there is shown exemplary multimedia content (such as, the multimedia content110) with a set of branched storylines 600. FIG. 6 shows a normal playback 602A, an age safe playback 602B, a gender-based playback 604A, an age and gender safe playback 604B, a first emotion based playback 606A, an age, gender and first emotion safe playback 606B, a second emotion based playback 608A, an age, gender and second emotion safe playback 608B, a third emotion based playback 61 OA, an age, gender and third emotion safe playback 61 OB, and a branch point 612.

[0115] The first electronic device 102 may receive the multimedia content 110 including the set of branched storylines 600 associated with the multimedia content 110. The set of branched storylines 600 may provide different perspectives and renditions of the multimedia content 110. The set of branched storylines 600 may correspond to one or more first branched storylines. The one or more first branched storylines may start from the branch point 612.

[0116] For example, the set of branched storylines 600 may be woven together in such a way that the first user from the set of users 114 may involuntarily enjoy the set of media segments such as a particular stream while the first user watches the multimedia content 110. The first electronic device 102 may distribute the multimedia content 110 dynamically and adapt the multimedia content 110 rendered to the set of users 114. The multimedia content 110 may be adapted based on the demographic information 304A (such as gender and / or age), the emotion information 304B (such as emotions expressed by the first user), and / or the mind map 304C (such as a user experience measured using cognitive viewer analytics). The second electronic device 104 may provide the demographic information 304A, the emotion information 304B, and / or the mind map 304C that may be utilized to select the set of media segments from the multimedia content 110.

[0117] For instance, a multimedia content maker may create the multimedia content with a set of set of branched storylines tailored to different user profiles of the set of users 114.An Al model (such as the first Al model 102A, the second Al model 104A, or the third Al model) may recommend a branch point (such as, the branch point 612) for each of the branched storyline of the set of branched storylines 600 to the multimedia content maker The first electronic device 102 may dynamically adapt the multimedia content path based on the user information received from the second electronic device 104. Further, the first electronic device 102 may select and transmit the set of media segments from the multimedia content to the second electronic device 104. Thus, the second electronic device 104 may control the display device (such as the display device 210A or the display device 210B) to render the set of media segments.

[0118] As shown in FIG. 6, content paths (including the set of media segments) may be selected based on the viewer's gender, age group, and emotional state. For example, during a suspenseful scene, if the emotion state is detected as heightened anxiety in younger viewers, the first electronic device 102 may select a less intense narrative branch at the next branch point 612.

[0119] The normal playback 602A may represent a default content path when no specific adaptations are required. However, as the viewer progresses through the content, various viewer characteristics and emotional responses may be continuously tracked, potentially triggering transitions to different playback modules via the branch points 612.

[0120] This branching structure may allow for a highly personalized viewing experience. For instance, the content may adapt its complexity based on the viewer's age, adjust character interactions based on detected emotional responses, or modify storylines to align with gender-based preferences identified by the Al models (e.g., the first Al model 102A and the second Al model 104A).

[0121] The first electronic device 102 may receive the multimedia content 110 specifically designed with multiple narrative branches to accommodate this adaptiveplayback structure. The first electronic device 102 may then deliver the selected media segments to the second electronic device 104 for rendering, to create a seamless, personalized viewing experience that responds to the viewer's demographic profile and emotional journey throughout the content.

[0122] In an embodiment, the second electronic device 104 may control the display device (such as the display device 210A, the display device 21 OB, or the display device associated with another user device) to render the set of media segments. For example, the rendered set of media segments may be media segments that may be associated with gender and first emotion safe playback 606B for a set of first users. For example, the set of first users may be a human-male in a sad emotional state. Further, the second electronic device 104 may store the set of media segments associated with the gender and first emotion safe playback 606B for the first set of users. Further, the second electronic device 104 may receive a request for display of the set of media segments associated with the gender and first emotion safe playback 606B of the first set of users, from a second set of users. For example, the second set of users may be a human-female with a happy emotional state (Herein, the human-female may want to watch the multimedia content the human-male may have watched). Further, the second electronic device 104 may control the display device (such as the display device 210A, the display device 210B, or the display device associated with another user device) to render the stored set of media segments associated with the gender and first emotion safe playback 606B for the second set of users.

[0123] FIG. 7 is a flowchart that illustrates operations of an exemplary method for transmission of set of media segments of multimedia content with branched storyline from first electronic device, based on user demography and emotions, in accordance with an embodiment of the disclosure. FIG. 7 is described in conjunction with elements from FIG.1 , FIG. 2A, FIG. 2B, FIG. 3, FIG. 3, FIG. 4, FIG. 5 and FIG. 6. With reference to FIG. 7, there is shown a flowchart 700. The flowchart 700 may include operations from 702 to 712 and may be implemented by the first electronic device 102 of FIG. 1 or by the circuitry 202A of FIG. 2A. The flowchart 700 may start at 702 and proceed to 704.

[0124] At 704, multimedia content including a set of branched storylines associated with the multimedia content may be received. The circuitry 202A may be configured to receive the multimedia content 110 including the set of branched storylines associated with the multimedia content 110. Each branched storyline of the set of branched storylines corresponds to at least one of the demographic information associated with the set of users or the demographic information and the emotion information associated with the set of users 114. Details related to receiving the multimedia content are provided, for example, at 302, in FIG. 3.

[0125] At 706, demographic information and emotion information associated with a set of users of a second electronic device may be received from the second electronic device. The circuitry 202A may be configured to receive the demographic information 304A and the emotion information 304B associated with the set of users 114 from the second electronic device 104. Details related to the demographic information and the emotion information associated with the set of users of the second electronic device are provided, for example, at 304, in FIG. 3.

[0126] At 708, a first artificial intelligence (Al) model may be applied on the demographic information and the emotion information. The circuitry 202A may be configured to apply the first Al model 102A on the demographic information 304A and the emotion information 304B associated with the set of users 114. For example, the first Al model 102A may be applied on the demographic information and the emotion information upon detection of a branch point of the storyline. Details related to application of the first Al model are provided,for example, at 308, in FIG. 3.

[0127] At 710, a set of media segments may be selected from the multimedia content, based on the application of the first Al model, where the set of media segments may correspond to one or more of first branched storylines from the set of branched storylines. The circuitry 202A may be configured to select a set of media segments from the multimedia content 110, based on the application of the first Al model 102A. The set of media segments may correspond to one or more of a first branched storylines from the set of branched storylines. Also, the selection of the set of media segment may be associated with the detection of the branch point of the one or more first branched storylines of the set of branched storylines by the first electronic device 102 may. Details related to selection of the set of media segments are provided, for example, at 310, in FIG. 3.

[0128] In an embodiment, the circuitry 202A may be configured to apply a fourth Al model on the multimedia content 110. The fourth Al model may be a generative Al model that may be configured to create new content, such as text, images, music, or code, by learning patterns from large datasets. The generative Al model may often be based on deep learning architectures like neural networks, generate human-like outputs by predicting the next logical sequence in data. Examples of the generative Al model may include, but not limited to, language models that write essays, image-generation models that create artwork from descriptions, and Al-powered music composition tools. Further, the circuitry 202A may be configured to generate the set of media segments of the multimedia content 110 based on the application of the fourth Al model.

[0129] At 712, the set of media segments may be transmitted to the second electronic device. The circuitry 202A may be configured to transmit the set of media segments to the second electronic device 104. Details related to transmission of the set of media segments are described further in, for example, at 312, in FIG. 3. Control may pass to end.

[0130] Although the flowchart 700 is illustrated as discrete operations, such as, 704, 706, 708, 710, and 712, the disclosure is not so limited. Accordingly, in certain embodiments, such discrete operations may be further divided into additional operations, combined into fewer operations, or eliminated, depending on the implementation without detracting from the essence of the disclosed embodiments.

[0131] FIG. 8 is a flowchart that illustrates operations of an exemplary method to render set of media segments of multimedia content with branched storyline at second electronic device, based on user demography and emotions, in accordance with an embodiment of the disclosure. FIG. 8 is described in conjunction with elements from FIG. 1 , FIG. 2A, FIG. 2B, FIG. 3, FIG. 4, FIG. 5, FIG. 6, and FIG. 7. With reference to FIG. 8, there is shown a flowchart 800. The flowchart 800 may include operations from 802 to 816 and may be implemented by the second electronic device 104 of FIG. 1 or by the circuitry 202B of FIG. 2B. The flowchart 800 may start at 802 and proceed to 804.

[0132] At 804, a set of users associated with a second electronic device may be detected. The circuitry 202B may be configured to detect the set of users 114 associated with the second electronic device 104. Details related to detection of the set of users are provided, for example, at 402, in FIG. 4.

[0133] At 806, sensor data associated with the set of users may be captured based on detection of the set of users. The circuitry 202B of the second electronic device 104 may be configured to capture the sensor data 404 associated with set of users 114 based on detection of set of users 114. Details related to the sensor data are provided, for example, at 404, in FIG. 4.

[0134] At 808, a second artificial intelligence (Al) model may be applied on the sensor data. The circuitry 202B may be configured to apply the second Al model 104A on the sensor data 404. For example, the second Al model 104A may be applied to informationrelated to user’s cognitive state / emotions I demography that may be captured from the set of sensors 104B. Details related to the application of the second Al model are described, for example, at 406, in FIG. 4.

[0135] At 810, demographic information and emotion information associated with the set of users may be determined based on the application of the second Al model. The circuitry 202B of the second electronic device 104 may be configured to determine the demographic information 304A and the emotion information 304B associated with set of users 114 based on the application of the second Al model 104A. Details related to the detection of the demographic information and the emotion information are described further, for example, at 408, in FIG. 4.

[0136] At 812, the demographic information and the emotion information may be transmitted to a first electronic device. The circuitry 202B may be configured transmit the demographic information 304A and the emotion information 304B to the first electronic device 102. Details related to transmission of the demographic information and the emotion information (user information) are described further, for example, at 410, in FIG. 4.

[0137] At 814, a set of media segments from the multimedia content may be received from the first electronic device, based on the transmitted demographic information and the emotion information, where the multimedia content may include the set of branched storylines, and the set of media segments may correspond to one or more first branched storylines from the set of branched storylines. The circuitry 202B may be configured to receive the set of media segments of multimedia content 110 from the first electronic device 102, based on the transmitted demographic information and emotion information. The multimedia content 110 may include the set of branched storylines, and the set of media segments may correspond to one or more first branched storylines from the set ofbranched storylines. Details related to the set of media segments reception are described further, for example, at 412, in FIG. 4.

[0138] At 816, rendering of the set of media segments to users may be controlled. The circuitry 202B may be configured to control the display device 210A to render the set of media segments to users from the set of users 114. The rendering of set of media segments may include display of branched storylines based on the transmitted demographic information and the emotion information. Details related to the control the display device to render the set of media segments may be provided, for example, at 418, in FIG. 4. Control may pass to end.

[0139] Although the flowchart 800 is illustrated as discrete operations, such as, 804, 806, 808, 810, 812, 814, and 816, the disclosure is not so limited. Accordingly, in certain embodiments, such discrete operations may be further divided into additional operations, combined into fewer operations, or eliminated, depending on the implementation without detracting from the essence of the disclosed embodiments.

[0140] Various embodiments of the disclosure may provide a non-transitory computer- readable medium and / or storage medium having stored thereon, computer-executable instructions executable by a machine and / or a computer to operate an electronic device (for example, the first electronic device 102 of FIG. 1 ). Such instructions may cause the first electronic device 102 to perform operations that may include reception of multimedia content (e.g., the multimedia content 110) including a set of branched storylines associated with the multimedia content 110. The operations may further include reception, from a second electronic device (e.g., the second electronic device 104), demographic information and emotion information associated with a set of users (e.g., the set of users 114) of the second electronic device 104. The operations may further include application of a first artificial intelligence (Al) model (e.g., the first Al model 102A) on the demographicinformation and the emotion information. The operations may further include selection of a set of media segments from the multimedia content 110, based on the application of the first Al model 102A. The set of media segments corresponds to one or more first branched storylines from the set of branched storylines. The operations may further include transmission of the set of media segments to the second electronic device 104. The second electronic device 104 may be configured to control the display device to render the set of media segments.

[0141] Various embodiments of the disclosure may provide a non-transitory computer- readable medium and / or storage medium having stored thereon, computer-executable instructions executable by a machine and / or a computer to operate an electronic device (for example, the second electronic device 104 of FIG. 1 ). Such instructions may cause the second electronic device 104 to perform operations that may include detection of a set of users (e.g., the set of users 114) associated with the second electronic device 104. The operations may further include capture of sensor data associated with the set of users 114 based on the detection of the set of users 114. The operations may further include application of a second artificial intelligence (Al) model (e.g., the second Al model 104A) on the sensor data. The operations may further include determination of demographic information and emotion information associated with the set of users 114, based on the application of the second Al model 104A. The operations may further include transmission, to a first electronic device (e.g., the first electronic device 102), of the demographic information, and the emotion information. The operations may further include reception, from the first electronic device 102, of a set of media segments of the multimedia content 110, based on the transmitted demographic information and the transmitted emotion information. The multimedia content 110 may include a set of branched storylines, and the set of media segments corresponds to one or more first branched storylines from the setof branched storylines. The operations may include control of rendering of the set of media segments.

[0142] Exemplary aspects of the disclosure may provide an electronic device (such as, the first electronic device 102 of FIG. 1 ) that includes circuitry (such as, the circuitry 202A). The circuitry 202A may be configured to receive multimedia content (e.g., the multimedia content 110) including a set of branched storylines associated with the multimedia content 110. The circuitry 202A may receive from a second electronic device (e.g., the second electronic device 104), the demographic information and emotion information associated with a set of users (e.g., the set of users 114) of the second electronic device 104. The circuitry 202A may apply a first Al model (e.g., the first Al model 102A) on the demographic information and the emotion information. The circuitry 202A may select a set of media segments from the multimedia content 110, based on the application of the first Al model 102A. The set of media segments may correspond to one or more first branched storylines from the set of branched storylines. The circuitry 202A may transmit the set of media segments to the second electronic device 104. The second electronic device 104 may be configured to control the display device to render the set of media segments.

[0143] In an embodiment, each branched storyline of the set of branched storylines may correspond to at least one of the demographic information associated with the set of users or the emotion information associated with the set of users 114.

[0144] In an embodiment, the demographic information of the set of users may comprise at least one of an age, a gender, a race, an ethnicity, a user profile indicative of an education level, an employment status, a marital status, a geographic location, a religion, and health conditions associated with the set of users 114.

[0145] In an embodiment, the emotion information of the set of users may comprise at least one of a mood, emotional triggers, an emotional history, stress levels, copingmechanisms, an emotional intelligence, an emotional support, and a mental health associated with the set of users 114.

[0146] In an embodiment, the circuitry 202A is further configured to detect a branch point of the one or more first branched storylines of the set of branched storylines associated with the multimedia content 110. The set of media segments is selected from the multimedia content further based on the branch point.

[0147] In an embodiment, the branch point corresponds to a media segment of the multimedia content 110 at which a storyline of the set of branched storylines splits into the one or more first branched storylines.

[0148] In an embodiment, the first Al model 102A corresponds to at least one of a natural language processing (NLP) model, a neural language model, a sentiment analysis model, an emotional recognition model, a demographic analysis model, a recommendation system, or a classification machine learning (ML) model.

[0149] In an embodiment, the circuitry 202A may be further configured to receive from the second electronic device 104, a mind map of a cognitive state of at least a first user from the set of users 114. The circuitry 202A is further configured to apply the first Al model 102A on the mind map of the cognitive state. The circuitry 202A is further configured to select a first media segment from the multimedia content, based on the application of the first Al model 102A on the mind map of the cognitive state. The circuitry 202A may be further configured to transmit the first media segment to the second electronic device 104. The second electronic device 104 may be configured to control the display device to render the first media segment for at least the first user.

[0150] Exemplary aspects of the disclosure may provide an electronic device (such as, the second electronic device 104 of FIG. 1 ) that includes circuitry (such as, the circuitry 202B). The circuitry 202B may be configured to detect a set of users (e.g., the set users114) associated with the second electronic device 104. The circuitry 202B may capture sensor data associated with the set of users 114 based on the detection of the set of users 114. The circuitry 202B may apply a second artificial intelligence (Al) model (e.g., the second Al model 104A) on the sensor data. The circuitry 202B may determine demographic information and emotion information associated with the set of users 114, based on the application of the second Al model 104A. The circuitry 202B may transmit to a first electronic device (e.g., the first electronic device 102), the demographic information, and the emotion information. The circuitry 202B may receive from the first electronic device 102, a set of media segments of multimedia content (e.g., the multimedia content 110), based on the transmitted demographic information and the transmitted emotion information. The multimedia content 110 may include a set of branched storylines, and the set of media segments corresponds to one or more first branched storylines from the set of branched storylines. The circuitry 202B may control a rendering of the set of media segments.

[0151] In an embodiment, the circuitry 202B may be further configured to determine a first color-grading / quality of the set of media segments of the multimedia content 110. The circuitry 202B may be configured to change the first color-grading / quality to a second color- grading / quality of the set of media segments based on the emotion information associated with the set of users 114, and control the display device to render the set of media segments based on the second color-grading / quality.

[0152] In an embodiment, the sensor data is captured by a set of sensors (e.g., the set of sensors 104B) associated with the second electronic device 104, and the set of sensors 104B may comprise at least one of a video sensor, a wearable sensor, a social sensor, a facial recognition sensor, an Electrodermal Activity (EDA) sensor, a heart rate monitor, anaudio sensor, a temperature sensor, a gesture or posture recognition sensor, or an environment sensor.

[0153] In an embodiment, the second Al model 104A may correspond to at least one of a supervised learning model, an unsupervised learning model, a semi-supervised learning model, a self-supervised learning model, a deep learning model, a reinforced learning model, or an anomaly detection model.

[0154] In an embodiment, the circuitry 202B may be further configured to detect at least a first user from the set of users 114 associated with the second electronic device 104. The circuitry 202B may be further configured to capture neurological data associated with at least the first user from the set of users 114. The circuitry 202B may be further configured to apply a third artificial intelligence (Al) model on the neurological data. The circuitry 202B may be further configured to generate a mind map of a cognitive state of at least the first user from the set of users 114, based on the application of third Al mode. The circuitry 202B may transmit to the first electronic device 102, the mind map of the cognitive state of at least the first user from the set of users 114.

[0155] In an embodiment, the circuitry 202B may be further configured to receive from the first electronic device 102, a first media segment from the multimedia content 110, based on the mind map of the cognitive state. The first media segment may be selected from the multimedia content 110, based on the mind map of the cognitive state. The circuitry 202B may be further configured to control a rendering of the first media segment to the first user.

[0156] In an embodiment, the circuitry 202B of the second electronic device 104 may receive a user input from a second user of the set of users. The user input may be indicative of first demographic information and first emotion information of a first user of the set of users. The circuitry 202B of the second electronic device 104 may transmit tothe first electronic device 102, the first demographic information and the first emotion information. The circuitry 202B of the second electronic device 104 may receive from the first electronic device 104, a set of first media segments of the multimedia content, based on the transmitted first demographic information and the transmitted first emotion information. The set of first media segments may corresponds to one or more second branched storylines from the set of branched storylines. The circuitry 202B of the second electronic device 104 may control a display device (e.g., the display device 21 OB) to render the set of first media segments.

[0157] The present disclosure may also be positioned in a computer program product, which comprises all the features that enable the implementation of the methods described herein, and which when loaded in a computer system is able to conduct these methods. Computer program, in the present context, means any expression, in any language, code or notation, of a set of instructions intended to cause a system with information processing capability to perform a particular function either directly, or after either or both of the following: a) conversion to another language, code or notation; b) reproduction in a different material form.

[0158] While the present disclosure is described with reference to certain embodiments, it will be understood by those skilled in the art that various changes may be made, and equivalents may be substituted without departure from the scope of the present disclosure. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departure from its scope. Therefore, it is intended that the present disclosure is not limited to the embodiment disclosed, but that the present disclosure will include all embodiments that fall within the scope of the appended claims.

Claims

CLAIMSWhat is claimed is:1 . A first electronic device, comprising: circuitry configured to: receive multimedia content including a set of branched storylines associated with the multimedia content; receive, from a second electronic device, demographic information and emotion information associated with a set of users of the second electronic device; apply a first artificial intelligence (Al) model on the demographic information and the emotion information; select a set of media segments from the multimedia content, based on the application of the first Al model, wherein the set of media segments corresponds to one or more first branched storylines from the set of branched storylines; and transmit the set of media segments to the second electronic device, wherein the second electronic device is configured to control a display device to render the set of media segments.

2. The first electronic device according to claim 1 , wherein each branched storyline of the set of branched storylines corresponds to at least one of the demographic information associated with the set of users or the emotion information associated with the set of users.

3. The first electronic device according to claim 1 , wherein the demographic information of the set of users comprises at least one of an age, a gender, a race, an ethnicity, auser profile indicative of an education level, an employment status, a marital status, a geographic location, a religion, and health conditions associated with the set of users.

4. The first electronic device according to claim 1 , wherein the emotion information of the set of users comprises at least one of a mood, emotional triggers, an emotional history, stress levels, coping mechanisms, an emotional intelligence, an emotional support, and a mental health associated with the set of users.

5. The first electronic device according to claim 1 , the first electronic device further be configured to: detect a branch point of the one or more first branched storylines of the set of branched storylines associated with the multimedia content, wherein the set of media segments is selected from the multimedia content further based on the branch point.

6. The first electronic device according to claim 5, wherein the branch point corresponds to a media segment of the multimedia content at which a storyline of the set of branched storylines splits into the one or more first branched storylines.

7. The first electronic device according to claim 1 , wherein the first Al model corresponds to at least one of a natural language processing (NLP) model, a neural language model, a sentiment analysis model, an emotional recognition model, a demographic analysis model, a recommendation system, or a classification machine learning (ML) model.

8. The first electronic device according to claim 1 , wherein the circuitry is further configured to:receive, from the second electronic device, a mind map of a cognitive state of at least a first user from the set of users; apply the first Al model on the mind map of the cognitive state; select a first media segment from the multimedia content, based on the application of the first Al model on the mind map of the cognitive state; and transmit the first media segment to the second electronic device, wherein the second electronic device is configured to control the display device to render of the first media segment for at least the first user.

9. A second electronic device, comprising: circuitry configured to: detect a set of users associated with the second electronic device; capture sensor data associated with the set of users based on the detection of the set of users; apply a second artificial intelligence (Al) model on the sensor data; determine demographic information and emotion information associated with the set of users, based on the application of the second Al model; transmit, to a first electronic device, the demographic information and the emotion information; receive, from the first electronic device, a set of media segments of the multimedia content, based on the transmitted demographic information and the transmitted emotion information, wherein the multimedia content includes a set of branched storylines, and the set of media segments corresponds to one or more first branched storylines from the set of branched storylines; and control a display device to render the set of media segments.

10. The second electronic device according to claim 9, wherein the circuitry is further configured to: determine a first color-grading / quality of the set of media segments of the multimedia content; change the first color-grading / quality to a second color-grading / quality of the set of media segments based on the emotion information associated with the set of users; and control a display device to render the set of media segments based on the second color-grading / quality.11 . The second electronic device according to claim 9, wherein the sensor data is captured by a set of sensors associated with the second electronic device, and the set of sensors comprises at least one of a video sensor, a wearable sensor, a social sensor, a facial recognition sensor, an Electrodermal Activity (EDA) sensor, a heart rate monitor, an audio sensor, a temperature sensor, a gesture or posture recognition sensor, or an environment sensor.

12. The second electronic device according to claim 9, wherein the second Al model corresponds to at least one of a supervised learning model, an unsupervised learning model, a semi-supervised learning model, a self-supervised learning model, a deep learning model, a reinforced learning model, or an anomaly detection model.

13. The second electronic device according to claim 9, wherein the circuitry is further configured to:detect at least a first user from the set of users associated with the second electronic device; capture neurological data associated with at least the first user from the set of users; apply a third artificial intelligence (Al) model on the neurological data; generate a mind map of a cognitive state of at least the first user from the set of users, based on the application of third Al model; and transmit, to the first electronic device, the mind map of the cognitive state of at least the first user from the set of users.

14. The second electronic device according to claim 13, wherein the circuitry is further configured to: receive, from the first electronic device, a first media segment from the multimedia content, based on the mind map of the cognitive state, wherein the first media segment is selected from the multimedia content, based on the mind map of the cognitive state; and control a display device to render of the first media segment to the first user.

15. The second electronic device according to claim 9, wherein the circuitry is further configured to: receive a user input from a second user of the set of users, wherein the user input is indicative of first demographic information and first emotion information of a first user of the set of users; transmit, to the first electronic device, the first demographic information and the first emotion information;receive, from the first electronic device, a set of first media segments of the multimedia content, based on the transmitted first demographic information and the transmitted first emotion information, wherein the set of first media segments corresponds to one or more second branched storylines from the set of branched storylines; and control a display device to render the set of first media segments.

16. A method, comprising: in a first electronic device: receiving multimedia content including a set of branched storylines associated with the multimedia content; receiving, from a second electronic device, demographic information and emotion information associated with a set of users of the second electronic device; applying a first artificial intelligence (Al) model on the demographic information and the emotion information; selecting a set of media segments from the multimedia content, based on the application of the first Al model, wherein the set of media segments corresponds to one or more first branched storylines from the set of branched storylines; and transmitting the set of media segments to the second electronic device, wherein the second electronic device is configured to control a display device to render of the set of media segments.

17. The method according to claim 16, further comprising:detecting a branch point of the one or more first branched storylines of the set of branched storylines associated with the multimedia content, wherein the set of media segments is selected from the multimedia content further based on the branch point.

18. The method according to claim 17, wherein the branch point corresponds to a media segment of the multimedia content at which a storyline of the set of branched storylines splits into the one or more first branched storylines.

19. The method according to claim 16, wherein the demographic information of the set of users comprises at least one of an age, a gender, a race, an ethnicity, a user profile indicative of an education level, an employment status, a marital status, a geographic location, a religion, and health conditions associated with the set of users, and the emotion information of the set of users comprises at least one of a mood, emotional triggers, an emotional history, stress levels, coping mechanisms, an emotional intelligence, an emotional support, and a mental health associated with the set of users.

20. The method according to claim 16, further comprising: receiving, from the second electronic device, a mind map of a cognitive state of at least a first user from the set of users; applying the first Al model on the mind map of the cognitive state; selecting a first media segment from the set of media segments, based on the application of the first Al model on the mind map of the cognitive state; andtransmitting the first media segment to the second electronic device, wherein the second electronic device is configured to control a display device to render the first media segment for at least the first user.

Images