Abstract: Provided is a method for predicting emotion of a user by an electronic device. The method includes receiving, by the electronic device, a user context, a device context and an environment context from the electronic device and one or more other electronic device connected to the electronic device and determining, by the electronic device, a combined representation of the user context, the device context and the environment context. The method also includes determining, by the electronic device, a plurality of user characteristics based on the combined representation of the user context, the device context and the environment context; and predicting, by the electronic device, an emotion of the user based on the combined representation of the user context, the device context, the environment context and the plurality of user characteristics.

Abstract: Embodiments herein provide a method for managing private content by an electronic device (100). The method includes: displaying content on a screen of the electronic device (100), wherein the content is stored in a non-secure folder of the electronic device (100) and extracting a feature(s) of the displayed content to categorize the displayed content using a machine learning (ML) model. Further, the method includes categorizing the displayed content into the private content or public content based on globally objectionable private content and user-specific objectionable private content. Further, the method includes displaying a recommendation on the screen of the electronic device (100) for transferring the displayed content into a secure folder of the electronic device (100), detecting an event to transfer the private content into the secure folder of the electronic device (100), and automatically transferring the private content from the non-secure folder to the secure folder of the electronic device (100).

Abstract: The present disclosure is related to the field of digital communication and provides a method and system for providing personalized multimodal objects in real-time. An object predicting system receives a text input from at least one application installed in a user device associated with a user. Thereafter, the object predicting system determines an intent of the user by analyzing the text input, which is then correlated with contextual data to generate a query. Subsequently, the object predicting system performs a unified search in a universal database, based on the query, wherein the universal database comprises multimodal data. Further, a plurality of multimodal objects predicted in response to the unified search are ranked based on at least one of the contextual data and user preferences. Finally, at least one of the predicted plurality of multimodal objected related to the text input are provided to the user based on the ranking.

Abstract: An apparatus and method for generating a customized content are provided. An apparatus for generating a customized content, may include: at least one memory configured to store one or more instructions; at least one processor configured to execute the one or more instructions to: (1) obtain an input from a user; (2) detect, from the input, at least one feature and modality of the input among a plurality of modalities comprising a text format, a sound format, a still image format, and a moving image format; (3) determine a mode of the customized content, from a plurality of modes, based on the at least one feature and the modality of the input, the plurality of modes including an image mode and a text mode; and (4) generate the customized content based on the determined mode, and a display configured to display the customized content.

Abstract: A method for determining sensitivity-based bias of text includes detecting an input action performed by a user from a plurality of actions, wherein the plurality of actions comprises typing one or more words on a virtual keyboard of a user device and accessing readable content on the user device. When the input action is accessing the readable content on the user device, determining the readable content to be insensitive by parsing the readable content and feeding the parsed readable content to a machine learning (ML) model, wherein the ML model is trained with insensitive datasets of an adversarial database, and presenting a first alert message on the user device before displaying the readable content completely on the user device when the readable content is determined to be insensitive.