Methods and systems for sharing data between electronic devices

Abstract

Methods (1100, 1100A) and systems (300, 300A) for sharing data between electronic devices including a sender device 302 and a receiver device 304. The system (300) comprises a context module (309) configured to receive, by the sender device (302), device information and contextual information associated with the receiver devices (304), a context deriving module (310) configured for deriving, using machine learning models, a context for the receiver device (304) based on the received device and contextual information, a classifying module (320) configured to classify, by the sender device (302), data stored at the sender device (302) by selecting data that matches the derived context and is supported by the receiver device (304) based on the derived context, and a storing module (330) configure to generate, by the sender device (302), one or more sharing folders based on the classified data. Subsequently, the system (300) also includes a mounting module (340) configured to mount the one or more sharing folders on the one or more receiver devices (304).

Description

METHODS AND SYSTEMS FOR SHARING DATA BETWEEN ELECTRONIC DEVICES

[0001] The present disclosure generally relates to the field of electronic devices, and more particularly, the present disclosure relates to a method and a system for sharing data between the electronic devices.

[0002] With advancements in technology, data such as pictures, videos, and audio is being generated at a tremendous rate. Users are generating such data through devices such as tablets, mobile phones, cameras, laptop computers and the like, by performing actions such as capturing photos, videos, curating audio playlists, and downloading and sharing documents. Furthermore, sometimes, such generated data needs to be shared among different users and / or different devices.

[0003] Figures 1-2 of the prior art illustrate conventional solutions for sharing data, such as Storage Share Service (SSS) allows mounting of a whole Internal Storage (IS) including such generated data to enable all the nearby client devices to access the IS. However, as the whole of IS is mounted, a process to identify required data from the IS by a client of the nearby mounted client device becomes time consuming. Moreover, as the whole IS is shared with all the nearby client devices, security of data stored at such IS (also referred to as mount output) becomes vulnerable. Further, privacy of data at the server, especially sensitive personal data, is also a concern. The conventional solutions fails to address the above-mentioned challenges in data sharing. Further, there may be privacy and security concerns of the server and the client device data when all of the IS data is shared. Furthermore, the current solutions do not consider the device requirement and capabilities of the client devices while sharing the IS data.

[0004] Another conventional solution may allow sharing selective data from the IS of the server device. Another solution may propose sharing data with different recipients and / or recipient groups based on file type, recently shared file, and the choice of the sharing user. Such sharing may often require manual efforts for data classification before sharing, which is cumbersome and may not be able to serve all the client devices optimally. Further, client devices may still end up downloading unnecessary data which may not be useful, which may not be supported, or which was not intended to be shared being private. As a consequence, network bandwidth and resources are unnecessarily overloaded which is avoidable. Further, latency increases, and user experience is deteriorated.

[0005] Further, the data is being continually added to the devices (both server, client). The newly added data may require further efforts for classification and may not be available instantly unless classified manually. The user, such as a client device, may not be aware of such data being added and may not be able to access or share the important data required. Further, for the same device, the user configuration (such as context and profile) may change with time or transfer of device. The current solution are not able to provide for such dynamic changes in data and device information.

[0006] Therefore, in view of the above-mentioned problems, it is advantageous to provide an improved system and method that can overcome the above-mentioned problems and limitations.

[0007] This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the disclosure. This summary is neither intended to identify key or essential inventive concepts of the invention nor is it intended to determine the scope of the disclosure.

[0008] According to an embodiment, the present disclosure relates to a method for sharing data between electronic devices. The method includes receiving, by a sender device, device information and contextual information associated with one or more connected receiver devices. Further, the method includes deriving, using machine learning models, by the sender device, a context for the receiver device based on the received device information and the received contextual information. Furthermore, the method includes classifying, by the sender device, data stored at the sender device based on the received device information and the derived context. Subsequently, the method includes generating, by the sender device, one or more sharing folders based on the classified data, and storing, by the sender device, the classified data in the one or more generated sharing folders.

[0009] According to another embodiment, the present disclosure relates to a method for sharing data between electronic devices. The method includes transmitting, by a receiver device, a connection request to a sender device for establishing a connection between the electronic devices for sharing the data. Further, the method includes sending, by the receiver device, contextual information and device information associated with the receiver device to the sender device. Subsequently, the method includes mounting, by the receiver device, on the sender device for sharing the data of the sender device, the mounting being a contextual mounting based on the sent contextual information.

[0010] According to another embodiment, the present disclosure relates to a system for sharing data between electronic devices. The system includes a context module configured to receive, by a sender device, device information and contextual information associated with the one or more receiver devices. Further, the system includes a context deriving module configured for deriving, using machine learning models, a context for the receiver device by the sender device based on the received device information and the received contextual information. Further, the system includes a classifying module configured to classify, by the sender device, data stored at the sender device based on the received device information and the derived context. Furthermore, the system includes a storing module configure to generate, by the sender device, one or more sharing folders based on the classified data. Subsequently, the system includes a mounting module configured to mount the one or more sharing folders on the one or more receiver devices.

[0011] According to another embodiment, the present disclosure relates to a system for sharing data between electronic devices. The system includes a connecting module configured for transmitting, by a receiver device, a connection request to a sender device for establishing a connection between the electronic devices for sharing the data. Further, the system includes a sending module configured for sending, by the receiver device, contextual information and device information associated with the receiver device to the sender device. Subsequently, the system includes a mounting module configured for mounting, by the receiver device, on the sender device for sharing the data of the sender device, the mounting being a contextual mounting based on the sent contextual information.

[0012] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will be rendered by reference to specific embodiments thereof, which are illustrated in the appended drawing. It is appreciated that these drawings depict only typical embodiments of the disclosure and are therefore not to be considered limiting its scope. The disclosure will be described and explained with additional specificity and detail with the accompanying drawings.

[0013] These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

[0014] Figures 1-2 illustrate solutions for sharing data between electronic devices, in the prior art;

[0015] Figure 3 illustrates a system for sharing data between electronic devices including a first electronic device and a second electronic device, in accordance with an embodiment of the present disclosure;

[0016] Figure 3A illustrates a system for sharing data between the electronic devices, in accordance with another embodiment of the present disclosure;

[0017] Figure 3B illustrates the working of a deriving module of the system, in accordance with an exemplary embodiment of the present disclosure;

[0018] Figure 3C illustrates the working of a storing module and a monitoring module of the system, in accordance with an exemplary embodiment of the present disclosure;

[0019] Figure 4A-B is a process flow illustrating the working of the system, in accordance with an exemplary embodiment of the present disclosure;

[0020] Figure 5A is a process flow illustrating the working of the system, in accordance with another exemplary embodiment of the present disclosure;

[0021] Figure 5B illustrates the aspect of context aware mounting of the systems, in accordance with an exemplary embodiment of the present disclosure;

[0022] Figure 6 is a process flow illustrating the working of the system, in accordance with an exemplary embodiment of the present disclosure;

[0023] Figure 7 is a process flow illustrating the aspect of contextual mounting of the system, in accordance with an exemplary embodiment of the present disclosure;

[0024] Figure 8 is a process flow illustrating the aspect of contextual mounting of the system, in accordance with another exemplary embodiment of the present disclosure;

[0025] Figure 9 is an exemplary process flow illustrating the working of the deriving module of the system, in accordance with an embodiment of the present disclosure;

[0026] Figure 10 is an exemplary process flow illustrating contextual mounting of the system, in accordance with an exemplary embodiment of the present disclosure; and

[0027] Figures 11-11A are flowcharts illustrating methods for sharing data between the electronic devices including the first device and the second device, in accordance with an embodiment of the present disclosure.

[0028] Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present disclosure. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

[0029] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the various embodiments, and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as illustrated therein being contemplated as would normally occur to one skilled in the art to which the disclosure relates.

[0030] The term “some” or “one or more” as used herein is defined as “one”, “more than one”, or “all.” Accordingly, the terms “more than one,” “one or more” or “all” would all fall under the definition of “some” or “one or more”. The terms “an embodiment”, “another embodiment”, “some embodiments”, or “in one or more embodiments” may refer to one embodiment or several embodiments, or all embodiments. Accordingly, the term “some embodiments” is defined as meaning “one embodiment, or more than one embodiment, or all embodiments.”

[0031] The terminology and structure employed herein are for describing, teaching, and illuminating some embodiments and their specific features and elements and do not limit, restrict, or reduce the spirit and scope of the claims or their equivalents. The phrase “exemplary” may refer to an example.

[0032] More specifically, any terms used herein such as but not limited to “includes,” “comprises,” “has,” “consists,” “have” and grammatical variants thereof do not specify an exact limitation or restriction and certainly do not exclude the possible addition of one or more features or elements, unless otherwise stated, and must not be taken to exclude the possible removal of one or more of the listed features and elements unless otherwise stated with the limiting language “mush comprise” or “needs to include”.

[0033] Whether or not a certain feature or element was limited to being used only once, either way, it may still be referred to as “one or more features”, “one or more elements”, “at least one feature”, or “at least one element.” Furthermore, the use of the terms “one or more” or “at least one” feature or element does not preclude there being none of that feature or element unless otherwise specified by limiting language such as “there needs to be one or more” or “one or more element is required.”

[0034] Unless otherwise defined, all terms, and especially any technical and / or scientific terms, used herein may be taken to have the same meaning as commonly understood by one having ordinary skill in the art.

[0035] Figure 3 illustrates a system 300 for sharing data between electronic devices including a first electronic device 302 and a second electronic device 304, in accordance with an embodiment of the present disclosure. In an embodiment, the first device 302 may be a sender device such as a mobile phone device of a first user 302U. In an embodiment, the second device 304 may be a receiver device such as a laptop device of a second user 304U. In an embodiment, there may be more than one receiver devices. In an embodiment, the second user 304U of the second device 304 may want to access data on the first device 302. In an embodiment, there may be more than one user of the second device 304.

[0036] In an alternate embodiments, at different instances, the first user 302U of the first device 302 may want to access data stored on the second device 304 and vice versa. Accordingly, a device with stored data may act as a server / sender device and a device, the user of which wants to access data on the other device, may act as a client device. The devices 302, 304 may be referred to, alternatively, as the receiver device (or client) or the sender device (or server) depending upon the instance of the exemplary embodiment and the access of the data required between the devices 302 and 304.

[0037] In an embodiment, the system 300 may reside on the first device 302 (herein sender device, server, etc.), which device has the data which the second user 304U wants to access via the second device 304. In various embodiments, any of the devices 302, 304 may be a smartphone, a mobile phone, a tablet device, a laptop, a camera, or any other electronic device which may have data storage and / or processing capabilities without departing from the scope of the present disclosure.

[0038] As shown in Figure 3, the system 300 includes a plurality of modules 300M including a context module 309, a context deriving module 310 including a machine learning model 310m, a classifying module 320 including a machine learning model 320m, a storing module 330, and a mounting module 340. The context module 309 is configured to receive device information and contextual information associated with the one or more receiver devices such as the second device 304. The context deriving module 310 is configured for deriving, using the machine learning model 310m, by the sender device 302, a context for the receiver device 304 based on the received device information and the received contextual information; The device information and contextual information associated with the second device 304 is received by the sender device 302. The classifying module 320 is configured to classify, by the sender device 302, data stored at the sender device 302 based on the received device information and the derived context. In an embodiment, the classifying module 320 comprises the machine learning model 320m configured to classify the stored data at the device 302 into clusters.

[0039] The storing module 330 is configured to generate, by the sender device 302, one or more sharing folders (shared folders) of the stored data based on the classified data.

[0040] An admin of a storage server such as the device 302 can create a shared folder and copy some relevant files into the created shared folder for other nearby clients such as the device 304 to access, modify the created shared folder, etc. In an exemplary embodiment, an Internal Storage (IS) of the device 302 may be marked as 'Shared Folder' and a path to the Shared Folder may be specified while configuring shared resource 'media' at the server side. This shared resource 'media' may be accessed by an allowed client devices / users such as the device 304. Following is an example of such a shared resource and a shared path:

[0041] [media]

[0042] ; share parameters

[0043] force group = everybody

[0044] force user = u0_a307

[0045] max connections = 2

[0046] path = / storage / emulated / 0 -> is Internal Storage of the device 302

[0047] read only = no

[0048] herein,

[0049] shared resource : [media]

[0050] shared path: / storage / emulated / 0

[0051]

[0052] Following is an example of the media / folders stored in the IS of the device 302:

[0053] IS: / storage / emulated / 0

[0054] Photos / Videos: / storage / emulated / 0 / DCIM

[0055] Downloads: / storage / emulated / 0 / Download

[0056] Music Files: / storage / emulated / 0 / Music

[0057] Pictures: / storage / emulated / 0 / Pictures

[0058] ... Others...

[0059] In an embodiment, the sender device 302 may use an implementation system such as a Server Message Block (SMB) server implementation. As an example, KSMBD is a Linux kernel server which implements SMB3 protocol in kernel space for sharing files over network. KSMBD is part of Linux Mainline Kernel and is part of Android code base. Other alternative examples for SMB server implementation may include Windows Samba Server, Linux Samba Server, and the like.

[0060] Subsequently, the mounting module 340 is configured to mount the one or more sharing folders on the device 304 for accessing by nearby client devices such as the device 304. In an embodiment, mounting may be performed at the device 304 wherein content of the device 302 which may be in the form of the stored data (such as the 'media' resource defined at the server, the device 302) may be mapped at the client side (the device 304) as a shared network drive which a client user can access. For example, like any other local drive (C: / , D: / ), via:

[0061] mount -t cifs / <IP> / <SharedResourceName> -o username=<>,password=<> <target path>

[0062] In an embodiment, the device information comprises an account-type, a device-type, a capability information, and login-information of the device 304. In an embodiment, the derived context comprises a user image, a user-profile information, one or more user preferences, a user location, and one or more user events associated with the user 304U of the device 304. Further, the derived context comprises profile information of one or more users related to the user 304U.

[0063] In an embodiment, the system 300 further comprises a connecting module 350 configured for establishing, by the sender device 302, a connection between the sender device 302 and the one or more receiver devices such as the second device 304.

[0064] In an embodiment, the system 300 further comprises a monitoring module 360. Upon mounting the one or more sharing folders on the one or more receiver devices such as the second device 304, the monitoring module 360 is configured to detect, continually, an addition of new data at the sender device 302, classify the new data based on the derived context using the classifying module 320, and update the one or more sharing folders with the classified new data. In an embodiment, the monitoring module is located at the sender device 302.

[0065] In an embodiment, the monitoring module 360 at the sender device 302 is also configured to detect, continually, a change in the received contextual information. The received contextual information may include parameters shared by the receiver device 304. Upon detection of the change in the received contextual information, the context deriving module 310 is configured to derive, using the machine learning model 310m, by the sender device 302, an updated context associated with the receiver device 304 based on the detected change in the received contextual information. Subsequently, the classifying module 320 is configured to re-classify the data based on the updated context and the detected change in the contextual information such as the parameters received and update the one or more sharing folders with the re-classified data.

[0066] In an embodiment, the monitoring module 360 is also configured to detect a change in the device information associated with the one or more receiver devices such as the device 304 and update the one or more sharing folders based on the detected change in the device information. Further functionalities and detailed working of each of the modules 300M of the system 300 are explained in greater detail with reference to the following Figures.

[0067] Figure 3A illustrates a system 300A for sharing data between the electronic devices including the first device 302 and the second device 304, in accordance with an embodiment of the present disclosure. The system 300A includes a plurality of modules 300MA including a connecting module 352, context deriving module 312 including a machine learning model 312m, a sending module 370, and a mounting module 342. In an embodiment, the connecting module 352 is configured for transmitting, by the receiver device 304, a connection request to the sender device 302 for establishing a connection between the electronic devices 302, 304 for sharing the data. The context deriving module 312 is configured for deriving, using the machine learning models 312m, by the receiver device 304, a context associated with the receiver device 304 based on the contextual information associated with the receiver devices 304.

[0068] Upon deriving of the context, the sending module 370 is configured for sending, the derived context associated with the receiver device 304 to the sender device 302. The mounting module 342 is configured for mounting, by the receiver device 304, on the sender device 302 for sharing the data of the sender device 302. The mounting is a contextual mounting based on the sent contextual information. Herein, the context deriving module 312 is located on the receiver device 304. In various embodiments, the functionalities and working of the context deriving module 312 and context deriving module 310 of the embodiment as in Figure 3 may coincide and may be similar but for the location of the corresponding module in devices 302, 304.

[0069] The functionality and working of each of the modules 300MA is explained in greater detail with reference to the following Figures. In an embodiment, the system 300A may reside on the receiver device 304 (herein second device, client), the user 304U of which device wants to access the data stored at the server device 302, which the user 304U wants to access. In various embodiments, any of the devices 302, 304 may be a smartphone, a mobile phone, a tablet device, a laptop, a camera, or any other electronic device which may have data storage and / or processing capabilities without departing from the scope of the present disclosure.

[0070] In an embodiment, the device information comprises an account-type, a device-type, a capability information, and login-information of the receiver device 304. In an embodiment, the derived context comprises a user image, a user-profile information, one or more user preferences, a user location, and one or more user-events associated with the user 304U of the receiver device 304. The derived context further comprises profile information of one or more users related to the corresponding user 304U.

[0071] In an embodiment, the context deriving module 312 is configured for determining activities associated with the receiver device 304, determining a manual configuration of a context being defined by the user 304U of the receiver device 304 and deriving, using the machine learning models 312m, the context based on at least one of the determined activities and the determined manual configuration of the context being defined by the user.

[0072] In an embodiment, the systems 300, 300A include one or more processors 390, a memory 308, a transceiver 326, and an I / O interface 328. The processor 390 may be disposed in communication with a communication network via a network interface. In an embodiment, the network interface may be the I / O interface 328. The network interface may connect to the communication network to enable the connection of the system 300 with the device. The network interface may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10 / 100 / 1000 Base T), transmission control protocol / internet protocol (TCP / IP), token ring, IEEE 702.11a / b / g / n / x, etc. The communication network may include, without limitation, a direct interconnection, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, etc. Using the network interface and the communication network, the system 300 may communicate with other devices. The network interface may employ connection protocols including, but not limited to, direct connect, Ethernet (e.g., twisted pair 10 / 100 / 1000 Base T), transmission control protocol / internet protocol (TCP / IP), token ring, IEEE 702.11a / b / g / n / x, etc.

[0073] In some embodiments, the memory 308 may be communicatively coupled to the processor 390. The memory 308 may be configured to store data, and instructions executable by the processor 390. In one embodiment, the memory 308 may be provided within the device. In another embodiment, the memory 308 may be provided within the system 300 being remote from the device. In yet another embodiment, the memory 308 may communicate with the processor 390 via a bus within the system 300. In yet another embodiment, the memory 308 may be located remotely from the processor 390 and may be in communication with the processor 390 via a network. The memory 308 may include, but is not limited to, a non-transitory computer-readable storage media, such as various types of volatile and non-volatile storage media including, but not limited to, random access memory, read-only memory, programmable read-only memory, electrically programmable read-only memory, electrically erasable read-only memory, flash memory, magnetic tape or disk, optical media and the like.

[0074] In one example, the memory 308 may include a cache or random-access memory for the processor 390. In alternative examples, the memory 308 is separate from the processor 390, such as a cache memory of a processor, the system memory, or other memory. The memory 308 may be an external storage device or database for storing data. The memory 308 may be operable to store instructions executable by the processor 390. The functions, acts, or tasks illustrated in the figures or described may be performed by the programmed processor 390 for executing the instructions stored in the memory 308. The functions, acts, or tasks are independent of the particular type of instruction set, storage media, processor, or processing strategy and may be performed by software, hardware, integrated circuits, firmware, micro-code, and the like, operating alone or in combination. Likewise, processing strategies may include multiprocessing, multitasking, parallel processing, and the like.

[0075] In some embodiments, the plurality of modules 300M, 300MA may be included within the memory 308. The plurality of modules 300M, 300MA may include a set of instructions that may be executed to cause the system 300, 300A in particular, the processor 390 of the system 300, to perform any one or more of the methods / processes disclosed herein. The plurality of modules 300M may be configured to perform the steps of the present disclosure using the data stored in the database. For instance, the plurality of modules 300M, 300MA may be configured to perform the steps disclosed in Figures 11, 11A.

[0076] In another embodiment, each of the plurality of modules 300M, 300MA may be a hardware unit that may be outside the memory 308. Further, the memory 308 may include an operating system for performing one or more tasks of the system 300, as performed by a generic operating system. Each of the modules 300M, 300MA may be in communication with one another and the processor 390.

[0077] At least one of the plurality of modules 300M, 300MA may be implemented through an Artificial Intelligence (AI) or a machine learning model. For example, in an embodiment, the modules 310m, 320m, and 312m are configured to apply the AI or machine learning models. A function associated with the AI models may be performed through a memory 308 (including a non-volatile memory and / or a volatile memory) and the processor 390.

[0078] The processor 390 may include one or a plurality of processors. At this time, one or a plurality of processors may be a general purpose processor, such as a central processing unit (CPU), an application processor (AP), or the like, a graphics-only processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU), and / or an AI-dedicated processor such as a neural processing unit (NPU). The one or a plurality of processors control the processing of the input data in accordance with a predefined operating rule or the AI model stored in the non-volatile memory and the volatile memory. The predefined operating rule or the AI model is provided through training or learning. Here, being provided through learning means that, by applying a learning technique to a plurality of learning data, a predefined operating rule or AI model of a desired characteristic is made. The learning may be performed in a device itself in which AI according to an embodiment is performed, and / or may be implemented through a separate server / system.

[0079] The AI model may consist of a plurality of neural network layers. Each layer has a plurality of weight values and performs a layer operation through calculation of a previous layer and an operation of a plurality of weights. Examples of neural networks include, but are not limited to, a Convolutional Neural Network (CNN), a Deep Neural Network (DNN), a Recurrent Neural Network (RNN), a Restricted Boltzmann Machine (RBM), a Deep Belief Network (DBN), a Bidirectional Recurrent Deep Neural Network (BRDNN), a Generative Adversarial Networks (GAN), and deep Q-networks.

[0080] The learning technique is a method for training a predetermined target device (for example, a robot) using a plurality of learning data to cause, allow, or control the target device to make a determination or prediction. Examples of learning techniques include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. In an embodiment, the classifier model 310m may be pretrained.

[0081] According to the disclosure, a method for generating a plurality of instructions may use an AI model to recommend / execute the plurality of instructions by using data generated by various sensors of the device. The processor may perform a pre-processing operation on the data to convert into a form appropriate for use as an input for the AI model. The AI model may be obtained by training. Here, "obtained by training" means that a predefined operation rule or AI model configured to perform a desired feature (or purpose) is obtained by training a basic artificial intelligence model with multiple pieces of training data by a training technique. The artificial intelligence model may include a plurality of neural network layers. Each of the plurality of neural network layers includes a plurality of weight values and performs neural network computation by computation between a result of computation by a previous layer and the plurality of weight values.

[0082] Reasoning prediction is a technique of logical reasoning and predicting by determining information and includes, e.g., knowledge-based reasoning, optimization prediction, preference-based planning, or recommendation.

[0083] In an embodiment, each of the plurality of modules 300M, 300MA may be a hardware unit that may be outside the memory 308. Further, the memory 308 may include an operating system for performing one or more tasks of the system 300, as performed by a generic operating system. Each of the modules 300M, 300MA may be in communication with one another and the processor 390. The working and functioning of the plurality of modules 300M, 300MA of the system 300 have been described in detail with reference to the following Figures.

[0084] In an embodiment, the system 300 may be located in the devices 302, 304. In another embodiment, the system 300 is in the form of programmed instructions and may be located at distributed locations such as within the Operating System of the device, installed externally as a software application on the device, or in the cloud. In another embodiment, the system 300 may be located on a server in communication with the device.

[0085] Figure 3B illustrates the working of the context deriving module 310 of the system 300A, in accordance with an exemplary embodiment of the present disclosure. The client (herein, the device 304) based on the storage share activities shares a list of features with the server (herein, the device 302) via an MCF link. The list of features shared with the server acts as an input to understand the client context by the server. The context may be generated from the client and passed via the MCF to the server. The server may have available data clusters and pre-classifies the data amongst the data clusters. The input received from the client is compared with centroids of the available clusters. A matching cluster is identified and a Virtual Storage Folder (VSF) or group of VSFs corresponding to the identified cluster is shared with the client. A VSF is a sharing folder which may be a virtual folder having the symbolic links (reference links) of all the resultant files matched with the client context.

[0086] In an alternate embodiment, a set of client parameters corresponding to the input may be collected and passed via the MCF to the server and the server, in turn may generate the context. In an embodiment, the client (the device 304) may not have the ML model (the machine learning model 310m) to generate the context. Advantageously, the system 300A works with any client even if the ML model may not be able to run on a specific client device (the device 304).

[0087] In an alternate embodiment as described in Figure 3A, the client device (the device 304) may include the context deriving module 312, which may have functionalities similar to context deriving module 310, (but for being located in the client device 304). In such embodiments, the client device 304 derives and sends the derived context to the server 302, which may need to not derive the context for the specific client device 304.

[0088] In an embodiment, the following may be selected as the set of client parameters:

[0089] 1: LRU (Least Recent Used) Cache of Client View - This includes the name of the file view, the duration of the file, and the number of times the specific file has been viewed. The size of LRU is fixed as 1000 and can be increased based on the deployment scenarios.

[0090] 2. Personal information: The details available from the MCF profile, such as Name, Age, Profile picture, Samsung account details, and demographic details will be fetched and can be used to understand the context

[0091] 3. Communication Patterns: This determines the mounting patterns such as aggressive user or specific time the storage sharing has been accessed.

[0092] 4. Location info: If we have the consent of the user to share the location details, this also can be fetched and used.

[0093] 5. Manual User Inputs: This is part of our next phase, where the user can input the text, and we use the generative model to directly understand and fetch the corresponding context.

[0094] Figure 3C illustrates the working of the storing module 330 and the monitoring module 360 of the system 300, in accordance with an exemplary embodiment of the present disclosure. The storing module 330 generates the sharing folders of the stored data at the device 302. In an embodiment, during an initial setup phase at the device 302 (server side), there may be an overhead associated with classification of the stored data of the device 302 into the clusters and labeling sources of the various client contexts. This may involve running the clustering algorithm of the classifying module 320 on the entire IS of the device 302 to identify distinct clusters and assign corresponding labels accordingly. This step is necessary to determine the centroids of the clusters, which represent the central point or representative sample of each cluster.

[0095] Once the centroids are established, the centroids serve as reference points for future iterations. The monitoring module detects the addition of the new data at the sender device 302 and classifies the new data based on the contextual information using the classifying module 320 and updates the sharing folders with the classified new data. Specifically, in subsequent iterations, instead of processing the entire IS of the device 302 again, the clustering algorithm of the classifying module 320 will compare the input raw data (the new data) with the centroids. In an embodiment, the classifying module 320 may calculate a distance or similarity between the new data and the centroids and determines an appropriate cluster assignment for the new data.

[0096] As a result, the processing time for subsequent iterations is reduced, as the system 300 no longer needs to perform the full clustering analysis from scratch. Instead, the system 300 relies on the previously computed centroids, which act as a guide for assigning the new data to the appropriate clusters using the monitoring module 360 and the classifying module 320. Consequently, the computational overhead is minimized, leading to faster processing times and improved efficiency.

[0097] In an embodiment, if there is no change in the context or no new data is added, apparently the system 300 need not use the classifying module 320 for classifying data again or the storing module 330 to create or update any of the folders.

[0098] Figure 4A-B is a process flow 400 illustrating the working of the system 300, 300A, in accordance with an exemplary embodiment of the present disclosure.

[0099] In the exemplary embodiment as shown in Figure 4, both the client (the device 304) and the server (the device 302, e.g. a Mobile device) may use the same account (authentication), connect to same Wi-Fi Network SSIDs (otherwise, uses Wi-Fi P2P Network) and may enable Storage Share feature on both client and server apps.

[0100] Following is an exemplary process flow for communication including authentication between the devices 302, 304:

[0101] MCF(Multi Connectivity Framework) - SDK(software development kit) enables communication between nearby devices over local area networks like WLAN (wireless LAN), P2P(point to point) etc. and supports client-server architecture.

[0102] MCF offers a mechanism for server device such as the device 302 to advertise the services to nearby client devices using mechanism such as BLE Advertisement.

[0103] MCF further enables and authorizes the nearby devices logged in with the same account to authenticate and to discover the nearby services using BLE Discovery and Authentication mechanism.

[0104] MCF SDK may be supported for all devices like Mobile, Tablet, Laptop, TV etc.

[0105] MCF session may be used for sharing the client contextual information with server.

[0106] MCF chooses underlying connectivity technologies like BLE(for initial advertisement, discovery of services), Wi-Fi(for data transfer), Wi-Fi Direct (data transfer when user is outside and there is NO Common Wi-Fi AP) etc., based on the User / Network requirements of high level solutions.

[0107] In an exemplary embodiment, process flow 400 may include the following steps:

[0108] The user 304U of the client (the device 304) clicks on discovered server such as the mobile device 302.

[0109] A secure MCF connection may be established

[0110] Client requests SMB credentials to establish File Sharing session over Secure MCF Session

[0111] SSS generates SMB Connect Credentials and shares to nearby Storage Share Client / s(SSC / s)

[0112] Client establishes separate SMB Connection(File Transfer Session) with Server using credentials shared in 4) and using SMB protocol.

[0113] User on the Laptop client may open Windows File Explorer

[0114] User sees 밪hared Network Drive S: / of the Mobile"

[0115] Upon Client / Server user performing File transfer operations like upload, download, copy, move, delete etc. on the network drive and is achieved using underlying SMB protocol messages.

[0116]

[0117] Figure 5A is a process flow 500 illustrating the working of the system 300, 300A, in accordance with an exemplary embodiment of the present disclosure. The process flow 500 shows steps 1-5 pertaining to the MCF channel setup. MCF enables nearby devices to advertise, discover, authenticate and connect and communicate with each other over local network. Following is an exemplary explanation of the steps 1-5:

[0118] Step 1: Enable Storage Share feature on both SS client and SS server applications.

[0119] Step 2: MCF offers a mechanism for server device to advertise about its service (Ex: Storage Share Service) to nearby client devices using means such as BLE advertisement. SS server(Husband's Device) sends advertisement about its Social Storage Share Service for nearby SS clients (Wife's device) over BLE link using MCF framework.

[0120] Step 3: MCF enables SS clients to discover the nearby SS services offered using BLE Discovery mechanism. SS Client (Wife's device) discovers nearby SS server device of the husband over BLE using MCF.

[0121] Step 4a: MCF enables and authorizes all those nearby devices (SS Client and SS Server) logged in with same authentication account to authenticate with each other.

[0122] Step 4b: Post MCF Authentication, discovered SS Server (Husband's Device) is shown for Client User (Wife) as "husband's device".

[0123] Step 5: When client user clicks on discovered SS Server device, MCF session will be established between nearby SS client (wife) and SS server (husband).

[0124] Subsequently, the SS Client (Wife device) shares user and device contextual information to the SS Server (Husband device) over MCF Session [Step#6]. The AI models such as the machine learning models 312m of the context deriving module 312 may be used to derive the context. Using the AI model and based on the parameters that are specific to the receiver device and corresponding user of the receiver device, the context is derived as an output of this AI model.

[0125] In various embodiments, the context may be various information like Device Type, Location Information, Search History, Specific Text, Specific Image, App currently in use and the like. For example, SS Client device (Wife device) uses Client User Image (Wife's Image) as a Context and passes it to SS Server device (GS24 -> Husband's device). Wife's Image is set as the Context for Mounting. SS Client shares client user and client device contextual information to nearby SS Server using MCF. Client device (Wife device) uses already established MCF communication link (link established in steps#2 to 5) to send all the required parameters to generate Context to Server device (Husband device).

[0126] Subsequently, the SS Server classifies the data, creates shared resource (VSF#N), generates credentials for file transfer [Step#7]. The SS Server auto classifies data for nearby clients based on the derived context. The SS server searches the entire IS of the SS server based on the given context (user and device contextual information) and gathers all the matched data. Existing search features of the devices such as Android Gallery Search features and any other search or AI ML based algorithms for matching client contextual information against the entire server storage data may be used. Symbolic links (reference links) for all the matched resultant files are created and those links are copied to Virtual Shared Folders (VSFs).

[0127] The VSFs may be virtual folders having the symbolic links (reference links) of all the resultant files matched with the client context. Symbolic links in the VSFs are just a reference links pointing to the real source files (present in some other folders on the same SS Server device). Each VSF is created per client context specific. Example, Wife's Image is received as a context parameter as a part of the contextual information from the wife device (client) and is fed as input to the AI / ML based search algorithms and symbolic links are created and stored in separate VSFs. Such VSF that is exclusively created based on Wife Image context will have all the data belonging to Wife (images, videos, etc.). SS Server enables appropriate permissions like read only, read / write etc., on the VSF based on the context exchanged between SS Client and Server. SS Server generates credentials (username, password) required to be shared with client for establishing SMB (Server message block) session (for further file transfer operations). SS Server shares credentials with client along with its IP-Address, Shared Resource Name('VSF#N'), etc. using MCF link.

[0128] The mounting module 342 is enables mounting of the receiver device 304 (the client) on the sender device 302 (the server) for sharing the data of the sender device 302. The mounting is a contextual mounting based on the derived context. Continuing with the exemplary embodiment above, subsequently, the SS Client performs CONTEXT AWARE Mounting, establishes SMB Session (using SMB Protocol) [Steps 8-9].

[0129] At Step 8: The SS client performs CONTEXT AWARE MOUNTING using SMB credentials (username, password) to the SS server. For example, Context Aware Mount to fetch Wife Images by Client using Shared Resource, Context Aware Mount to fetch Fishing Images by Client using Shared Resource. Upon successful SMB (Server Message Block) user authentication, the SS client requests for SMB TREE and connects to access the shared resource (pointing to the VSF that is created as per client context ). The mounted drive at client side represents VSF's at server side and will contain all the data corresponding to the context of the corresponding client. For example, the Context aware Mounted Drive contains all the Wife's Images, Context aware Mounted Drive contains all the Fishing Images.

[0130] Step 9: Client user (for example, the wife) can browse the mounted drive data, download any of her photos over local Wi-Fi network, can open a file, can copy files, can move files from shared resource to her local client storage (based on server permission granted) etc.

[0131] Figure 5B illustrates the aspect of context aware mounting of the systems 300, 300A, in accordance with an exemplary embodiment of the present disclosure. The systems 300 and 300A enable context based mounting on all the nearby client devices such as the device 304 by enabling personalized data sharing (corresponding to each context) for all the nearby clients such as the device 304.

[0132] The context based mounting of the systems 300, 300A in a Multi Device Environment (MDE) (of electronic devices such as the devices 302, 304) enable the client devices such as the device 304 to enquire about the server data by sending corresponding contextual information including contextual parameters. The context based mounting offers faster access to required data from the server on each of the client device. The systems 300 and 300A enable virtually partitioning the IS of the server device by creating sharing folders based on each client context. As a result, each client is allowed to access only corresponding personalized data. Further, each client is also restricted from accessing other internal storage data of server, thus ensuring security and addressing privacy concerns.

[0133] Figure 6 is a process flow 600 illustrating the working of the systems 300, 300A, in accordance with an exemplary embodiment of the present disclosure. A Virtual Storage Folder (VSF) is a sharing folder which may be a virtual folder having the symbolic links (reference links) of all the resultant files matched with the client context. Symbolic links in the VSF may be reference links pointing to the real source files (present in some other folders on server device). In an embodiment, each VSF is as per the client context.

[0134] Figure 7 is a process flow 700 illustrating the aspect of contextual mounting of the system 300, 300A, in accordance with an exemplary embodiment of the present disclosure. In the example of Figure 7, a wife's mobile device is the receiver device 304 and her husband's mobile device is the server device 302. The wife may access and download all her photos from the husband's mobile by sharing her image as the context for mounting.

[0135] Figure 8 is a process flow 800 illustrating the aspect of contextual mounting of the system 300, 300A, in accordance with another exemplary embodiment of the present disclosure. In the example of Figure 8, Mr. Kim (client) gets all his FISHING activity photos from his friend Mr. Lee's Mobile (server) after the ICE FISHING Festival by passing 'FISHING' activity in text form as CONTEXT for mounting.

[0136] Figure 9 is an exemplary process flow 900 illustrating the working of the context deriving module 312 of the system 300A, in accordance with an embodiment of the present disclosure. In the example of Figure 9, Mr. Kim is browsing on the web about the Ice-Fishing festival, and is searching for Fishing Images on his gallery, etc. An AI based context derivation engine such as the context deriving module 312 auto detects such user activities and automatically sets 'FISHING' as the context for Mounting. Mr. Kim (SS Client) gets all his FISHING activity photos from his nearby friend Mr. Lee's Mobile (SS Server).

[0137] Figure 10 is an exemplary process flow 10000 illustrating contextual mounting of the system 300, 300A, in accordance with an exemplary embodiment of the present disclosure. In the example of Figure 10, Mr. Manju watching his personal family videos (wedding videos, kids' videos, etc.) present in Mobile (SS Server) on his VST Pro device (SS Client) for experiencing cinematic / theatre effect. Mr. Manju may experience an immersive experience for watching movies, TV shows, or other media in a simulated large-screen environment. The VST device transforms the surrounding virtual space into a cinema-like environment, creating the illusion of watching content on a massive screen, akin to sitting in a theatre.

[0138] Figures 11-11A are flowcharts illustrating methods 1100, 110A for sharing data between the electronic devices including the first device 302 and the second device 304, in accordance with an embodiment of the present disclosure. Referring to Figures 3-10 together, the method 1100, 1100A may be performed by the devices such as the devices 302, 304 based on instructions retrieved from non-transitory computer-readable media. A computer-readable media may include machine-executable or computer-executable instructions to perform all or portions of the described method. The computer-readable media may be, for example, digital memories, magnetic storage media, such as magnetic disks and magnetic tapes, hard drives, or optically readable data storage media.

[0139] The method 1100, 1100A include a series of operations shown at step 1102 through step 1108 and step 1102A through step 1106A of Figures 11, 11A. The method 1100, 1100A may be performed by the systems 300, 300A in conjunction with one or more modules 300M, 300MA, the details of which are explained in conjunction with Figures 3-10, and the same are not repeated here for the sake of brevity.

[0140] Referring specifically to Figure 11, the method 1100 begins at step 1102. At step 1102, the method 1100 includes receiving the device information and the contextual information associated with the one or more receiver devices such as the second device 304. The device information and contextual information associated with the second device 304 is received by the sender device 302. In an embodiment, the device information comprises an account-type, a device-type, a capability information, and login-information of the device 304.

[0141] In an embodiment, the method 1100 further includes establishing, by the sender device 302, a connection between the sender device 302 and the one or more receiver devices such as the second device 304.

[0142] At step 1103, the method 1100 includes deriving, using machine learning models, by the sender device 302, a context for the receiver device 304 based on the received device information and the received contextual information. In an embodiment, the derived context comprises a user image, a user-profile information, one or more user preferences, a user location, and one or more user events associated with the user 304U of the device 304. Further, the contextual information comprises profile information of one or more users related to the user 304U.

[0143] In an embodiment, the step 1103 of deriving the context comprises determining, based on the received contextual information activities associated with the receiver device 304 and a manual configuration of a context being defined by a user of the receiver device 304. Further the method 1100 at step 1103 includes deriving, using the machine learning models, the context based on at least one of the determined activities and the determined manual configuration of the context being defined by the user. Further the method 1100 at step 1103 includes re-classifying the data at the sender device 302 based on the updated context and the detected change in the contextual information and updating the one or more sharing folders with the re-classified data

[0144] At step 1104, the method 1100 includes classifying, by the sender device 302, data stored at the sender device 302 based on the received device information and the derived context. In an embodiment, the method 1100 at step 1104 includes using machine learning models such as the machine learning model 320m configured to classify the stored data at the device 302 into clusters. Specifically, the classifying may include selecting, from the data stored at the sender device 302, data that matches the derived context and is supported by the receiver device 304 based on the received device information and the derived context. For example, the sender device 302 may identify data relevant to the derived context and further filter the identified data based on capability information of the receiver device 304, such that data that is not supported by the receiver device 304 is excluded from the classified data.

[0145] At step 1106, the method 1100 includes generating, by the sender device 302, one or more sharing folders (the shared folders) of the stored data based on the classified data.

[0146] At step 1108, the method 1100 includes mounting the one or more sharing folders on the device 304 for accessing by the nearby client devices such as the device 304.

[0147] In an embodiment, the method 1100 further includes detecting, continually, the addition of the new data at the sender device 302, classifying the new data based on the contextual information, and updating the one or more sharing folders with the classified new data.

[0148] In an embodiment, the method 1100 further includes detecting, continually, a change in the contextual information, re-classifying the data based on the detected change in the contextual information and updating the one or more sharing folders with the re-classified data.

[0149] In an embodiment, the method 1100 further includes detecting a change in the device information associated with the one or more receiver devices such as the device 304 and update the one or more sharing folders based on the detected change in the device information.

[0150] Referring specifically to Figure 11A, the method 1100A begins at step 1102A. At step 1102A, the method 1100A includes transmitting, by the receiver device 304, a connection request to the sender device 302 for establishing a connection between the electronic devices 302, 304 for sharing the data. At step 1106A, the method 1100A includes sending, by the receiver device, the contextual information and the device information associated with the receiver device 304 to the sender device 302.

[0151] At step 1106A, the method 1100A may include deriving, using the machine learning models 312m, by the receiver device 304, a context associated with the receiver device 304. In an embodiment, the device information comprises an account-type, a device-type, a capability information, and login-information of the device 304. In an embodiment, the derived context comprises a user image, a user-profile information, one or more user preferences, a user location, and one or more user events associated with the user 304U of the device 304. Further, the derived context comprises profile information of one or more users related to the user 304U.

[0152] At step 1106A, the method 1100A further includes determining activities associated with the receiver device 304, determining a manual configuration of a context being defined by a user 304U of the receiver device 304 and deriving, using the machine learning models, the context based on at least one of the determined activities and the determined manual configuration of the context being defined by a user 304U.

[0153] Subsequently, at step 1108A, the method 1100A includes mounting, by the receiver device 304, on the sender device 302 for sharing the data of the sender device 302. The mounting is a contextual mounting based on the sent contextual information.

[0154] The system and method of the invention has user-awareness and device-awareness and generates personalized features for storage share client users. The user profile, characteristics, contents, location, events, etc., are identified. The device capabilities, grouping devices etc., are identified. The method and system of the invention does not require any internet or additional data cost. Further, the invention also does not require any manual user interventions. The invention enhances the storage share experience (offers personalized features to clients based on the contextual information) for nearby my devices (TV, Tablet, Laptop, Mobile, VST, Buds Iconx, etc.,) and for family members and friends' devices by auto classifying server's storage data based on different clients' context.

[0155] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.

[0156] The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein.

[0157] Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.

[0158] Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.

Claims

1.A method (1100) for sharing data between electronic devices, the method (1100) comprising:receiving, by a sender device (302), device information and contextual information associated with one or more connected receiver devices (304);deriving, using machine learning models, by the sender device (302), a context for the receiver device (304) based on the received device information and the received contextual information;classifying, by the sender device (302), data stored at the sender device (302) based on the received device information and the derived context;generating, by the sender device (302), one or more sharing folders based on the classified data; andstoring, by the sender device (302), the classified data in the one or more generated sharing folders.2.The method (1100) as claimed in claim 1, wherein the device information comprises an account-type, a device-type, a capability information, and login-information of the receiver device (304).3.The method (1100) as claimed in claim 1, wherein the derived context comprises:a user image, a user-profile information, one or more user preferences, a user location, and one or more user-events associated with a corresponding user of the one or more receiver devices (304); andprofile information of one or more users related to the corresponding user.4.The method (1100) as claimed in claim 1, wherein deriving the context comprises:determining, based on the received contextual information:activities associated with the receiver device (304); anda manual configuration of a context being defined by a user of the receiver device (304); andderiving, using the machine learning models, the context based on at least one of the determined activities and the determined manual configuration of the context being defined by the user.5.The method (1100) as claimed in claim 1 further comprises establishing, by the sender device (302), a connection between the sender device (302) and the receiver device (304) based on a connection request received from the one or more receiver devices (304).6.The method (1100) as claimed in claim 1 further comprises:detecting, continually, for addition of new data at the sender device (302);classifying the new data based on the derived context; andupdating the one or more sharing folders by storing the classified new data in corresponding sharing folders.7.The method (1100) as claimed in claim 1 further comprises:detecting, continually, a change in the received contextual information;deriving, using the machine learning models, by the sender device (302), an updated context associated with the receiver device (304) based on the detected change in the received contextual information;re-classifying the data at the sender device (302) based on the updated context and the detected change in the contextual information; andupdating the one or more sharing folders with the re-classified data.8.The method (1100) as claimed in claim 1 further comprises:detecting a change in the device information associated with the one or more receiver devices (304); andupdating the one or more sharing folders based on the detected change in the device information.9.The method (1100) as claimed in claim 1, wherein classifying the data based on the received device information and the derived context is performed using one or more machine learning models by selecting data that matches the derived context and is supported by the receiver device (304).10.A method (1100A) for sharing data between electronic devices, the method (1100A) comprising:transmitting, by a receiver device (304), a connection request to a sender device (302) for establishing a connection between the electronic devices for sharing the data;sending, by the receiver device (304), contextual information and device information associated with the receiver device (304) to the sender device (302); andmounting, by the receiver device (304), on the sender device (302) for sharing data that matches the contextual information and is supported by the receiver device (304), the mounting being a contextual mounting based on the sent contextual information and the device information.11.The method (1100A) as claimed in claim 10, wherein the device information comprises an account-type, a device-type, a capability information, and login-information of the receiver device (304).12.The method (1100A) as claimed in claim 10, wherein sending the contextual information comprises:deriving, using machine learning models, by the receiver device (304), a context associated with the receiver device (304) based on the contextual information associated with one or more connected receiver devices (304); andsending the derived context to the to the sender device (302);wherein the contextual information comprises:a user image, a user-profile information, one or more user preferences, a user location, and one or more user-events associated with a corresponding user of the receiver device (304); andprofile information of one or more users related to the corresponding user.13.The method (1100A) as claimed in claim 12, wherein deriving the context comprises:determining activities associated with the receiver device (304);determining a manual configuration of a context being defined by a user of the receiver device (304); andderiving, using the machine learning models, the context based on at least one of the determined activities and the determined manual configuration of the context.14.A system (300) for sharing data between electronic devices, the system (300) comprising:a context module (309) configured to receive, by a sender device (302), device information and contextual information associated with the one or more receiver devices (304);a context deriving module (310) configured for deriving, using machine learning models, by the sender device (302), a context for the receiver device (304) based on the received device information and the received contextual information;a classifying module (320) configured to classify, by the sender device (302), data stored at the sender device (302) by selecting data that matches the derived context and is supported by the receiver device (304) based on the received device information and the derived context;a storing module (330) configure to generate, by the sender device (302), one or more sharing folders based on the classified data; anda mounting module (340) configured to mount the one or more sharing folders on the one or more receiver devices (304).15.The system (300) as claimed in claim 13, wherein the device information comprises an account-type, a device-type, a capability information, and login-information of the one or more receiver devices (304).

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