Patent ID: 12254128

DETAILED DESCRIPTION

Embodiments of the disclosure and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the implementations to the precise form disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from practice of the implementations. Further, one or more features or components of one embodiment may be incorporated into or combined with another embodiment (or one or more features of another embodiment). Additionally, in the flowcharts and descriptions of operations provided below, it is understood that one or more operations may be omitted, one or more operations may be added, one or more operations may be performed simultaneously (at least in part), and the order of one or more operations may be switched.

It will be apparent that systems and/or methods, described herein, may be implemented in different forms of hardware, firmware, or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and/or methods is not limiting of the implementations. Thus, the operation and behavior of the systems and/or methods were described herein without reference to specific software code—it being understood that software and hardware may be designed to implement the systems and/or methods based on the description herein.

Even though particular combinations of features are recited in the claims and/or disclosed in the specification, these combinations are not intended to limit the disclosure of possible implementations. In fact, many of these features may be combined in ways not specifically recited in the claims and/or disclosed in the specification. Although each dependent claim listed below may directly depend on only one claim, the disclosure of possible implementations includes each dependent claim in combination with every other claim in the claim set.

No element, act, or instruction used herein should be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles “a” and “an” are intended to include one or more items, and may be used interchangeably with “one or more.” Where only one item is intended, the term “one” or similar language is used. Also, as used herein, the terms “has,” “have,” “having,” “include,” “including,” or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise. Furthermore, expressions such as “at least one of [A] and [B]” or “at least one of [A] or [B]” are to be understood as including only A, only B, or both A and B.

Reference throughout this specification to “one embodiment,” “one or more embodiments,” or similar language means that a particular feature, structure, or characteristic described in connection with the indicated embodiment is included in at least one embodiment of the present solution. Thus, the phrases “in one embodiment”, “in one or more embodiments,” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

Furthermore, the described features, advantages, and characteristics of the present disclosure may be combined in any suitable manner in one or more embodiments. One skilled in the relevant art will recognize, in light of the description herein, that the present disclosure may be practiced without one or more of the specific features or advantages of a particular embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that may not be present in all embodiments of the present disclosure.

The embodiments herein disclose a device and methods for providing an information transfer from a virtual or metaverse session to a best possible real world device, for example internet of things (IoT) device, in a user's real world environment. The embodiments provide an intelligent intuitive way of transferring content from the virtual world to the real world by correlation information of user's real device capabilities. Referring now to the drawings, and more particularly toFIGS.2through18, where similar reference characters denote corresponding features consistently throughout the figures, there are shown embodiments.

FIG.2is a block diagram illustrating an electronic device200for enabling a context transfer from a virtual world to a real world, according to one or more embodiments. The electronic device200comprises a processor202, a communication module204, and a memory module206. The electronic device200may be a real world device (also referred to as a “user device”) present in the real world environment of the user. For example, a real world device may correspond to a physical device that has the capability of connecting to a virtual environment. Examples of the electronic device200may be, but not limited to, a desktop, a laptop, a smart phone, a personal digital assistant, a wearable device, and so on.

In one or more embodiments herein, the processor202may be configured to detect at least one user in an on-going virtual session. The virtual session may be implemented in a metaverse environment, or any other virtual environment known to one of ordinary skill in the art. A plurality of virtual reality (VR) elements may be utilized for interfacing of the electronic device200in the virtual session.

FIG.3is a flowchart illustrating a method300for interfacing (or connecting) the VR elements by the electronic device200in the virtual session, according to one or more embodiments. The method300discloses managing (or obtaining) a plurality of physical properties of the virtual session, as depicted in operation302. The plurality of physical properties may be, but not limited to, a gesture, movement, context, and so on. Subsequently, the method300discloses rendering a plurality of visual elements of the virtual session, as depicted in operation304. Thereafter, the method300discloses providing information regarding starting and ending of the virtual session, as depicted in operation306. The various actions in method300may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed inFIG.3may be omitted.

The processor202may determine (or obtain) a context of at least one virtual device content present in the virtual session, in response to detecting the user. The virtual device content may be, but not limited to, an audible content, a visual content, an audio-visual content, an ambience of the virtual session, and so on.

FIG.4is a flowchart illustrating a method400for determining the context of the virtual device content, according to one or more embodiments. The method400discloses scanning a virtual environment for identifying the virtual device content which has been showcased in the virtual session, as depicted in operation402. In one or more examples, virtual device content may be showcased by displaying the content more prominently than other content. For example, the size of the showcased content may be displayed larger than other content, or may be highlighted with a different color than other content. The virtual device content may be from at least one participating user or a central entity. Subsequently, the method400discloses attaching (or applying) a listening mechanism to the identified virtual device content, as depicted in operation404. The listening mechanism monitors and observes for changes in the virtual device content. Thereafter, the method400discloses determining the context of the virtual device content, based on the listening mechanism, as depicted in operation406. For example, the context may comprise at least one of a song to which the user expresses interest by listening and dancing to the song, an ambience to which the user expresses interest, a document etc. The various actions in method400may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed inFIG.4may be omitted. The listening mechanism may be described as a pre-determined mechanism or a detecting mechanism. In one or more examples, the mechanism may be implemented as an algorithm.

The processor202may derive (or obtain or extract) at least one user interest towards the virtual device content using at least one multimodal user input within the virtual session. The user interest may be derived using at least one of an explicit user input and an implicit user input in the virtual session. Examples of the multimodal user input may be, but not limited to, a speech input, a gaze input, a gesture input, etc.

FIG.5is a flowchart illustrating a method500for deriving at least one user interest using the multimodal user input, according to one or more embodiments. The method500discloses listening (or detecting) to at least one avatar action of the user, as depicted in operation502. Subsequently, the method500discloses deriving (or obtaining or extracting) correlation information between the avatar action and a plurality of generalized interests of the user, as depicted in operation504. The correlation information may be derived through score representation. For example, a score may be assigned between an avatar action and an interest representing a correlation between these items. If the score is above a threshold, the avatar action and interest may be designated as being correlated with each other. Thereafter, the method500discloses differentiating (or classifying) between objects in the real world and the virtual session based on the derived correlation, to understand or determine an intent of the user, as depicted in operation506. The method500further discloses deriving the user interest based on the intent of the user, as depicted in operation508. The various actions in method500may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed inFIG.5may be omitted.

The processor202may correlate the user interest with at least one content capability of a plurality of real world devices of the user. The content capability of the real world devices may be, but not limited to an audio capability, a video capability, an internet capability, a display capability, an ambience control capability, and so on.

FIG.6is a flowchart illustrating a method600for correlating the user interest with the content capability of the plurality of real world devices, according to one or more embodiments. The method600discloses analyzing (or obtaining) at least one user activity in the real world for finding at least one real-world context relevant to the derived user interest, as depicted in operation602. Subsequently, the method600discloses breaking down (or classifying) the plurality of real world devices into capability skeletons for extracting the content capability of the real world devices, as depicted in operation604, on finding the real-world context. In one or more examples, a capability skeleton is a data structure that specifies one or more capabilities of a device. Thereafter, the method600discloses finding the real world devices which are in user proximity, as depicted in operation606, on breaking down the plurality of real world devices into the capability skeletons. The method600further discloses analyzing the capability skeletons and stored metadata information of the user interest for identifying and recommending (or providing) at least one real world device from the proximity real world devices, as depicted in operation608. The various actions in method600may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed inFIG.6may be omitted.

Thus, the processor202may recommend at least one real world device to the user for transferring the context of the virtual device content based on the analyzed capability skeletons of the real world devices and stored metadata information of the user interest.

In one or more embodiments herein, the processor202may comprise one or more of microprocessors, circuits, and other hardware configured for processing. The processor202may be configured to execute instructions stored in the memory module206.

In one or more examples, the processor202may be at least one of a single processer, a plurality of processors, multiple homogeneous or heterogeneous cores, multiple Central Processing Units (CPUs) of different kinds, microcontrollers, special media, and other accelerators. The processor202may be an application processor (AP), a graphics-only processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU), and/or an Artificial Intelligence (AI)-dedicated processor such as a neural processing unit (NPU).

In one or more embodiments herein, the communication module204may be configured to enable communication between the electronic device200and a server through a network or cloud. The server may be configured or programmed to execute instructions of the electronic device200. The communication module204through which the electronic device200and the server communicate may be in the form of either a wired network, a wireless network, or a combination thereof. The wired and wireless communication networks may comprise but not limited to, GPS, GSM, LAN, Wi-Fi compatibility, Bluetooth low energy as well as NFC. The wireless communication may further comprise one or more of Bluetooth (registered trademark), ZigBee (registered trademark), a short-range wireless communication such as UWB, a medium-range wireless communication such as Wi-Fi (registered trademark) or a long-range wireless communication such as 3G/4G or WiMAX (registered trademark), according to the usage environment.

In one or more embodiments herein, the memory module206may comprise one or more volatile and non-volatile memory components which are capable of storing data and instructions to be executed. Examples of the memory module206may be, but not limited to, NAND, embedded Multi Media Card (eMMC), Secure Digital (SD) cards, Universal Serial Bus (USB), Serial Advanced Technology Attachment (SATA), solid-state drive (SSD), and so on. The memory module206may also include one or more computer-readable storage media. Examples of non-volatile storage elements may include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. In addition, the memory module206may, in some examples, be considered a non-transitory storage medium. The term “non-transitory” may indicate that the storage medium is not embodied in a carrier wave or a propagated signal. However, the term “non-transitory” should not be interpreted to mean that the memory module206is non-movable. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in Random Access Memory (RAM) or cache).

FIG.2shows example modules of the electronic device200. However, as understood by one of ordinary skill in the art, the embodiments are not limited to this configuration. In other embodiments, the electronic device200may include less or more number of modules. Further, the labels or names of the modules are used only for illustrative purpose and does not limit the scope of the embodiments. One or more modules may be combined together to perform same or substantially similar function in the electronic device200.

FIG.7is a block diagram illustrating a processor202for enabling the context transfer from a virtual session to a real world according to one or more embodiments. The virtual session may be a metaverse session which is implemented in a meta world. However, as understood by one of ordinary skill in the art, the embodiments are not limited to a metaverse session and may include any suitable virtual environment known to one of ordinary skill in the art. The processor202further comprises a plurality of virtual modules702, a bridge module704, and a plurality of real modules706. The plurality of virtual modules702may be metaverse modules that perform one or more functions in a virtual environment. The virtual modules702further comprise a session manager710, a virtual scanner712, and an interest detection module714. In one or more embodiments herein, the session manager710may be configured for detecting at least one user in the virtual session or in the metaverse session. The session manager710may serve as an interface to the plurality of VR elements in the virtual session.

FIG.8is a block diagram illustrating the VR elements800in the virtual session or metaverse session, according to one or more embodiments. The processor202may interface with the VR elements800through the session manager710for implementing the virtual session. The VR elements800comprise a physics module802, a renderer module804, and a session module806. In one or more embodiments herein, the physics module802may be configured for managing(or obtaining) a plurality of physical properties of the virtual session, as depicted in operation808. Examples of the physical properties may be, but not limited to, a gesture, movement, and context which are performed by the user. In one or more embodiments herein, the renderer module804may be configured for rendering a plurality of visual elements in the virtual session, as depicted in operation810. The renderer module804may be used for scanning the rendered visual elements in the virtual scanner712. In one or more embodiments herein, the session module806may be configured for providing information regarding starting and ending the virtual session, as depicted in operation812.FIG.8shows example modules of the VR elements800. However, as understood by one of ordinary skill in the art, the embodiments are not limited to the configuration illustrated inFIG.8. In other embodiments, the VR elements800may include less or more number of modules. Further, the labels or names of the modules are used only for illustrative purpose and does not limit the scope of the embodiments. One or more modules may be combined together to perform same or substantially similar function in the VR elements800.

In one or more embodiments herein, the virtual scanner712may be configured for determining the context of at least one virtual device content present in the virtual session in response to detecting the user. The virtual scanner712may scan or check the virtual environment on detecting the user in the virtual session for determining the virtual device content. Examples of the virtual device content may be, but not limited to, a song being played in the virtual session, a video being played in the virtual session, an ambience in a virtual session, an utterance by a user during a virtual session, and a slide or document from a virtual meeting in the virtual session.

In one or more embodiments herein, the virtual scanner712may be configured for scanning the virtual environment for different types of content (e.g., the virtual device content showcased in the virtual session). The virtual device content may be retrieved from the scanned content in an immediate vicinity of the user. The virtual scanner712may attach a listening mechanism using a content observer724to the identified virtual device content.

FIG.9is a block diagram illustrating an example functional representation900of virtual scanning in the metaverse session using the virtual scanner712, according to one or more embodiments. As illustrated inFIG.9, the virtual scanner712interfaces with the renderer module804of the VR elements800through the session manager710and scans the rendered metaverse environment. The virtual scanner712identifies at least one possible virtual device content such as audio, video, document, device based configuration, based on the scan of the metaverse environment. The virtual scanner712attaches a listening mechanism to the identified virtual device content using the content observer724.

For example, the virtual scanner712may request the session manager710for information related to the virtual device (VD) and obtain a relevant match with a virtual device as a virtual audio source and a virtual capability as audio and internet based on the information received from the session manager710. The virtual scanner712then attaches an observer instance to the audio source using the content observer724. Further, the virtual scanner712determines the context of the virtual device content based on the listening mechanism.

In one or more embodiments herein, the interest detection module714may be configured for deriving at least one user interest towards the virtual device content using at least one multimodal user input within the virtual session. Examples of the user interest may be, but not limited to, at least one avatar action of the user such as explicit statements and implicit remarks through voice, body language/gesture, etc.

In one or more embodiments herein, the interest detection module714may utilize a multimodal interest engine726for deriving the user interests. The multimodal interest engine726further comprises a multimodal input module738and a generic interest detection engine740.

The multimodal input module738may be configured for listening to or detecting the avatar actions of the user through at least one multimodal user input. The generic interest detection engine740utilizes an artificial intelligence (AI) model which may be configured for deriving a correlation between at least one avatar action and a plurality of generalized interests of the user. The generic interest detection engine740may differentiate between objects in the real world and the virtual world based on the correlation to understand an intent of the user. The generic interest detection engine740may further derive the user interest based on the intent of the user.

In one or more embodiments herein, the interest detection module714comprises a data collector728for collecting metadata information of the derived at least one user interest.

FIG.10is a diagram illustrating an example functional representation1000of deriving the user interest in the metaverse session using the interest detection module714.

The multimodal input module738of the interest detection module714may be used to detect if the user is interested in transferring the virtual contents to the real world. The user interest may be expressed in any form through explicit statements, implicit remarks, body language, gestures, actions, etc. The multimodal input module738may listen for any avatar action in any form such as through voice, body language, and so on. For example, the user may verbally say “Queue this song for me”.

The generic interest detection engine740is an AI model that generalizes different ways in which a VR avatar may express a corresponding interest. The generic interest detection engine740captures interest across various modes of expression to understand an intent of the user. The interest may be based on a selection of an item to be displayed or played in the virtual environment, or the context of an utterance by a user.

The data collector728collects actionable material on the interested meta-object from the session manager interface. The actionable materials are capabilities of user interest devices, where capabilities may be the actions that may be performed on the device. For example, a user may play and pause songs on a speaker, or the user may control brightness, saturation, color and ambience of a light etc.

In one or more embodiments herein, the bridge module704acts as an interface between the plurality of virtual modules702and the plurality of real modules706. The bridge module704comprises a content stream database716. The content stream database716collects the metadata information from the data collector728of the virtual session and stores the metadata information.

The real modules706comprise a scenario analyzer718, a capability abstractor720, and an intelligent device identification engine722. In one or more embodiments herein, the scenario analyzer718may be configured for analyzing at least one user activity in the real world for finding at least one real-world context relevant to the derived user interest, as obtained from the interest detection module714.

FIG.11is a diagram illustrating an example functional representation1100of analyzing at least one user activity in the real world using the scenario analyzer718, according to one or more embodiments. The scenario analyzer718may be in communication with the content stream database716of the bridge module704and the real world. For each metadata information of the derived user interest stored in the content stream database716, the scenario analyzer718observes user activity in the real world for a pre-defined period of time using the meta-context observer730to check when the metadata information is useful and relevant. The scenario analyzer718utilizes the inference model732to find and determine the real-world context which is relevant to the derived user interest. For example, as depicted, as a user schedules a party in the real world, the inference model732finds meta context ID MC02with content type device setting as relevant from the metadata information, which is available in the content stream database716.

In one or more embodiments herein, the capability abstractor720may be configured for correlating the derived user interest with at least one content capability of a plurality of real world devices708of the user. The capability abstractor720breaks down the plurality of real world devices708into capability skeletons for extracting the content capability of each real world device708, on finding the relevant real-world context. The content capabilities of the real world device708may be, but not limited to an audio, video, internet, brightness, and saturation.

FIG.12is a diagram illustrating an example functional representation1200of breaking down the real world devices708into capability skeletons in the real world using the capability abstractor720, according to one or more embodiments. The capability abstractor720obtains each real world device708object that belongs to the user from the user's cloud account via the IoT cloud1202. The capability abstractor720breaks down the real world device708object into content capability skeletons and extracts various content capabilities of the real world devices708. The capability abstractor720utilizes model info and device data of each real world device708and creates the capability skeleton. For example, if a TV has the capability to play audio, video and some content over the internet, then the capability skeleton of the TV becomes as follows: [Audio, Video, and Internet].

In one or more embodiments herein, the intelligent device identification engine722may be configured for recommending at least one real world device708to the user, wherein the context of the virtual device content may be transferred to the recommended at least one real world device708, based on a correlation of the user interest with the content capability. The intelligent device identification engine722identifies the best possible real world device present in the user's real world environment based on the correlation of the content capabilities of the real world devices708of the user. The intelligent device identification engine722finds the best possible real world device from the real world devices708which is in user proximity, on breaking down the plurality of real world devices708into the capability skeletons.

The intelligent device identification engine722further comprises a software compatibility model734and a device compatibility model736. In one or more embodiments herein, the software compatibility model734may be configured for performing deep systemic checks to verify whether the software, required to display the context from the virtual session or metaverse session, is present in the recommended real world device708. In one or more embodiments herein, the device compatibility model736may be configured for analyzing the capability skeletons and the stored metadata information of the user interest using an AI model. Based on this analysis, the device compatibility model736identifies and recommends the real world device708from the proximity real world devices708for transferring the context of the virtual device content.

FIG.13is a diagram illustrating an example functional representation1300of identifying and recommending at least one real world device708to the user using the intelligent device identification engine722, according to one or more embodiments. As illustrated inFIG.13, the software compatibility model734may filter out software incompatible devices. For example, from the device ID's D01, D02, and D03, the device D01-TV is filtered out because this device does not meet the requirements for bulb attributes like brightness, saturation and color.

In one or more examples, the device compatibility model736is an AI model that outputs the probability of each of the user's cloud devices for being the best possible real world device708for displaying the transferred content. In one or more embodiments herein, the device compatibility model736is an unsupervised model.

For example, for each real world device708, data such as proximity engine1302, device properties1304, content data point1306, queue analyzer1308, and feedback bias1310, act as input nodes to the device compatibility model736. The proximity engine1302uses ultra-wideband (UWB) technology to output the distance of the real world devices708from the user.

The device properties1304field comprises of device information such as device_id, device_type, etc. This information helps in establishing a correlation in a user's choice of the real world device708and content transferred. The content data point1306field comprises of metadata of the transferred content. The queue analyzer1308analyzes the content types in the queue of transfer requests, as the best device needs to be found for all the transfers that happen during a metaverse session. The best device is the device on which the user may access the data with minimal effort. The feedback bias1310node may understand the user's choice over a period of time. This feedback effectively creates a personalized model with an understanding of user's preference of real world devices708for a specific type of transferred content. Thus, the device compatibility model736analyzes the data nodes and recommends the best possible real world device708for transferring the context of the virtual device content.

FIG.7shows example modules of the processor202. However, as understood by one of ordinary skill in the art, the embodiments are not limited to this configuration. In other embodiments, the processor202may include less or more number of modules. Further, the labels or names of the modules are used only for illustrative purpose and does not limit the scope of the embodiments. One or more modules may be combined together to perform a same or substantially similar function in the processor202.

FIG.14is a flow chart illustrating a method1400for enabling context transfer from the virtual session to the real world, according to one or more embodiments. The method1400begins with detecting, by the session manager710of the electronic device200, at least one user in an on-going virtual session, as illustrated in operation1402. Subsequently, the method1400discloses determining, by the virtual scanner712of the electronic device200, a context of at least one virtual device content present in the virtual session, in response to detecting the user, as illustrated in operation1404. The virtual device content is detected by scanning the virtual environment once the user is detected. Thereafter, the method1400discloses deriving, by the interest detection module714of the electronic device200, at least one user interest towards the virtual device content, as illustrated in operation1406. The user interest is derived using the multimodal user input within the virtual session. The method1400further discloses correlating, by the capability abstractor720of the electronic device200, the user interest with at least one content capability of a plurality of real world devices708of the user, as illustrated in operation1408. The method1400further discloses recommending, by the intelligent device identification engine722of the electronic device200, at least one real world device708to the user for transferring the context of the virtual device content based on correlation, as illustrated in operation1410. The real world device708may be a real world device which is detected and recommended based on the correlation of the user interest with the content capabilities of the real world devices. The various actions in method1400may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed inFIG.14may be omitted.

FIG.15is a diagram illustrating an example use case1500where user expresses interest towards a song in a metaverse session of a party. As illustrated, as the user starts the metaverse session, the session manager710detects the user in the metaverse party. The user may listen to a particular song or may express interest in a song. The virtual scanner712scans the metaverse environment and identifies audio as the context from a virtual device content using the content observer724. The interest detection module714derives the user interest as “Wow! I like this song” using the multimodal interest engine726, and the user interest is stored in the data collector728. Once the user exits the metaverse session, the scenario analyzer718finds the real-world context relevant to the user interest. If the user listens to similar genre song in the real world at a later point, the capability abstractor720correlates the user interest with at least one content capability of the plurality of real world devices708through the IoT cloud1202. The intelligent device identification engine722recommends best possible device to the user such as Galaxy Home based on proximity, capability and usage patterns at the right time and suggests as “You liked this song in John's Party. Do you want to listen?”. Based on the user response, the song is transferred to the recommended device and played through it.

FIGS.16A and16Bare diagrams illustrating an example use case1600where user expresses interest towards ambience of a party in a metaverse session. As illustrated inFIGS.16A and16B, as the user starts the metaverse session, the session manager710detects the user in the metaverse party. The user likes the ambience of the party and may express interest. The virtual scanner712scans the metaverse environment and identifies ambience as the context from a virtual device content using the content observer724. The interest detection module714listens the avatar action as “Lighting is trippy!” using the multimodal input module738of the multimodal interest engine726. The generic interest detection engine740of the multimodal interest engine726implements voice-positive interest correlation and derives user interest. The user interest along with content type as device settings and category as ambience are stored in the data collector728.

Once the user exits the metaverse session, the scenario analyzer718finds the real-world context relevant to the user interest as MC01as meta context ID, where content metadata may comprise brightness, saturation and color. For example, when a user schedules a party in future, the capability abstractor720correlates the user interest with at least one content capability of the plurality of real world devices708through the IoT cloud1202. The intelligent device identification engine722checks the software compatibility734and device compatibility736and recommends best possible device to the user through a speaker, based on proximity, capability and usage patterns at the right time and suggests as “You liked the ambience in Keith's meta party. Do you want to try it?”. Based on the user response, the ambience is transferred to the best possible devices in the User's living room.

FIGS.17A and17Bare diagrams illustrating an example use case1700where user is in a metaverse office meeting.

As depicted, as the user starts the metaverse session, the session manager710detects the user in the metaverse meeting. The user is working in the metaverse session. The virtual scanner712scans the metaverse environment and identifies document as the context from a virtual device content using the content observer724. The interest detection module714identifies the avatar action as “Dennis, you make an Interesting claim at slide6” using the multimodal input module738through inputs of note taken by the user and voice given to the multimodal interest engine726. The generic interest detection engine740of the multimodal interest engine726implements voice and body language-positive interest correlation and derives the user interest. The user interest along with content type as presentation and broad category as document are stored in the data collector728.

Once the user exits the metaverse session, the scenario analyzer718finds the real-world context relevant to the user interest as MC01as meta context ID, where content metadata may comprise file name, cloud link, slide marker, and owner. If the user is working in real world, the capability abstractor720correlates the user interest with at least one content capability of the plurality of real world devices708through the IoT cloud1202. The intelligent device identification engine722checks the software compatibility734and device compatibility736and recommends best possible device to the user through a display, based on proximity, capability and usage patterns at the right time. Thus, the document is shown as a dismissible view that may be saved on the best possible device such as tablet or PC or laptop.

FIGS.18A,18B and18Care diagrams illustrating an example use case1800where user likes a video and song in the metaverse.

As depicted in theFIG.18A, as the user starts the metaverse session, the session manager710detects the user in the metaverse party. The virtual scanner712scans the metaverse environment and identifies audio as the context from a virtual device content using the content observer724. The interest detection module714identifies the avatar action as “Wow! I like this song” using the multimodal input module738through inputs of voice and user dancing given to the multimodal interest engine726. The generic interest detection engine740of the multimodal interest engine726implements voice and body language-positive interest correlation and derives the user interest. The user interest along with content type as audio and broad category as multimedia is stored in the data collector728.

Simultaneously, as illustrated in theFIG.18B, the virtual scanner712scans the metaverse environment and identifies video as the context from a virtual device content using the content observer724. The interest detection module714identifies the avatar action as “Wow! I like this shot in the movie” using the multimodal input module738through input of voice given to the multimodal interest engine726. The generic interest detection engine740of the multimodal interest engine726implements voice positive interest correlation and derives the user interest. The user interest along with content type as video and broad category as multimedia is stored in the data collector728.

Once the user exits the metaverse session, the scenario analyser718finds the real-world context relevant to the user interest as MC01and MC02as meta context ID, where content metadata may comprise artist name, producer, writer, title, song title, release date, genre, duration or track duration. For example, if the user browses a content in the real world device such as a television or phone, the capability abstractor720correlates the user interest with at least one content capability of the plurality of real world devices708through the IoT cloud1202. The intelligent device identification engine722checks the software compatibility734and device compatibility736and recommends best possible real world device to the user through a display, based on proximity, capability and usage patterns at the right time. Thus, content such as the audio and video relevant to the browsed content is queued and displayed for the user, based on the user interest, as a dismissible view on the best possible device such as TV and speaker.

The embodiments disclosed herein may be implemented through at least one software program running on at least one hardware device. The modules shown inFIG.7include blocks which may be at least one of a hardware device, or a combination of hardware device and software module.

The embodiments disclosed herein describe a device and methods for transferring a user preferred virtual context to a best possible real world device after terminating the virtual session. Therefore, it is understood that the scope of the protection is extended to such a program and in addition to a computer readable means having a message therein, such computer readable storage means contain program code means for implementation of one or more operations of the method, when the program runs on a server or mobile device or any suitable programmable device. The method is implemented in at least one embodiment through or together with a software program written in e.g. Very high speed integrated circuit Hardware Description Language (VHDL) another programming language, or implemented by one or more VHDL or several software modules being executed on at least one hardware device. The hardware device may be any kind of portable device that may be programmed. The device may also include means which could be e.g. hardware means like e.g. an ASIC, or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. The method embodiments described herein could be implemented partly in hardware and partly in software. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.

The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of embodiments and examples, those skilled in the art will recognize that the embodiments and examples disclosed herein may be practiced with modification within the spirit and scope of the embodiments as described herein.