LIVE SHARING OF VIRTUAL COLLABORATION IN SOCIAL NETWORKS

Techniques are described with respect to a system, method, and computer product for a method for sharing virtual environment collaboration. An associated method includes analyzing a virtual environment and determining a plurality of contextual information and a degree of shareability associated with the virtual environment based on the analysis. The method further including presenting the virtual environment to a social media network associated with a user based on the degree of shareability.

FIELD

This disclosure relates generally to virtual reality and augmented reality, and more particularly to computing systems, computer-implemented methods, and computer program products configured to provide live sharing of virtual collaboration within social networks.

Social media networks and sites have become one of the most universally popular mechanisms for communication and disseminating information. For example, users may utilize live streaming functionalities in order to share live content amongst social media network audiences and receive real-time feedback from viewers. However, the aforementioned functionalities may be performed in a public manner creating privacy, security, and safety risks not only for the content uploaders, but also the viewers and commentors due to the accessibility to the general public. For example, location sharing done by a live-streamer may pose an immediate risk to the live-streamer and their surrounding environment.

In addition, social media networks may be utilized as platforms for hosting virtual collaborations in which users across geographic locations meet in a virtual centralized location to communicate and exchange content; however, these virtual locations may also run into privacy, security, and safety issues due to public accessibility in addition to suffer from limitations that arise from a context of the virtual collaboration not being established.

SUMMARY

Embodiments relate to a method, system, and computer program product for sharing virtual environment collaboration. In some embodiments, the computer-implemented method for sharing virtual environment collaboration comprises analyzing a virtual environment; determining a plurality of contextual information and a degree of shareability associated with the virtual environment based on the analysis; and presenting the virtual environment to a social media network associated with a user based on the degree of shareability.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosed herein; however, it can be understood that the disclosed embodiments are merely illustrative of the claimed structures and methods that may be embodied in various forms. Those structures and methods may, however, be embodied in many different forms and should not be construed as limited to the exemplary embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete and will fully convey the scope to those skilled in the art. In the description, details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the presented embodiments.

In the context of the present application, where embodiments of the present invention constitute a method, it should be understood that such a method is a process for execution by a computer, i.e., is a computer-implementable method. The various steps of the method therefore reflect various parts of a computer program, e.g., various parts of one or more algorithms.

Also, in the context of the present application, a system may be a single device or a collection of distributed devices that are adapted to execute one or more embodiments of the methods of the present invention. For instance, a system may be a personal computer (PC), a server or a collection of PCs and/or servers connected via a network such as a local area network, the Internet and so on to cooperatively execute at least one embodiment of the methods of the present invention.

As used herein, the term “virtual collaboration” includes any mechanism for hosting digital sessions carried out via technology-mediated communications which may include, but are not limited to, live broadcasting services, online video sharing/hosting, gaming platform services, social media platform content (e.g., forums, posts, streaming sessions, etc.), virtual matchmaking services, videotelephony/video-conferencing, augmentative and alternative communication (AAC), voice over internet protocol (VOIP), metaverses, and other applicable form of telecommunications known to those of ordinary skill in the art.

The following described exemplary embodiments provide a method, computer system, and computer program product for sharing virtual environment collaboration. Social networking systems have become one of the most popular forms for steadily and smoothly streaming media content and facilitating interactions among persons and organizations. Social network systems have further become mechanisms to connect and share information across users, in which social network systems have the potential to transform countless aspects of communication within human life. For example, social networking has started to be integrated into virtual, augmented, mixed, and/or extended reality environments allowing users to interact and communicate among each other in real-time with generated images, sounds, haptic feedback, and other sensations to augment a real world environment. However, this combination of technology exposes users to privacy concerns regarding virtual collaboration environments involving confidential and/or sensitive subject matter or more intimate virtual settings. Therefore, the present embodiments have the capacity to improve the technical fields of virtual/augmented reality and social networking by not only generating privatized virtual environments for presentation and collaboration within social networks, but also modifying virtual environment privacy settings based on the determined context of the virtual environment and/or the users present.

It is further understood that although this disclosure includes a detailed description on cloud-computing, implementation of the teachings recited herein are not limited to a cloud-computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed.

Referring now toFIG.2, a functional block diagram of a networked computer environment illustrating a computing environment for an augmented reality experience merging environment system200(hereinafter “system”) comprising a server210communicatively coupled to a database215, an virtual environment analyzer module220communicatively coupled to a virtual environment analyzer module database230, a collaboration allocation module240communicatively coupled to a collaboration allocation module database250, and a computing device260associated with a user270, each of which are communicatively coupled over WAN102(hereinafter “network”) and data from the components of system200transmitted across the network is stored in database215.

In some embodiments, server210is configured to operate a centralized platform serving as a cloud-based virtual environment analyzer and privacy allocating platform for social networks within virtual/augmented reality environments. Server210is configured to provide a mechanism of user270to establish preferences, privacy settings, and the like via accessing one or more user interfaces and application programming interfaces (APIs) provided to computing device260. Traditionally, social media content (e.g., live-stream media, posts, shares, etc.) may be published by social media users in which they are viewed within the virtual environment hosted by a VR/AR service providing platform; however, the centralized platform is configured to allow user270to view virtual collaborations within the virtual environment associated with the applicable social network based on a plurality of privacy rights assigned to the social media content and/or the virtual environment by collaboration allocation module240. The centralized platform further allows user270to make adjustments to the privacy rights based upon preferences and/or the context of the social media content/virtual environment. For example, the virtual collaboration may be a multi-party discussion (i.e., telecommunication meeting) pertaining to sensitive subject matter, in which user270may access the centralized platform in order to determine not only who has access to the virtual collaboration presented via the social network, but also the role of the individuals within the virtual environment.

In some embodiments, server210is associated with one or more web crawlers configured to search and extract data from various internet-based data sources for storage in database215, in which the extracted data may be processed by one or more modules of virtual environment analyzer module220and collaboration allocation module240. Monitoring agents track interests from various external sources for which the user270has given access permission. Furthermore, one or more monitoring agents associated with server210may track social network relationships, activities, and the like associated with user270resulting in storage of applicable data within database215. In particular, monitoring agents track and analyze the user's online activity, such as web searches, social media posts/interactions, online content consumption, including textual, audio, video content, AR/VR experiences, etc. Server210can categorize the user's actions into a predefined set of interest categories, and use of an interest and knowledge level ascertained from the tracking makes it possible to track temporal changes in interests of user270and ensures that interests are up to date and stored in database215. In some embodiments, the tracking of interest and knowledge level allow server210to utilize the tracked actions to calculate and assign knowledge levels for various subject matters relevant to the virtual collaborations offered in a social network and/or a VR/AR system.

Virtual environment analyzer module220is configured to detect virtual environments and ascertain data associated with virtual environments including, but not limited to, contextual information, geographic data, spatial/temporal data associated with applicable users and data sources, and the like. In addition, virtual environment analyzer module220may detect dialogues, resources, and the like associated with the virtual environment for the purpose of determining whether the social media content attempting to be shared is subject to monitoring for sensitivity or privacy. The data that is processed and analyzed by virtual environment analyzer module220is configured to be stored in virtual environment analyzer module database230, in which virtual environment analyzer module database230is designed to function as a repository continuously updated with not only data ascertained by analyses performed by virtual environment analyzer module220, but also other applicable data sources including, but not limited to, crowdsourcing platforms, internet based data sources ascertained by web crawlers (e.g., social media platforms,), inputs of user270provided to the centralized platform, and the like. In some embodiments, contextual factors, parameters, and/or user preferences such as, for example, current weather conditions, a geographical location, physical features and styling, likes and dislikes, user's purchase and/or interest, and the like may be accounted for in virtual environment analyzer module database230. As described herein, contextual information may include, but is not limited to, user location, topic/subject matter, date/time, environment/virtual object theme/setting, a participant threshold of user270associated with the virtual collaboration presented by the applicable social media network, participant dialogue concept, an event within the virtual environment (e.g., E-sport, dining experience, shopping experience, etc.), occurrences of a predetermined pattern of content or activity within the event, or any other applicable contextual-based data known to those of ordinary skill in the art. Virtual environment analyzer module220may further utilize one or more techniques to analyze virtual environments including, but not limited to, natural language processing (NLP), image analysis, topic identification, virtual object recognition, setting/environment classification, and any other applicable artificial intelligence and/or cognitive-based techniques known to those of ordinary skill in the art. Virtual environment analyzer module220is further tasked with providing techniques that facilitate automatic, reliable performance of a point cloud object-environment segmentation task in order to analyze virtual environments. For example, virtual environment analyzer220may provide the capability to perform automatic segmentation of a 3D point cloud into object and virtual environment segments by progressively learning the object-environment segmentation from tracking sessions in augmented reality (AR) applications associated with the centralized platform. In some embodiments, segmenting of the virtual environment allows virtual environment analyzer220to not only identify one or more virtual collaborations occurring within the virtual environment, but also ascertain various elements associated with virtual collaborations, such as, but not limited to, amount of users present, level of user engagement associated with a virtual collaboration, topics/subjects discussed in a virtual collaboration, and the like.

Collaboration allocation module240is configured to analyze virtual collaborations in order to determine if privatization and security is warranted for the integration of a presentation of social media content in the virtual environment. In some embodiments, collaboration allocation module240utilizes the segmenting performed by virtual environment analyzer220to perform partitioning of the detected virtual collaboration in order to assign the degree of shareability to one or more segments of the virtual collaboration derived from the partitioning. Partitioning of the virtual collaborations enables the degree of shareability to be allocated to the segments based upon one or more of the contextual information, current topic/subject being discussed in the virtual collaboration, user preferences, level of user engagement with particular virtual collaborations, and the like. For example, a segment of virtual collaboration may pertain to a sensitive subject matter in which the degree of shareability assigned to the applicable segment determines which users of the virtual collaboration have access to the particular virtual collaboration during the time period in which the sensitive subject matter is being discussed. Thus, certain users have access to the particular virtual collaboration at certain times of the discussion based upon the assigned degree of shareability of the segment which determines each users' access privileges during that segment.

In addition, collaboration allocation module240is further configured to analyze a user profile associated with user270in order to ascertain user data comprising, but not limited to, user personal data, user social media data, user activity data, user preferences, user inputs provided to the centralized platform, and the like for the purpose of optimizing the privacy and security of virtual collaborations associated with user270based on their history of interactions with social media content and their social media connections. For example, if user270is not connected (i.e., friends on the particular social network) with another user then either the virtual collaboration session and/or the social media content presented within the virtual reality is presented to the other user with limited accessing right assigned by collaboration allocation module240(e.g., other user only has viewing access to the virtual collaboration, cannot virtually vote or opine on a matter during dialogue, etc.).

In addition, collaboration allocation module240is tasked with detecting both users within the virtual collaboration and users attempting to gain access to the virtual collaboration via the applicable social network in order to classify said users as “participants” or “viewers”, in which a participant is assigned one or more capabilities to contribute to the virtual collaboration (e.g., screen sharing, vote casting, speaking rights, session recording, etc.) and a viewer may only view the virtual collaboration via the applicable social network. In addition, collaboration allocation module240is tasked with determining whether one or more components of a virtual environment and social media content shared by the applicable social media platform are shareable or un-shareable, in which shareability is a metric associated with who and what collaboration allocation module240is able to share with virtual collaboration viewers and participants. In some embodiments, classification of groups of users associated with social networks may be based on one or more of an engagement level, a virtual behavior pattern, and role associated with user270.

It should be noted that in the acquiring, processing, utilizing, etc. of data by the proposed system, laws are observed, and privacy of user270is protected. In some embodiments, collaboration allocation module240is configured to protect the virtual environment or virtual collaboration by rendering content of the applicable social media network relative to the applicable user based on the contextual information, or privatizing one or more components of the rendered content based on shareability determined by collaboration allocation module240. For example, live streamed content that has been deemed as sensitive or private may be rendered to user270from another user operating on the social network based upon collaboration allocation module240ascertaining the position (spatial information) of user270within the virtual environment. In addition, temporal information and shareability of virtual environments and social media content associated with the location of user270is factored into collaboration allocation module240determining to render the content for presentation to user270.

Referring now toFIG.3, an example architecture300of virtual environment analyzer module220and collaboration allocation module240are depicted, according to an exemplary embodiment. In some embodiments, virtual environment analyzer module220comprises a contextual module310, a virtual environment (VE) classification module320, and a visualization module330. Collaboration allocation module240comprises a user profile module340, a collaboration assessment module350, a machine learning module360, a privacy module370, and a social network module380.

Contextual module310is designed to determine the context of a virtual environment, in which the context may be established by one or more of virtual environment elements (e.g., setting, theme, virtual objects, etc.), dialogue/topic/presenter associated with virtual collaboration, social media network provider, detected geographic location or virtual environment location of the user270, and the like. For example, contextual module310may detect based on analysis of the virtual environment that user270is attending an E-sport event allowing contextual module310to determine that user270is a “public” setting where sensitive and/or private information associated with a virtual collaboration should not be transmitted or received. In another example, contextual module310may ascertain that subject matter of a virtual collaboration is sensitive based on applicable viewers and participants, or that user270is not engaged in the virtual collaboration due to a lack of activity within a virtual collaboration for a period of time, an utterance of one or more phrases indicating uncertainty/confusion, and the like. It should be noted that contextual module310may utilize cognitive/analytic systems, natural language processing (“NLP”), linear discriminant analysis (“LDA”), semantic analyzer, parsing functions, multi-media detection/analyses systems, computer visioning systems, and other applicable systems to ascertain contextual information of the virtual environment or dialogue/interactions viewers and participants associated with virtual collaborations.

VE classification module320is tasked with classifying the virtual environments based on one or more of activities occurring within the applicable virtual environment (e.g., dining, shopping, spectating, etc.), geographic location of user270, virtual objects of the virtual environment (e.g., virtual agents, chatbots, interactive virtual elements, and the like), dialogue/topic/subject matter of virtual collaboration within the virtual environment, and any other applicable factor configured to assist with classifying a virtual environment known to those of ordinary skill in the art. In some embodiments, classification of virtual environments and elements thereof may be performed via segmenting of the virtual environment; however, various techniques may also be utilized including, but not limited to, image/video analysis, parsing, tokenizing, 3D point cloud segmentation, or any other applicable VR/AR-based analysis mechanism known to those of ordinary skill in the art. It should be known that one of the underlying purposes of VE classification module320classifying virtual environments is to assist collaboration allocation module240with determining whether a virtual collaboration is associated with private and/or sensitive subject matter. For example, VE classification module320may ascertain that an applicable virtual collaboration is a multi-player race track provided in a virtual environment, in which the virtual environment is a racing video game setting facilitated by an applicable gaming provider hosted by a social media-based platform. The physical environment surrounding user270would need to be analyzed and mapped in order for the gaming provider to augment the race track provided to computing device260; thus, privacy concerns would arise in light of the virtual environment taking place in the physical environment being a private physical space of user270(e.g., living room, bedroom, etc.). Therefore, VE classification module320not only is configured to classify the virtual environment as a gaming space, but also VE classification module320is designed to classify the physical environment associated with user270as physical space where privacy concerns need to be addressed to sustain safe virtual collaborations.

Visualization module330is tasked with generating an AR-based visualization of virtual collaborations (e.g., a virtual reality model of the virtual collaboration or superimposing virtual content over a real-world view of the social media-based virtual collaboration in augmented reality) based on one or more analyses performed by collaboration allocation module240. In particular, virtual collaborations are continuously analyzed by collaboration allocation module240in order to determine permission levels (hereinafter referred to as “access rights”), roles of users associated with the virtual collaborations (e.g., presenter, viewer, participant, etc.), and the like. AR/VR-based virtual collaborations and/or social media content provided therein may be modified by visualization module330based on the role assigned to user270by collaboration allocation module240. For example, if collaboration allocation module240determines the role of user270within a virtual collaboration is a “participant”, then visualization module330modifies the virtual environment hosting the virtual collaboration and/or the social media content associated with the virtual collaboration in a manner that provides user270various functionalities such as, but not limited to, presentation integration, screen sharing, polling, or any other applicable host-based virtual collaboration features known those of ordinary skill of the art. In the instance in which collaboration allocation module240determines the role of user270as a “viewer”, then visualization module330modifies the virtual environment hosting the virtual collaboration and/or the social media content in a manner that provides user270only viewer functionality for the virtual collaboration. Visualization module330further supports rendering of the virtual environment in a manner in which user270may navigate the virtual environment while being engaged with the virtual collaboration. In some embodiments, user270provides an input indicating a desire to engage the virtual environment depicted on the social media network as a viewer or a participant via the centralized platform and/or within the virtual environment. User profile module340is configured to generate a user profile associated with user270and other applicable users in the virtual environments. The user profile is designed to indicate interests, current knowledge level regarding a topic of interest, social media activity, browsing patterns, user analytics, preferences, and the like associated with user270, in which the user profile is stored on collaboration allocation module database250. User profiles may further function as aggregations of user data associated with user270including, but not limited to, personal information, biological data, behavioral data, socio-demographic data, psychographic data, and the like continuously being updated via applicable data being received from one or more of server210, computing device260, and any other applicable data sources. The present invention may contain various accessible data sources, such as database215, that may include personal and/or confidential company data, content, or information the user wishes not to be processed. Processing refers to any operation, automated or unautomated, or set of operations such as collecting, recording, organizing, structuring, storing, adapting, altering, retrieving, consulting, using, disclosing by transmission, dissemination, or otherwise making available, combining, restricting, erasing, or destroying personal and/or confidential company data. Server210enables the authorized and secure processing of personal data. Server210provides informed consent, with notice of the collection of personal and/or confidential company data, allowing the user to opt in or opt out of processing personal and/or confidential company data. Consent can take several forms. Opt-in consent can impose on the user to take an affirmative action before personal and/or confidential company data is processed. Alternatively, opt-out consent can impose on the user to take an affirmative action to prevent the processing of personal and/or confidential company data before personal and/or confidential company data is processed. System200provides information regarding personal and/or confidential company data and the nature (e.g., type, scope, purpose, duration, etc.) of the processing. Server210provides the user with copies of stored personal and/or confidential company data. Server210allows the correction or completion of incorrect or incomplete personal and/or confidential company data. Server210allows for the immediate deletion of personal and/or confidential company data.

In some embodiments, the user profile is configured to be analyzed and verified by privacy module370, and privacy module370assigns the role to user270for a virtual collaboration based on the analysis and verification of the user profile. For example, the user profile might indicate that user270operates in a managerial capacity among participants in a virtual collaboration, in which privacy module370assigns the role of “host” to user270allowing user270to assign access rights to users to the virtual collaboration via the centralized platform, designate virtual collaboration functions to users, and the like. Upon verification and/or lack thereof by privacy module370to allow user270to attend the virtual collaboration, access rights to and/or shareability associated with the virtual collaboration are updated or re-assigned in accordance with the contextual information ascertained by contextual module310, in which the virtual collaboration is terminated with proper closure for user270by privacy module370if user270currently has access to the virtual collaboration. Thus, reassignment of permission levels is based on the plurality of contextual information, in which the contextual information factors the participant threshold of the presentation within the virtual environment.

Collaboration assessment module350is tasked with analyzing virtual collaborations for the purpose of ascertaining virtual collaboration specific information. For example, collaboration assessment module350is designed to ascertain virtual collaboration contexts, virtual collaboration user thresholds, virtual collaboration operation bandwidths, number of participants/viewers associated with a virtual collaboration, user profiles of participants/viewers, and any other applicable virtual collaboration derived information known to those of ordinary skill in the art. For example, in the instance in which the virtual collaboration is a VR/AR based gaming experience, collaboration assessment module350is designed to analyze and determine the type of virtual collaboration taking place, the degree of shareability of the particular virtual collaboration, the number of participating gamers as opposed to viewers, the respective user profiles of the aforementioned, the gaming experience threshold and capacities, and the like. In some embodiments, collaboration assessment module350is configured to determine a degree of shareability associated with the virtual environment and/or the virtual collaboration within the virtual environment based on various factors including, but not limited to, contextual information received from contextual module310, type of virtual collaboration, analyses performed on the user profiles, social network specific characteristics, threshold of the virtual environment hosting the virtual collaboration, etc. The degree of shareability is utilized as a component of one or more policies for the virtual collaboration generated by privacy module370, in which the degree of shareability may be one of the factors that directly impacts the accessing rights and enabled functionalities of user270. For example, if the contextual information results in the degree of shareability of a virtual collaboration being high, then user270may be enabled to perform screen sharing, commenting, polling, and the like within the virtual collaboration subject to classification provided in the policies by privacy module370. The assigned degrees of shareability associated with virtual collaborations are configured to be stored, managed, and updated accordingly in collaboration allocation module database250. Collaboration assessment module350is further configured to communicate with social network module380in order to classify a group of users associated with the virtual environment and/or virtual collaboration based on assessment of the social network connections of user270a particular social network. Classification of the users may not only assist with determining user affiliations, but also with determining the degree of shareability of a virtual environment and/or virtual collaborations. For example, social network module380accessing a social connections model allows collaboration assessment module350to ascertain that user270is not affiliated with a group of users on a social network resulting in the degree of shareability associated with the applicable virtual collaboration to be reduced (e.g., the group of users may not be able to view contributions of user270to the virtual collaboration).

Machine learning module360is configured to use one or more heuristics and/or machine learning models for performing one or more of the various aspects as described herein (including, in various embodiments, the natural language processing or image analysis discussed herein). In some embodiments, the machine learning models may be implemented using a wide variety of methods or combinations of methods, such as supervised learning, unsupervised learning, temporal difference learning, reinforcement learning and so forth. Some non-limiting examples of supervised learning which may be used with the present technology include AODE (averaged one-dependence estimators), artificial neural network, back propagation, Bayesian statistics, naive bays classifier, Bayesian network, Bayesian knowledge base, case-based reasoning, decision trees, inductive logic programming, Gaussian process regression, gene expression programming, group method of data handling (GMDH), learning automata, learning vector quantization, minimum message length (decision trees, decision graphs, etc.), lazy learning, instance-based learning, nearest neighbor algorithm, analogical modeling, probably approximately correct (PAC) learning, ripple down rules, a knowledge acquisition methodology, symbolic machine learning algorithms, sub symbolic machine learning algorithms, support vector machines, random forests, ensembles of classifiers, bootstrap aggregating (bagging), boosting (meta-algorithm), ordinal classification, regression analysis, information fuzzy networks (IFN), statistical classification, linear classifiers, fisher's linear discriminant, logistic regression, perceptron, support vector machines, quadratic classifiers, k-nearest neighbor, hidden Markov models and boosting, and any other applicable machine learning algorithms known to those of ordinary skill in the art. Some non-limiting examples of unsupervised learning which may be used with the present technology include artificial neural network, data clustering, expectation-maximization, self-organizing map, radial basis function network, vector quantization, generative topographic map, information bottleneck method, IBSEAD (distributed autonomous entity systems based interaction), association rule learning, apriori algorithm, eclat algorithm, FP-growth algorithm, hierarchical clustering, single-linkage clustering, conceptual clustering, partitional clustering, k-means algorithm, fuzzy clustering, and reinforcement learning. Some non-limiting examples of temporal difference learning may include Q-learning and learning automata. Specific details regarding any of the examples of supervised, unsupervised, temporal difference or other machine learning described in this paragraph are known and are considered to be within the scope of this disclosure. In particular, machine learning module370is configured to operate and maintain one or more machine learning models configured to utilize training datasets derived from database215, virtual environment analyzer module database230, collaboration allocation module database250, and any other applicable data source, in which the one or more machine learning models are configured to generate outputs representing predictions of virtual environment contextual information, virtual collaboration accessing rights, social network used to host the virtual collaboration, social network based grouping of users for virtual collaborations, and the like.

Privacy module370is tasked with generating one or more policies associated with the virtual collaborations. It should be noted that the policies pertain to the role assigned to user270, the level of access of user270to the virtual collaboration, the functionalities enabled for user270within the virtual collaboration, topic/subject matter classified as sensitive within applicable dialogue of the virtual collaboration, etc. One primary purpose of the policies is to allocate the role associated with the virtual collaboration to user270, in which the role assignment may be based on one or more of the degree of shareability of the virtual environment and/or virtual collaboration, the contextual information, analyses of the user profiles, virtual collaboration-based functionalities available (e.g., screen-sharing, live commenting, etc.), and the like. In some embodiments, policies are managed on a blockchain or distributed ledger, in which privacy module370generates code interpretations and/or derivatives of the logic that determine which components of the virtual collaboration are protected and which are subject to view by user270or any other applicable user associated with the virtual collaboration. The blockchain further prevents undesired amendments to the policies allowing updates to the policies to be established by a trusted entity.

Social network module380is designed to manage and operate the social media content and social connections components associated with virtual collaborations. Connections established between server210and social network module380allow the centralized platform to function as a mechanism of user270to assign roles, permission levels, and functionalities to users associated with a virtual collaboration in the instance in which user270is the admin of a virtual collaboration. User270may also decide which social network platform they wish to share the virtual collaboration on via accessing the centralized platform, in which application programming interfaces (APIs) may function as plugins associated with the respective social network for the purpose of allowing social network module380to evaluate social connections, social media activity, and other applicable social network-based data associated with user270. Social network module380also assists collaboration assessment module350with maintaining the number of users from a particular social network that may engage with the virtual collaboration in which the user threshold is assessed based on a level of user engagement/participation from the users of the social network. In some embodiments, privacy module370may factor this level of user engagement in the process of updating the policies resulting in the access rights being modified in real-time based on the contextual information associated with the virtual collaboration. Social network module380further assists with classifying groups of users associated with the virtual collaboration based on one or more of analyses of user profiles, skills of users, likes/preferences, demographics, and the like.

Referring now toFIG.4, a virtual environment400associated with a social network is depicted, according to an exemplary embodiment. Virtual environment400hosts a virtual collaboration410in which a plurality of avatars may participate in virtual collaboration410by engaging with social media content420, in which social media content420may include various types of multi-media in addition to an interactive hub. In some embodiments, the centralized platform provides the interactive hub in virtual environment400and visualization module330renders the interactive hub to display one or more of a total number of participants within the virtual environment hosted on the social media network, a role of each of the participants, and an option for an administrator to control the permission level allocated to each of the participants or user groups overall. For example, the interactive hub allows the avatars to view who has entered into the virtual collaboration and what their associated user's respective role is in virtual collaboration410. The avatar with applicable access rights and role may control the user access rights of other users in virtual collaboration410directly by interacting with the interactive hub in virtual environment400. Visualization of avatars may be privatized and/or reduced based on the role of the user associated with the respective avatar. For example, during a virtual collaboration only certain avatars may be visible to others within virtual environment400based on the respective users being connected on one or more social network platforms. In addition, in the instance virtual collaboration is a public livestream session, user270may join the virtual collaboration and designate whether to be a participant or viewer via the centralized platform.

With the foregoing overview of the example architecture, it may be helpful now to consider a high-level discussion of an example process.FIG.5depicts a flowchart illustrating a computer-implemented process500for a method for sharing virtual environment collaboration, consistent with an illustrative embodiment. Process500is illustrated as a collection of blocks, in a logical flowchart, which represents a sequence of operations that can be implemented in hardware, software, or a combination thereof. In the context of software, the blocks represent computer-executable instructions that, when executed by one or more processors, perform the recited operations. Generally, computer-executable instructions may include routines, programs, objects, components, data structures, and the like that perform functions or implement abstract data types. In each process, the order in which the operations are described is not intended to be construed as a limitation, and any number of the described blocks can be combined in any order and/or performed in parallel to implement the process.

At step510of500, virtual environment analyzer module220analyzes the applicable virtual environment. It should be noted that virtual environments, such as, but not limited to, metaverses, may require image/video analysis, parsing, tokenizing, 3D point cloud segmentation, or any other applicable VR/AR-based analysis mechanism known to those of ordinary skill in the art. Detected virtual collaborations within virtual environments, avatars, virtual objects/features, virtual environment layouts, and the like may support the analysis of the virtual environment performed by virtual environment analyzer module220. In some embodiments, data associated with user270may be ascertained by computing device260via user interactions with the virtual environment and/or virtual collaborations. For example, analysis of targets associated with user gaze detection, topics of virtual collaborations highly engaged by user270, frequently interacted with virtual objects, visual preferences, and the like are within the scope of this disclosure. Reinforcement learning may also be applied during the virtual environment analysis phase in order to optimize and fine-tune analyses based on previous analyses.

At step520of500, contextual module310determines a plurality of contextual information of the virtual environment. Contextual module310determines the contextual information via utilizing various mechanisms such as, but not limited to, cognitive/analytic systems, NLP/linguistics analysis (e.g., word2vec, doc2vec, etc.), LDA, semantic analyzer, parsing functions, multi-media detection/analyses systems, computer visioning systems, applicable machine learning models operated by Machine learning module360, etc. As previously mentioned, contextual information may include, but is not limited to, user location, topic/subject matter, date/time, environment/virtual object theme/setting, a participant threshold of user270associated with the virtual collaboration presented by the applicable social media network, participant dialogue concept, an event within the virtual environment (e.g., E-sport, dining experience, shopping experience, etc.), occurrences of a predetermined pattern of content or activity within the event, or any other applicable contextual-based data known to those of ordinary skill in the art.

At step530of500, collaboration assessment module350classifies a group of users associated with the virtual environment and/or virtual collaboration. In some embodiments, the classification of users in the group is based on one or more of contextual information, social connections among users in the group associated with social network platforms, assigned degree of shareability associated with one or more virtual collaborations (including segments of virtual collaborations) of the virtual environment, and the like. One important purpose of classifying the users of a group is for applicable permission levels to be assigned to users accordingly. As a result, certain users may have access to a segment of a virtual collaboration that other users of the group do not because of various factors such as the social network connection, current subject of the virtual collaboration, contextual information, and the like.

At step540of500, virtual environment analyzer module220performs segmenting of a detect virtual collaboration associated with the virtual environment. One important purpose of segmenting virtual collaborations is to allow partitioning of virtual collaborations so that a degree of shareability can be assigned to the resulting segments of the virtual collaborations. As a result, user270may have access to a segment of a virtual collaboration that other users do not; thus, providing an enhanced layer of privacy to not only the virtual environment, but also each virtual collaboration occurring within the virtual environment.

At step550of500, privacy module370assigns a permission level to each user of the group. In some embodiments, the assignment of a permission level to a user is based upon one or more of the classification of the user group, contextual information, the media content shared within the virtual collaboration, social network connections among users of the group, degree of shareability assigned to the virtual environment and/or virtual environment, etc. Permission levels may further indicate the functionalities available to users within the virtual collaboration, in which users may have a role which may be an admin, a viewer, or a participant. For example, a viewer may only have permission to view a virtual collaboration and may not have functionalities to record virtual collaborations, vote, speak, contribute media content, screen-share, and the like. However, permission level for a user may be modified based upon contextual information or a detected change in the dynamic of the virtual collaboration or users within the virtual environment.

At step560of500, privacy module370assigns a degree of shareability to each segment of the virtual collaboration. Assignment of the degree of shareability may be based on the contextual information, analyses of the user profiles, current topic being discussed in the segment, presenter of content in the segment (e.g., sensitive party requiring privileged access right to participants/viewers), modifications to users in the associated virtual environment, and the like. Due to the inherent fluctuation of users in virtual environments associated with social network platforms it is imperative for the degree of shareability for virtual environments and virtual collaborations to be able to be modified in real-time. In some embodiments, user270may determine the degree of shareability by accessing the centralized platform via computing device260.

At step570of500, social network module380presents the virtual collaboration based on the assignments of privacy module370. In some embodiments, privacy module370determines who to present the virtual collaboration to based on the analyses of the user profiles associated with users participating in the virtual collaboration. It should be noted that both the assignment of the permission levels and the degree of shareability dictates not only who has access to virtual environments and/or virtual collaborations, but also what functionalities users have within the aforementioned. In some embodiments, user270may select the assigned roles to users in a virtual collaboration via the centralized platform, in which this feature may be visualized to user270within the virtual environment allowing the associated avatar to choose roles for users in the virtual collaboration in a privatized manner. In the instance in which user270is the administrator, they may also perform re-grouping of the users and/or assign group specific permission levels in order to ease the process of assigning access rights to virtual environments and/or virtual collaborations.

Based on the foregoing, a method, system, and computer program product have been disclosed. However, numerous modifications and substitutions can be made without deviating from the scope of the present invention. Therefore, the present invention has been disclosed by way of example and not limitation.

It will be appreciated that, although specific embodiments have been described herein for purposes of illustration, various modifications may be made without departing from the spirit and scope of the embodiments. In particular, transfer learning operations may be carried out by different computing platforms or across multiple devices. Furthermore, the data storage and/or corpus may be localized, remote, or spread across multiple systems. Accordingly, the scope of protection of the embodiments is limited only by the following claims and their equivalent.