WO2016090326A1 - Intent based digital collaboration platform architecture and design - Google Patents

Intent based digital collaboration platform architecture and design Download PDF

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Publication number
WO2016090326A1
WO2016090326A1 PCT/US2015/064118 US2015064118W WO2016090326A1 WO 2016090326 A1 WO2016090326 A1 WO 2016090326A1 US 2015064118 W US2015064118 W US 2015064118W WO 2016090326 A1 WO2016090326 A1 WO 2016090326A1
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WIPO (PCT)
Prior art keywords
platform
plurality
user
contents
users
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PCT/US2015/064118
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French (fr)
Inventor
Ramona PIERSON
Aniruddha Chaudhuri
Pankaj Anand
Jay PAINTER
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Declara, Inc.
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Priority to US201462088514P priority Critical
Priority to US62/088,514 priority
Priority to US62/111,056 priority
Priority to US201562111056P priority
Priority to US201562111063P priority
Priority to US62/111,063 priority
Priority to US62/111,626 priority
Priority to US201562111623P priority
Priority to US201562111626P priority
Priority to US62/111,623 priority
Priority to US62/112,144 priority
Priority to US201562112144P priority
Priority to US201562112139P priority
Priority to US62/112,139 priority
Priority to US62/112,633 priority
Priority to US201562112631P priority
Priority to US201562112633P priority
Priority to US62/112,631 priority
Priority to US62/113,319 priority
Priority to US201562113319P priority
Priority to US201562113323P priority
Priority to US62/113,323 priority
Application filed by Declara, Inc. filed Critical Declara, Inc.
Publication of WO2016090326A1 publication Critical patent/WO2016090326A1/en

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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/01Social networking
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/10Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting

Abstract

A digital collaboration platform comprising one or more servers and one or more remote devices facilitates learning in enterprises. The platform comprises permeable membrane logic, content learning logic, artificial intelligence logic, mobile communication logic, and social networking logic. The platform increases fluidity in collaborative learning.

Description

INTENT BASED DIGITAL COLLABORATION PLATFORM

ARCHITECTURE AND DESIGN

CROSS REFERENCE

[0001] The present PCT patent application claims priority to the following U.S. Provisional patent applications: 62/088,514, filed 05-Dec-2014, entitled "INTENT BASED SOCIAL LEARNING PLATFORM ARCHITECTURE AND DESIGN", attorney docket no. 46429- 701/101; 62/111,063, filed 02-Feb-2015, entitled "INTENT BASED DIGITAL

COLLABORATION PLATFORM ARCHITECTURE AND DESIGN", attorney docket no. 46429-701/102; 62/111,626, filed 03-Feb-2015, entitled "INTENT BASED DIGITAL COLLABORATION PLATFORM ARCHITECTURE AND DESIGN", attorney docket no. 46429-701/103; 62/112,144, filed 04-Feb-2015, entitled "INTENT BASED DIGITAL COLLABORATION PLATFORM ARCHITECTURE AND DESIGN", attorney docket no. 46429-701/104; 62/112,631, filed 05-Feb-2015, entitled, "INTENT BASED DIGITAL COLLABORATION PLATFORM ARCHITECTURE AND DESIGN", attorney docket no. 46429-701/105; 62/113,319, filed 06-Feb-2015, entitled "INTENT BASED DIGITAL COLLABORATION PLATFORM ARCHITECTURE AND DESIGN", attorney docket no. 46429-701/106; 62/111,056, filed 02-Feb-2015, entitled "INSIGHTS FOR TEXT

METHODS AND APPARATUS", attorney docket no. 46429-702/101; 62/111,623, filed 03- Feb-2015, entitled "INSIGHTS FOR EFFECTIVE COMPUTER BASED

COLLABORATION", attorney docket no. 46429-702/102; 62/112,139, filed 04-Feb-2015, entitled "INSIGHTS FOR EFFECTIVE COMPUTER BASED COLLABORATION", attorney docket no. 46429-702/103; 62/112,633, filed 05-Feb-2015, entitled "INSIGHTS FOR EFFECTIVE COMPUTER BASED COLLABORATION", attorney docket no. 46429- 702/104; and 62/113,323, filed 06-Feb-2015, entitled "INSIGHTS FOR EFFECTIVE COMPUTER BASED COLLABORATION", attorney docket no. 46429-702/105; the entire disclosures of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION

[0002] People need to communicate with each other. In prehistoric times, oral

communication was used to transmit information. Oral communication required that people be near each other, and the rate, accuracy and distance at which information could be transmitted was severely limited. As written languages evolved, information could be transmitted by passing written messages such as letters, but the extent to which information could be disseminated was limited. The advent of the printing press allowed information to be disseminated widely with books and other forms of printed paper, although two-way communication was limited and the printed object had to be moved to disseminate information. The inventions of the telegram, telephone and radio allowed communication to occur rapidly between locations. With the advent of modern computers, routers, and networks such as the Internet, millions of people can communicate and collaborate with each other. Computers have become ubiquitous and can be found in cell phones such as iPhones™ and Android™ phones, readers such as the Kindle™, notepads such as the iPad™, tablet computers, laptop computers, notebook computers, desktop computers, backend servers, cloud based computers and data storage computers. These modern computer devices are able to communicate with each other with many forms of communication such as wireless and wired communication, the Internet, networks and virtual private networks. Modern people use computer devices to communicate with each other, store information, and disseminate information.

[0003] Although modern computers and communication have significantly advanced knowledge and rate with which people can interact, further improvement is needed. For example, people can be working on computers and networks to solve similar problems and not be aware of each other, such that efforts and time spent working on the computer system can be wastefully duplicated. Also, prior methods and apparatus for displaying information on computers can provide undesirable information and the arrangement of the information provided on computer displays can be less than ideal, in at least some instances. For example, prior top down approaches to the display of information offer a preconfigured arrangement of information on a computer display, such as a template, which can be less than ideally suited to provide differing types of information.

[0004] Although Internet is used by over a billion people with computer devices, prior methods and apparatus of using the Internet can be less than ideal in at least some instances. For example, computer searches can be less specific than would be ideal. Also, when a computer user searches for a topic with a prior search engine, content such as an article may be presented in a less than ideal manner on the display. For example a user may need to sift through an entire article to determine what is relevant. Although the hits on some search terms may appear in a browser window, the presentation of the information in the browser window on the computer display can be less than ideal. Although Internet users can sift through millions of articles every year, quality indicators at the sub-article level are less specific and less granular than would be ideal. Although prior users can 'Like', share or comment on articles, such identification of articles can be less specific than would be ideal, and it can be difficult to determine what the user liked about an article. Also, prior methods and apparatus may provide less than ideal determination of content that excites many users and may provide less than ideal metrics for suggesting content such as text to computer users.

[0005] The Internet and computers devices allow people to connect in ways that were not possible prior to the advent of computers and networks. For example, crowdsourcing allows many people who are remote from each other to collaborate on projects. Although prior crowdsourcing computers and user interfaces can be used to enlist the services of many people on a project, the prior crowdsourcing methods and apparatus can be less than ideally suited for determining the actions and preferences of individual users. Further, the manner in which prior computers provide shared data to users can be less than ideal.

[0006] New paradigms of methods and apparatus are necessary to provide improved determination of the information sought by a user, improved identification of users to be connected with each other, improved integration of public and private information, and improved display of information based on the type of information being sought. What is needed are improved computer methods and apparatus of allowing people to communicate with improved specificity and metrics that allow improved identification subject matter for users. Also, it would be helpful to provide improved methods and apparatus to determining contributions of people on computer networks and tracing influence of one or more people with improved specificity. It would also be beneficial to provide improved methods and apparatus of profiling people using computers with improved specificity in order to provide improved searching and presentation of data.

SUMMARY OF THE INVENTION

[0007] Although examples and embodiments as described herein are directed to a social learning platform, the technologies disclosed herein will have application in many fields, such as social networking, search engines and displays.

[0008] Methods and apparatus as described herein provide improved acknowledgement and identification of content such as text. The methods and apparatus can provide a broad system of acknowledgement for many users to identify content such as text as relevant. Instructions of a processor allow users to highlight specific sections of content identified as insightful in order to crowdsource specific quality indicators from users. The methods and apparatus can validate the quality of content, and the associated information can be used in one or more of many innovative ways. A backend server can be configured to couple to the user interfaces of many users and to track and receive acknowledgements of many users. The combined acknowledgements provided by the users can validate the acknowledged subject matter as relevant and insightful. The user interface can be configured for users to identify and acknowledge specific portions of a document, and the back end server can be configured to determine overlapping words and regions acknowledge by many users in order to validate the content and to provide improved specificity and granularity to the identified subject matter. The improved specificity and granularity can provide improved validation of content such as text and improved tracing of an individual user's contribution.

[0009] The acknowledgement of many users of common granular portions of documents can validate the common portions of the document. The validated portions of the document can be recommended to other users, in order to provide improved accuracy of recommendations to other users. The acknowledgements provided by an individual user can be used to profile the individual user and to provide information about the individual user to the backend server comprising a tangible medium embodying processor instructions of an algorithm. The acknowledgements of the article provided to the back end server and profile of the individual user can be used to improve recommendations to the user, and to place the user in contact with other users with similar interests. The improved recommendations provided to many users with improved specificity and granularity can be acknowledged by the many users can increase the rate of validation and adoption by users.

[0010] In an aspect, disclosed herein is a digital collaboration (or social learning) platform comprising: a server including a server processor and a server operating system configured to provide an enterprise with a server application. The server application comprises an artificial intelligence logic (including cognitive graph logic), a permeable membrane logic, a content learning logic, and/or a mobile communication logic. The permeable membrane logic is configured to create a digital space, wherein the digital space comprises (i) user profiles of a plurality of users and (ii) a plurality of contents. The content learning logic is configured to: use the artificial intelligence logic to collect the plurality of contents; allow the plurality of users to provide insights on one or more accessed contents; record learning journals of the plurality of users after accessing the plurality of contents; use the artificial intelligence logic to (i) analyze access behaviors, the insights, the learning journals, and the user profiles and (ii) recommend one or more unaccessed contents to the plurality of users. The mobile communication logic is configured to communicate with a mobile device, which includes a mobile processor configured to provide a mobile user with a mobile application. The mobile application comprises a mobile access logic configured to access the plurality of contents. [0011] In the platform, the artificial intelligence logic may be configured to realize one or more artificial intelligence algorithms. The artificial intelligence algorithms may comprise graphical model and statistical inference.

[0012] The digital space configured in the platform may be created within the enterprise, or across the enterprise and a public domain. The permeable membrane logic may be further configured to configure content accessibility of the digital space. Furthermore, the permeable membrane logic may be further configured to configure security of the digital space. A user profile of a user may comprise one or more of the following: one or more knowledge domains, one or more expertise skills, one or more preferences, one or more likes, and one or more dislikes. The plurality of contents may comprise one or more of the following: one or more images, one or more video files, one or more audio files, one or more articles, one or more spreadsheets, and one or more RSS feeds.

[0013] The content learning logic may collect the plurality of contents from various sources. Non-limiting examples include a database inside the enterprise, an internal expert, an external expert, a public domain on the Internet. Once a content is collected, the content logic may tag a section of the content. Access to the plurality of contents is configured by the content learning logic, wherein the access comprises one or more of the accessing the plurality of contents comprises one or more of: viewing. Upon accessing the contents, the users may provide insight on the contents. The ways of providing insight include labeling one or more components of the plurality of contents. Labeling may comprise highlighting, marking, drawing, writing, taking notes, summarizing, and recording. In some scenarios, the labeling is based on texts or speaking. On the other hand, the one or more components of the plurality of contents may contain one or more of the following: one or more keywords, one or more tables, one or more audio segments, and one or more video segments. After the users access the contents, the content learning logic may assist the users to create personal libraries. In further examples, the content learning logic is configured to use the artificial intelligence logic to create a trending, which is associated with one or more of the following: a social event, knowledge, a skill, a user profile, an access behavior, an interest, a preference, and a like. Recording learning journals may be one of the functions of the content learning logic. The learning journal may comprise one or more records of learning the plurality of the contents, which may include discussing one or more contents by a first user with a second user, asking one or more questions, answering one or more questions, providing one or more comments, recommending one or more contents, contributing one or more contents to the plurality of contents, and influencing one or more other users. Furthermore, the content learning logic may associate between the user profiles and the one or more records of learning the plurality of the contents.

[0014] The server application of the platform may comprise a social networking logic. The social networking logic is configured to analyze access behaviors, the insights, the learning journals, and the user profiles. The social networking logic can also be configured to use the analysis to create social networks for the plurality of users.

[0015] The mobile communication logic of the platform can be configured to allow the plurality of users to access the plurality of contents on mobile devices. Furthermore, the mobile communication logic may send push notifications of recommended contents to the plurality of users on mobile devices.

[0016] The mobile device includes the mobile access logic to provide on-the-go access to the digital space. The mobile access logic allows the mobile user to receive one or more push notifications from the server application.

[0017] According to an aspect, there is provided a digital platform to provide

recommendations for content. The platform comprises a server, and the server comprises a server processor and memory. The memory comprises instructions executable by the processor, and when executed the instructions cause the platform perform a sequence of steps: The platform creates a digital space comprising user profiles for each of a plurality of users and a plurality of contents. The platform collects the plurality of contents. The platform allows the plurality of users to provide insights on one or more accessed contents, and the platform records learning journals of the plurality of users reflecting access of the plurality of contents by the plurality of users. The platform further performs a sequence of steps to generate recommendations of content: The platform generates a graphical model with a plurality of content nodes, a plurality of user nodes, and a plurality of concept nodes. The platform assigns each of the plurality of users to a corresponding user node in the plurality of user nodes and each of the plurality of contents to a corresponding content node in plurality of content nodes. The platform associates the plurality of user nodes with concept nodes by analyzing access behaviors, the insights, the learning journals, and the user profiles. The platform associates the plurality of content nodes with concept nodes in response to user access of the contents, user insights provided for the contents, and one or more components of the contents. The platform compares the plurality of contents to the plurality of users. And the platform recommends one or more unaccessed contents of the plurality of contents to the plurality of users in response to said comparing. [0018] In some examples, the step of comparing the plurality of contents to the plurality of users comprises determining affinities between the plurality of contents and the plurality of users.

[0019] In some instances, the affinities are determined by comparing one or more concept nodes associated with the plurality of users with one or more concept nodes associated with the contents.

[0020] In some instances, the step of the recommending one or more unaccessed contents comprises comparing the affinities of a plurality of unaccessed contents to each user in the plurality of users, and recommending to each user in the plurality of users an unaccessed content with highest affinity to that user.

[0021] In some instances, the association of the one or more components with the one or more concept nodes is determined by a comparison of properties of the component with properties of other components associated with the one or more concept nodes.

[0022] In some instances, the step of associating the plurality of contents with concept nodes comprises associating a concept node with each of a video content node, an audio content node, and a text content node.

[0023] In some instances, the step of associating the plurality of contents with concept nodes further comprises assigning to a concept node a content node, wherein the content node is assigned to a content that has not yet been accessed by users and has not yet had insights provided for it.

[0024] In some instances, the association of the content to the concept node is made by matching a classification of one or more components of the content with a classification associated with the concept node.

[0025] In some instances, the one or more components of the contents comprise one or more of a keyword, a sentence, a table, an audio segment, or a video segment.

[0026] In some instances, the platform is configured to recommend one or more contents previously accessed by at least one user.

[0027] In some instances, the unaccessed contents have not been accessed by any user of the plurality of users.

[0028] According to a further aspect of the invention, there is provided a digital platform to provide recommendations for content. The platform comprises a server, and the server comprises a server processor and memory. The memory comprises instructions executable by the processor, and when executed the instructions cause the platform perform a sequence of steps: The platform creates a digital space comprising user profiles for each of a plurality of users and a plurality of contents. The platform divides the digital space into a private digital space and a public digital space. The platform associates each of the plurality of contents with one or more of the private digital space or the public digital space. The platform determines access rights for the plurality of users with regard to the private digital space. And the platform control user access of contents associated with the private digital space in response to the access rights. The platform further collects the plurality of contents. The platform allows the plurality of users to provide insights on one or more accessed contents, and the platform records learning journals of the plurality of users reflecting access of the plurality of contents by the plurality of users. The platform further performs a sequence of steps to generate recommendations of content: The platform generates a graphical model with a plurality of content nodes, a plurality of user nodes, and a plurality of concept nodes. The platform assigns each of the plurality of users to a corresponding user node in the plurality of user nodes and each of the plurality of contents to a corresponding content node in plurality of content nodes. The platform associates the plurality of user nodes with concept nodes by analyzing access behaviors, the insights, the learning journals, and the user profiles. The platform associates the plurality of content nodes with concept nodes in response to user access of the contents, user insights provided for the contents, and one or more components of the contents. The platform compares the plurality of contents to the plurality of users. And the platform recommends one or more contents of the plurality of contents to the plurality of users in response to said comparing.

[0029] In some instances, the access behaviors comprise access behaviors in the public digital space as well as access behaviors in the private digital space.

[0030] In some instances, the recommendation of one or more contents comprises recommending one or more contents in the public space in response to access behaviors and insights in the private digital space.

[0031] In some instances, the access rights allow all users to access the public digital space, but only allow a portion of the users to access the private digital space.

[0032] In some instances, the private digital space comprises a first private digital space and a second private digital space, and wherein the access rights for the plurality of users selectively allow users to access the public digital space as well as one of: the first private digital space, the second private digital space, both private digital spaces, or neither private digital space. [0033] In some instances, the recommendation of one or more contents comprises recommending one or more contents in the second private space in response to access behaviors and insights in the first private digital space.

[0034] In some instances, the one or more components of the contents comprise one or more of a keyword, a sentence, a table, an audio segment, or a video segment.

[0035] In some instances, the platform is further configured to create a trending.

[0036] In some instances, the step of providing insight comprises labeling a component of the plurality of contents. In some cases, the labeling can comprise highlighting, marking, drawing, writing, taking notes, summarizing, and recording.

[0037] In some instances, the user profiles comprise one or more of the following: one or more knowledge domains, one or more expertise skills, one or more preferences, one or more likes, and one or more dislikes.

[0038] In some instances, the memory further comprises instructions to cause the platform to update the user profiles in response to an association of corresponding user nodes to one or more concept nodes.

[0039] In some instances, the associations of content nodes and user nodes with concept nodes comprise magnitudes of association.

[0040] In some instances, the memory further comprises instructions to cause the platform to update the magnitudes of association between concept nodes and user nodes and content nodes by determining that a user assigned to a user node has accessed or provided an insight for content assigned to a content node; increasing an association between the user node and one or more concept nodes associated with the content node in response to the magnitudes of association between the content node and the one or more concept nodes associated with the content node; and increasing an association between the content node and one or more concept nodes associated with the user node in response to the magnitudes of association between the user node and the one or more concept nodes associated with the user node.

[0041] In some instances, the amount of increase provided to each magnitude of association is greater if the platform determines that the user provided an insight for the content than if the platform determines that the user accessed the content.

[0042] In some instances, the memory further comprises instructions to cause the platform to determine that a user has not accessed a content associated with a content node that was recommended to the user within a predetermined period of time, and decrease the magnitudes of association between a user node associated with the user and one or more concept nodes associated with the content node in response to the determination that the user has not accessed the content. In some cases, the platform comprises further instructions to decrease the magnitudes of association between the content node and one or more concept nodes associated with the user node in response to the determination that the user has not accessed the content.

[0043] In some instances, the memory further comprises instructions executable by the processor to cause the platform to provide a summary of a content: The platform identifies one or more insights provided for the content by one or more users, as well as identifying portions of the content associated with the insights. The platform generates a summary of the content from the identified portions, and provides the summary to a user that has not yet accessed the content.

[0044] In some instances, the platform is further configured to prevent users from providing an insight for a content that overlaps with a previously provided insight.

[0045] In some instances, the platform is further configured to allow users to vote up or down the previously provided insight. In some cases, the platform is configured to identify the user that originally provided the previously provided insight and display a net voting score for the insight. The platform can be further configured to determine expertise of users in response to net voting scores for insights originally provided by the users. The platform can be further configured to determine the expertise of users for each of a plurality of concept nodes associated with the user nodes of the users. In some cases, the expertise of the user for a concept node is determined in response to a combination of the voting score of insights provided by the user for at least one content and a magnitude of association between the at least one content and the concept node.

[0046] In some instances, the platform further comprises a mobile communication logic configured to communicate with a mobile device. The mobile device can include a mobile processor configured to provide a mobile user with a mobile application, the mobile application comprising a mobile access logic configured to access the plurality of contents.

[0047] In some instances, a processor coupled to a display comprises instructions to display one or more insights identified by other users to a user and for the user to transmit an acknowledgement of the one or more insights identified by the other users.

[0048] In another aspect, a method provides recommendations for content. A digital space is created comprising user profiles for each of a plurality of users and a plurality of contents. The plurality of contents is collected. The plurality of users is allowed to provide insights on one or more accessed contents. Learning journals of the plurality of users are recorded reflecting access of the plurality of contents by the plurality of users. Recommendations of content are generated by:

[0049] generating a graphical model with a plurality of content nodes, a plurality of user nodes, and a plurality of concept nodes;

[0050] assigning each of the plurality of users to a corresponding user node in the plurality of user nodes and each of the plurality of contents to a corresponding content node in plurality of content nodes;

[0051] associating the plurality of user nodes with concept nodes by analyzing access behaviors, the insights, the learning journals, and the user profiles;

[0052] associating the plurality of content nodes with concept nodes in response to user access of the contents, user insights provided for the contents, and one or more components of the contents;

[0053] comparing the plurality of contents to the plurality of users; and

[0054] recommending one or more unaccessed contents of the plurality of contents to the plurality of users in response to said comparing.

[0055] In another aspect, a method provides recommendations for content. A digital space is created comprising user profiles for each of a plurality of users and a plurality of contents. The digital space is dived into a private digital space and a public digital space. Each of the plurality of contents is associated with one or more of the private digital space or the public digital space. Access rights are determined for the plurality of users with regard to the private digital space. User access is controlled for contents associated with the private digital space in response to the access rights. The plurality of contents is collected. The plurality of users is allowed to provide insights on one or more accessed contents. Learning journals of the plurality of users are recorded reflecting access of the plurality of contents by the plurality of users. Recommendations of content are generated by:

[0056] generating a graphical model with a plurality of content nodes, a plurality of user nodes, and a plurality of concept nodes;

[0057] assigning each of the plurality of users to a corresponding user node in the plurality of user nodes and each of the plurality of contents to a corresponding content node in plurality of content nodes;

[0058] associating the plurality of user nodes with concept nodes by analyzing the access behaviors, the insights, the learning journals, and the user profiles; [0059] associating the plurality of content nodes with concept nodes in response to user access of the contents, user insights provided for the contents, and one or more components of the contents;

[0060] comparing the plurality of contents to the plurality of users; and

[0061] recommending one or more contents of the plurality of contents to the plurality of users in response to said comparing.

[0062] Aspects and advantages of the present disclosure will become readily apparent to those skilled in this art from the following detailed description, wherein only illustrative instances of the present disclosure are shown and described. As will be realized, the present disclosure is capable of other and different instances, and its several details are capable of modifications in various obvious respects, all without departing from the disclosure.

Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.

INCORPORATION BY REFERENCE

[0063] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative instances, in which the principles of the invention are utilized, and the accompanying drawings (also "FIG." and "FIGs." herein), of which:

[0065] FIG. 1 depicts an overview of components of a social learning platform.

[0066] FIG. 2A depicts example features residing in a social learning platform.

[0067] FIG. 2B depicts data flow with the permeable membrane of the social/collaborative learning platform.

[0068] FIG. 3 depicts a partition of a digital space into individual, community, and content.

[0069] FIG. 4A depicts an example of insight providing user interface.

[0070] FIG. 4B depicts an example of clicking the insight indicator.

[0071] FIG. 4C depicts an example flowchart of providing insights. [0072] FIG. 5 depicts an example of recording learning journal.

[0073] FIG. 6 depicts an example of collaborative learning environment.

[0074] FIG. 7 depicts algorithms to generate a cognitive graph.

[0075] FIG. 8 depicts inputs, layers and intent based outputs to generate a cognitive graph.

[0076] FIG. 9A depicts an example of intent recognition with an algorithm.

[0077] FIG. 9B depicts an example of intent recognition algorithm where actions are dependent.

[0078] FIG. 9C depicts an example of intent recognition flowchart.

[0079] FIG. 10 depicts an example of underlying computer systems realizing a social learning platform.

[0080] FIG. 11 shows an example of a social learning platform with text insight providing.

[0081] FIG. 12A shows an example of video insight editing by marking starting and ending points along the video timeline.

[0082] FIG. 12B shows an example of an insight clip having been created and an interface for the user to annotate the insight clips.

[0083] FIG. 12C shows an example of playing and sharing video insight after the insight is created.

[0084] FIG. 13 shows an example of portable ID and insight content collection.

[0085] FIG. 14 shows an example of insight content complex and aggregation.

[0086] FIG. 15 shows an example of portable ID, permeable membrane, and portable contents.

[0087] FIG. 16 shows an example of cognitive graph.

[0088] FIG. 17 shows an example of enterprise platform and prosumers.

[0089] FIG. 18 shows an example flowchart of permeable membrane.

[0090] FIG. 19 shows an example flowchart of text insights.

[0091] FIG. 20 shows an example flowchart of video insights.

[0092] FIG. 21 shows an example flowchart of cognitive graph.

[0093] FIG. 22 shows an example flowchart of acknowledging receiving contents/insights.

[0094] FIG. 23 shows an example flowchart of collecting contents from social media cues.

[0095] FIG. 24 shows an example flowchart of portable identification.

DETAILED DESCRIPTION OF THE INVENTION

[0096] While various embodiments and examples of the invention have been shown and described herein, it will be obvious to those skilled in the art that such instances are provided by way of example only. Numerous variations, changes, and substitutions may occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments and examples of the invention described herein may be employed. It shall be understood that different aspects of the invention can be appreciated individually, collectively, or in combination with each other.

[0097] As used herein a computer encompasses a device with a processor, which can be coupled to a display.

[0098] Improved methods and apparatus are disclosed for displaying information and putting users seeking similar information in contact with each other. A dynamic spatial temporal user profile can be developed based on a plurality of inputs, such as user input search parameters and spatial temporal information. The user profile may comprise a declared user intent based on user activity. Users having similar profiles with one or similar intents can be placed in contact with each other based on one or more similar intentions. The information shown on a display can be based on the determined intent of the user and the type of information. The information shown on the display to the user can be dynamically adapted based on similar intention with another user, such as when the users are connected.

Platform design

[0099] FIG. 1 and FIG. 2 illustrate the underlying platform design described in this disclosure. Referring to FIG. 1 which shows an overview of components of the system, the platform comprises interfaces based on web application 101 or mobile application 102, and combinations thereof. The web and mobile interfaces access the platform through an API layer 103. The API layer 103 is configured to allow the access to a protected digital space 104. When the platform is installed in an enterprise/institution, the API layer comprises a permeable membrane logic that allows the platform administrator to set up different restriction criteria in order to prevent access by undefined users. Moreover, the API layer comprises a permeable space between the public domains and the protected digital space 104. The protected digital space 104 may comprise one or more of the following:

recommendations to one or more users, a portable identification (ID) of the one or more users, a search engine, profiles of one or more users, configuration settings, and content. The social/collaborative learning platform's APIs connect to mobile and web apps that enable data capturing, content creation/curation, content discovery & exploration, social learning and crowdsourced communication. The platform frequently acquires contents from the Internet, such as data repository. The platform on the one hand analyzes the types of contents should be acquired from the Internet, and other the other hand further classifies the acquired contents into different categories suitable for various types of digital space users. In the cases that the platform is used in an enterprise, the platform users are the enterprise employees comprising a wide range of backgrounds, such as finance, marketing, sale, research, engineering, administrative support, and manufacturing. The platform utilizes the data analytics engine 105 to filter the desired and undesired contents and recommend the contents tailored to different types of employees.

[00100] Once the contents have been acquired from the public domain or created by some experts, the contents are stored in the digital space 104. Along with the contents, the user profiles are also stored in the digital space 104. The digital space may further contain analysis results of contents, e.g., recommendations of suitable contents for individual users.

[00101] The digital space may comprise multiple computer executable

instructions/functions for processing/analyzing the contents and the user profiles. Non- limiting examples include making recommendations, searching, identifying users, classifying contents, and configuring the digital space. These functions may be coupled with data analytics, which is implemented with artificial intelligence engine/logic. The artificial intelligence engine/logic comprises realization of artificial intelligence algorithms. Non- limiting examples of artificial intelligence algorithms include graphical models and statistical inference. The details of the artificial intelligence logic will be described below.

[00102] FIG. 2A illustrates detailed technical features of the platform architecture.

External to the platform is the Internet, including various public domains including public social networks (e.g., Facebook, Twitter, Instagram), video repositories (e.g., YouTube), and news providers (e.g., CNN, BBC, Bloomberg, Reuters).

[00103] The platform architecture includes one or more of the following components: a public social network, a permeable space, a private social network, a search and

recommendation engine, a content creation module, a discovery module, badging framework and achievement, a cognitive graph, and data analytics. The public social network may comprise one or more of many known public social networks, such as an internet forum, Facebook, or Linkedln, for example. The public social network comprises a social network in which the user is allowed to share data with other uses at the discretion of the user. The permeable space may comprise an expert graph and persistent chat. The private social network may comprise a secure network of an institution or company, for example, in which the sharing of data with outside users and organizations is determined by the institution. The content creation module allows the user to create and store content within the private network. The discovery module allows the user to discover information, both public and private, and make and receive recommendations of content. The badging framework and achievement module provides competency and standards.

[00104] The components of the platform architecture can be configured in many ways.

The permeable space and public network can be configured to invite experts outside of private networks to share secure persistent chat conversations and information. The permeable space and private social network can be configured to provide private zones that facilitate personalized learning and peer tutoring. The apps and APIs can be configured to provide predictive analytics, business intelligence and badging. The back end server can be configured to provide data analytics for large amounts of data for many users, for example millions of users.

[00105] The interface between the private digital space and the public domains is a permeable space, which controls the accessibility and security. The permeable space includes expert graph and persistent chat, which allow experts outside of the protected digital space to provide/share contents with the digital space; the contents may comprise chat conversations and useful information.

[00106] The private digital space includes private social networks coupled with a search & recommendation engine. The purpose of the digital space is to facilitate

collaborative learning and peer tutoring among platform users. The individuals in the private social networks can create, curate, publish, and manage contents. The acquired public contents, the knowledge provided by the external experts, and the information curated by the internal users are aggregated together for an artificial intelligence analysis. During the analysis, the search & recommendation engine can discover new findings, perform business intelligence, and recommend learning or events. When users finish learning process, the badging framework grants badges to users for achievement honors.

[00107] The user activities are constantly analyzed by graphical models and data analytics tools, and the analysis results are fed back to the search & recommendation engine. The analysis includes a predictive capability so the platform is able to predict the desired learning materials of the users. The data analytics itself includes next-generation data architecture in order to handle real-time big data crunching.

[00108] The search engine included in the platform may rely on semantic search.

Alternatively, in some cases the search engine does not rely on semantic search at all.

Semantic search typically finds contents (e.g., articles, videos) based on titles or text descriptions, but the meaning of a whole content is not captured. Furthermore, critical insight of the whole content cannot be understood by the semantic search. The technology disclosed herein may utilize insight (to be described in the following sections) provided by the users to perform more effective search.

Permeable membrane logic and digital space

[00109] The methods, systems, media, and platforms disclosed herein may include permeable membrane logic and a digital space. The digital space comprises one or more digital storage media storing a plurality of contents and/or user profiles of a plurality of users. The permeable membrane logic is configured to create the digital space for an enterprise. The digital space may be created within the enterprise, or across the enterprise and a public domain.

[00110] The permeable membrane logic may be further configured to control content accessibility of the digital space. Furthermore, the permeable membrane logic may be further configured to control security of the digital space. The user profile of a user may comprise one or more of the following: one or more knowledge domains, one or more expertise skills, one or more preferences, one or more likes, and one or more dislikes. The plurality of contents may comprise one or more of the following: one or more images, one or more video files, one or more audio files, one or more articles, one or more spreadsheets, and one or more RSS feeds.

[00111] Referring again FIG. 1, the platform comprises an API/exchange layer to selectively pass internal and external information. The selective API/exchange layer comprises a "Permeable Membrane", which controls the social/collaborative learning platform users and especially the enterprise to utilize a private social learning experience in the organization to keep content and data secure. The permeable membrane also can use expertise and contents from outside organization that is found in a "lateral" search across external social networks like Facebook, Linkedln, and Twitter. The goal is that the enterprise client organization or individual have credentials to access a private social learning network can connect and work with the experts/mentors content in a "semi" secured space that allows information to move freely into the private social learning network, but prevents secured content (e.g., intellectual property, confidential documents, business strategies, etc) from leaking from the private network.

[00112] Referring again to FIG. 2A, the social/collaborative learning platform may comprise a "Bridge" that allows the secure communication and sharing of contents across experts and mentors who are outside a particular "private-paid social learning platform network.

[00113] FIG. 2B depicts data flow with the permeable membrane of the social/collaborative learning platform. The arrangement of data can be categorized as generally available public data and enterprise data. The generally available data may comprise private data and public data. The enterprise data may comprise private data and public data. The data can be classified into one of four data spaces comprising: i) public data (Domain 1) that is generally available; ii) private data (Domain 2) that is generally available such as a user's data that can be made public at the discretion of the user; iii) enterprise data (Domain 3) that can be made publicly available for release; and iv) enterprise data (Domain 4) that is secure and not available for release. The dashed line indicates data that can be made available to the public, and data within the dashed line comprises generally available data, such as generally available private user data, publicly available data and permeable space data. The user can be given the option of selecting which private data can be made publicly available. The permeable membrane is shown with arrows showing data going into and out of the permeable data space. The permeable membrane may comprise a permeable layer, or permeable barrier that at least partially defines the permeable space as described herein, and selectively permits data to be released from the permeable space. The permeable membrane can readily admit publicly available data into the permeable data space. The permeable membrane layer can, however, restrict data that is released from a secure network such as an enterprise network. An appropriate person such as a network administrator can change the security settings of the network to adjust the type of data released through the permeable membrane.

[00114] Data in the permeable space (Domain 3) can be abstracted prior to release to the public. The data can be abstracted in many ways to ensure that the data within the permeable space remains secure prior to release. The data in the permeable space can be obtained from one or more of many sources such as number of contacts within an organization, number of interactions with other members within an organization, activities of an individual user such as number of keystrokes typed, number of videos watched per day, and degree of influence within an organization. An organization may selectively decide to release identities of contacts within an organization from the permeable space.

[00115] The data released from through the permeable membrane to the public space can be used outside the enterprise in many ways, and may comprise one or more of the user's influence within the organization, number of contacts within the organization, identity of the contacts within the organization. A user profile can be released from the permeable space comprising one or more of the user' s influence within the organization, number of contacts within the organization, identity of the contacts within the organization. The user profile can be established when the user signs into the enterprise network and can be portable with the user when the user loses access to the secure enterprise network (Domain 4).

[00116] The platform can be configured to allow access to the system with login credentials and identifications from other networks, such as Facebook, Linkedln or other networks. The user can be assigned an internal unique identity associated with one or more other network accounts. For example, the user can be assigned a unique identifier associated with the Facebook and Linkedln accounts. Alternatively or in combination, the user can be provided with a separate ID and login for the secure enterprise network.

[00117] By having the user ID of the enterprise network associated with the other network accounts, the user or the enterprise can publish updates. For example, the user can publish an article from domain 3 to the public and private domains 1 and 2. For example, the user can publish the information to the public domains such as an internet forum such as "stack overflow" and also to a semi-private network that the user can limit viewing of his or her information such as a Facebook or Linkedln account. The enterprise may also have access to the users account information and can publish the information from the permeable domain 3 to the generally available data spaces.

[00118] In a higher application level the digital space can be divided into multiple components. Referring to FIG. 3, non-limiting examples of the components include individual users 301, communities 302, and contents 303. When analyzing the individual users 301 and contents 303 together, the platform can create personalized collections/libraries of contents. The combination of the individual data with analyzing the personalized collections can provide an understanding of the like and dislike of individuals. When contents 303 and communities 302 are considered together, community members are allowed to interact with each other for learning contents. Furthermore, community members may help each other by asking questions and answering questions. Finally, when individuals and community are considered together, the individuals can build up connections to form social networks. The users in a community share similar interests, so their real-time interactions (e.g., messaging) can facilitate communications, which in turn can enhance learning processes.

[00119] The overlap of the data for individual 101, community 302 and content 303 can be used to determine one or more system components. The overlap of the individual 301 with the community 302 can be used to determine connections and messaging. The overlap of the individual 301 with content 303 can be used to provide collections to the individual user. The overlap of the community 302 with content 303 can be used to provide insights and questions and answers (Q&A). The overlap of the individual 301, the community 302 and the content 303 with each other can provide the learning journal.

[00120] When the three components of the digital space are considered together, one can carry out learning journals. Individuals are allowed to access the contents and learn the contents. When the individuals have questions, they are able to ask questions with other community members, or simply said, learning from other community members. On the other hand, community members may become teachers/mentors for another community member. When the content access activities are recorded, the platform creates learning journals. By analyzing the learning journals with artificial intelligence algorithms, the platform

automatically provides recommendations to individuals or communities for advanced learning.

[00121] Photographs, videos and public contents can be uploaded (automatically by the platform or manually by the user) to the user profile. This functionality permits the user to share their contents by configuring privacy settings. Privacy settings permit access to the user only, selected friends, selected groups, all of friends, friends of friends, social learning platform users and social network users.

[00122] The permeable membrane logic is coupled with one or more of the following: the content learning logic, the learning journal, and the artificial intelligence logic. When a content is created, the permeable membrane logic is able to recognize its importance and confidentiality, and then configure the content's permeability (i.e., whether it can be accessed by public, and/or be accessed within the enterprise). For instance, when a document contains an invention, the permeable membrane automatically protects the content. In some cases, the permeable membrane even automatically configures its accessibility for specific personnel within the enterprise. Another example is when a marketing education document (which is irrelevant to confidentiality) is created, the permeable membrane logic automatically configures this document to be accessible by all the employees in the enterprise; in some cases, and the document can be shared in a public domain.

Content learning logic

[00123] The methods, systems, media, and platforms disclosed herein may include a content learning logic. The content learning logic may automatically collect the plurality of contents from various sources. Non-limiting examples of the various sources include databases in the enterprise, internal experts, external experts, public domains on the Internet such as blogs, social networks (e.g., Facebook, Twitter, Instagram), video repositories (e.g., YouTube), and news providers (e.g., CNN, BBC, Bloomberg, Reuters).

[00124] Once a piece of content is collected, the content learning logic may tag a section of the content. Access to the contents stored in the whole digital space may be configured by the permeable membrane logic. However, when the amount of the contents is tremendous, content learning logic facilitates content learning by intelligently recommending users to prioritize the access to the contents. Based on the user profiles, the content learning logic matches the users with their most interested contents. The users can access the contents by viewing, reading, watching, and/or listening.

[00125] During or after accessing the contents, the users may provide insights on the contents. The ways of providing insights include labeling one or more components of the plurality of contents. Labeling may comprise one or more of the following: highlighting, marking, drawing, writing, taking notes, summarizing, recording, ordering, and reordering. In some scenarios, the labeling may be based on texts describing the locations to be labeled, for instance, documenting that a video clip is important from at the first minute. Alternatively the labeling may be based on a recorded voice description on the components. On the other hand, the one or more components of the plurality of contents may contain: keywords, sentences, tables, audio segments, and/or video segments. In addition to labeling, providing insight may include making recommendations. Recommendations may be for an entire content, for a portion of the content, or for the labeled portions of the content.

[00126] FIG. 4A illustrates an example of providing insights. The content in this example comprises texts, indicated by 401. When the user reads the article, the platform allows the user to highlight a portion of the texts, as shown in 402. The highlighted texts are the insights provided by the user. The highlight may indicate a section or a sentence. The highlight is visible to the user and subsequent users accessing the texts. When the highlight/insight is provided, the insight indicator 403 displaying the location of the insight is created and an insight value 404 is assigned; the insight value indicates and may mean a total number of readers who indicate or like it as insight. The highlight/insight can be persistent across the whole platform and can be delivered a part of a collection/library/article, or to be delivered as a standalone content. The insight value can be used to ascertain the quality of the

collection/library/article; alternatively, it may represent an impact on readers. The insight value and the insight may further be used to create better recommendations and provide real feedback to authors, curators, or sharers of the content. [00127] The system may be configured to prevent users from identifying an insight that overlaps with a previously- identified insight. Instead, a user may be allowed to vote on an insight, such as voting up or down the insight. The net sum of votes on an insight may constitute a score for that insight, which may be used to identify the most important insights in a content.

[00128] The content along with the insight value can be delivered in speed reading mode on the web or on mobile devices. The button 405 can allow the user to choose reading the article in a speed reading mode. When reading an article already provided insight by another reader, the current reader may just read the insight areas; or, the current reader may click the button 406 to expand the view to more above/bottom texts or to the whole article. Thus, insights can be used to generate summaries of contents. In some cases, the most popular insights, such as determined by voting scores, may be used to form the summary. In some cases, a summary may be tailored to a particular user; for example, insights may be presented based on a comparison between the user's digital fingerprint in a cognitive graph and the digital fingerprints of other users who created or voted on those insights. Insights may be particularly promoted to a user if the other users voting on that insight show a strong affinity to the user, based on their respective attachments within the cognitive graph. Thus, different summaries may be provided to different users, each being tailored to that user's particular interests. The benefits of the insight providing mechanisms include: validating the quality of the highlighted insights using crowdsourcing; using the insight value to recommend the rest of the article; using a combination of auto-tagging and insight value to match similar articles; using crowdsourcing to break-up articles to deliver to small screen devices; distilling information to save time; quantifying the quality of articles. The same mechanism can be extended to providing insights on audios, videos, tables, and images.

[00129] When a content component is highlighted, next to the component shows an insight indicator. The insight indicator will be shown on the content for the future

viewer/reader/listener. Referring to FIG. 4B, when the insight indicator 403 and insight value 404 are displayed along with an insight (e.g., highlighted texts, images, videos, audios, tables), the indicator is a clickable object on the display. The future viewer can just click the insight indicator 403, and then the indicator becomes another style (e.g., color, brightness, etc), as shown indicator 413. Meanwhile, the insight value changes as well (in the FIG. 4B, the change is increment by 1), as shown insight value 414. Furthermore, a sharing button 415 may be displayed. By pressing the sharing button 415, the insight can be shared with another platform user. The insight indicator and the insight value provide a quick visual communication associated with creation, collection, and use of crowdsourced granular quality indicators in online articles. Internet users sift through thousands of articles every year, and the "Got It" acknowledgement as disclosed herein provides quality indicators at the sub- article level that is granular. The platform disclosed herein creates a broad system of acknowledgement called 'Got it.' It is a method to get users to acknowledge that they "Got it" after reading an article. The platform may be configured to add value for users realized that if users can highlight specific sections of content to be insightful, people would be able to crowdsource specific quality indicators from users. Moreover, the platform would be able to validate quality of content and many use the associated information in many innovative ways. Users can highlight and call out insights in articles. The first user highlights a section. It is visible to the user and subsequent users. Subsequent users can click on the icon next to the highlighted section to increase the impact of the highlight.

[00130] Referring to FIG. 4C, the method of creation, aggregation and propagation of Insights on the platform uses the power of collective readership. Content can be marked as an Insight on any instance of the content on the system. The server captures the event and logs the identity of the creator; the Insight value is then aggregated in a central database. The server then propagates the change in Insight value to all the current instances of the said content. The profile service uses this change in Insight value to readjust other algorithms. In the FIG 4(c), the bottom is a time index showing how the analysis progresses. At lOOOhrs, there is an original article, where a reader marked insight, and the inside value becomes 1 and insight indicator changes color. At 1100 hrs, the article is collected by other readers. At 1200 hrs, the article is copied/distributed to the readers who requested to collect the article. At 1400 hrs, a new reader marks the same insight or an overlapping insight, and the insight value increases to 2; this insight value is further propagated to all the copies of the articles. At 1600 hrs, a third reader marks the same insight, and the insight value increase to 3 while the insight indicator remains the same color. Meanwhile, the insight value propagates to all the copies of the article. As the article is propagated to many users, the insight value is increased with additional insights. Because more than one user can identify and insight and highlight portions in a manner similar to selecting text for copying, users may separately identify insights. The platform may comprise instructions and logic to determine overlapping insights, combine the overlapping insights, and increase the insight value accordingly.

Alternatively, the system may be configured to forbid overlapping insights, and allow users to increase the insight value, such as by voting, instead. [00131] After the users access the contents, the content learning logic may assist the users to create personal libraries. The accessed contents by the users can be marked as, for example, "got it" or "learned" which can represent the contents having been digested by the users, or can indicate the knowledge possessed by the users. The accessed or learned contents can be grouped into a personalized library, where contents may be categorized in various ways, such as content subjects and/or content types.

[00132] In many scenarios, the content learning logic can be configured to conduct semantic search on the contents, which may include photos, documents, videos, metadata, text messages, SMS, emails, and many other forms of contents both user generated and publisher/vendor generated or stored. The social/collaborative learning platform's algorithms may automate the content tagging and ontologies that will allow client organizations to reduce their costs tagging their legacy data. The social/collaborative learning platform's search technologies can discover open source and enterprise specific contents and deliver to users based on the Cognitive Graph (see artificial intelligence logic for more details).

[00133] The content learning logic may use the artificial intelligence logic to analyze the accessed/learned and unaccessed/unlearned contents to estimate a trending. In some cases, the content learning logic further predicts the trending. The trending may represent a hot topic. Alternatively, the trending may associate with users' learning demands (e.g., a skill for enhancing job quality) in the near future. The trending estimation/prediction may be associated with a social event, knowledge, a skill, a user profile, an access behavior, an interest, a preference, and a like. For instance, estimating a trending may be inferred from past social events, knowledge, skills, user profiles, access/learning behaviors, activities, interests, preferences, and/or likes. Alternatively, predicting a trending may use estimated trending to extrapolate future social events, knowledge, skills, user profiles, access/learning behaviors, activities, interests, preferences, and/or likes.

Learning journal

[00134] The methods, systems, media, and platforms disclosed herein may include learning journals, or use of the same. The learning journals comprise records of activities accessing the contents and learning the contents. The content learning logic along with the artificial intelligence logic may be used to record the learning journals, and then exploit the learning journals to facilitate the users' learning. Learning activities may be in various forms:

discussing one or more contents by a first user with a second user, asking one or more questions, answering one or more questions, providing one or more comments, recommending one or more contents, contributing one or more contents to the plurality of contents, and/or influencing one or more other users.

[00135] FIG. 5 illustrates an example of recording learning journals. In FIG. 5, the digital space is installed in an enterprise. When a new user (e.g., a professional) is created in the enterprise, a shadow user identity is created in a user profile within the digital space. All content accessing and learning activities (e.g., viewing, reading, listening, collaborating, commenting) taking place on the user's device are reported to Learning Journal push API.

[00136] Furthermore, learning journals are associated with a Learning Journal User

Interface, which is served directly from the platform users. The users can use this interface to view the learning history and learning activities. Furthermore, the users may receive recommendations to learn advanced topics via the Learning Journal User Interface.

[00137] Once learning journals (which may comprise collaborative learning activities) are recorded, the content learning logic and/or the artificial intelligence logic may analyze the learning journal to track influence of users. Referring to FIG. 6, once the platform collects contents 601 (either acquired from public domains or curated from individuals), the content learning logic 602 couples with the artificial intelligence logic to analyze the contents, user profiles, and learning journals to classify the contents and recommend suitable contents to the users and/or communities. When a user has accessed a content, the way how the users interact with other peers regarding the content is monitored by the platform, as shown in 603. When the user asks questions, the platform can log who has answered the questions, and denote that the answer provider possesses influencing capacity. Moreover, people whose contents have high access rates are likely top influencers. The influencers in some instances mean they are the source of domain knowledge, and are important assets of the enterprise. The platform can assist the management team of the enterprise to exploit employees' knowledge in efficient and effective ways.

Artificial intelligence logic

[00138] The methods, systems, media, and platforms disclosed herein may include an artificial intelligence logic, or use of the same. In the platform, the artificial intelligence logic may be configured to realize one or more artificial intelligence algorithms. The artificial intelligence algorithms may comprise graphical model and statistical inference.

[00139] In an enterprise, there are many employees with various professional backgrounds. Based on the professional backgrounds, the platform can shadow the user with different community groups. One of the strengths the platform has is to facilitate collaborative learning. Recording the learning activities of users assembles learning journals. Moreover, the platform analyzes the learning journal of the users to automatically adjust the community setting and profile setting, which can further be fed back to the enterprise to understand better their employees' skill sets. The cyclic analysis/feedback results in a positive learning environment, leading to knowledge-oriented enterprises.

[00140] Referring again to FIG. 1, the platform design comprises multiple layers:

Application layer, APIs, and data/infrastructure layer which drives search, analytics, recommendations and the intelligence of the social/collaborative learning platform. The social/collaborative learning platform is personalized with the assistance by the artificial intelligence logic. The artificial intelligence logic comprises cognitive graph in this disclosure. The cognitive graph, as the name suggests, can cogitate users' behaviors and then understand the contents, followed by performing intelligent search and recommendations to users for learning. The learning activities documented in the learning journals is further in the cognitive intelligence to extract more suitable contents for learning purpose.

[00141] The contents, user profiles, learning journals, access behavior, social networking, geolocation may be used individually or collectively to facilitate the learning process. Based on these data types, the artificial intelligence logic may create expert graphs, intent graphs, interest graphs, social graphs, and learning graphs. These graphs in turn drive the search and recommendation engine.

[00142] The artificial intelligence logic in the social/collaborative learning platform is a core of data analytics for performing one or more of the following key functions: (1) Drive unique functionality into the mobile and web apps such as interactive visualization of topic and content trends, for instance, to create a fun, engaging, and efficient experience for the users to access data, information, and content; (2) Perform deep understanding of the integrated social/expert graphs and provide users the ability to control their own search filters; (3) Perform social semantic search to understand their own archetype for learning, or integrating with content, information, etc.; (4) Have the capability to use an alternative archetype in a social graph to access resources, information, and content from a different perspective, personalized social graph search results; (5) Use the social data to help researchers validate user generated data; (6) Provide users information to support their research, display of visualized data; (7) Build user profiles around social graph, intent/interest, search, demographics, etc.; (8) Analyze data across data types to identify weak/strong signals and patterns in the data and present it to various users based on roles/functions in the platform, (9) Provide data simulation/predictive services and present it to the user based on roles/functions in the platform, contextualize data; (10) Provide alerting and monitoring services for internal operations teams; (11) Provide data analysis tools for data scientists; and (12) Push data to the visualization layer to be viewed and made actionable to the user in the form of reports or automated services for collaboration and coordination.

[00143] Artificial intelligence logic comprises intelligent agents and algorithms which are used to enhance functionality, personalizing through recommendations and intuitive search, and tailoring (personalizing) the user experience. The users of the social/collaborative learning platform can use statistical tools included in the artificial intelligence logic to assist in identifying weak and strong signals in data to improve and provide new methods of learning that are reliant on the behavioral trajectory of the users. The information utilized by the social/collaborative learning platform may include one or more of the following: (1) geospatial temporal data; textual and image data; (2) object and facial recognition tools to support rapid data collection and categorization and analysis; (3) correlative information across disparate data types (e.g., Linkedln resume data to develop learner paths and help with cold start; recommend content from Twitter that corresponds to user key "Declared roles"; twitter feeds tuned to client needs, Facebook data); (4) different search technologies used in all aspects of the platform; for example, to "Discover" content, resources, experts, and more; and (5) development of cognitive architecture; archetypes of people and behavioral changes; development of a Cognitive Graph from the data for the personalization process.

[00144] Artificial intelligence logic may comprise a mix of machine learning algorithms and deep learning algorithms that develop a flexible adaptation of the user cognitive and non- cognitive behaviors (and a learning pathway). The system creates a "cognitive graph" (a dynamic archetype based on conditions/context) of the user. Contents, resources, experts and mentors to be selected or recommended may be determined by the deep learning algorithms formulated as a dynamic programming problem whereby action-dependent state transitions are expressed as

Figure imgf000029_0001

and the expected reward is given by

r 1

ss

The goal of this equation is to then maximize the sum of discounted rewards over time,

R(t) = rt+l + K+2 + frt+3 + ... X rt+T+l [00145] FIG. 7 shows algorithms to generate a cognitive graph. The dynamic system relies upon the input users, for example "experts", to define the two constructs for the dynamic programming problem in the "top down" process. A solution to the dynamic programming problem in determining archetypes under various conditions/environments (context) is finding the optimal action (i.e. curricular/content selection) at every state of the behavioral trajectory or learner progression. Once sufficient data is collected, the system adaptively personalizes the constructs for each individual user or learner (educator & student) and match the users/behavioral/learning trajectory to its "N closest users" (or learners), it refines the dynamic programming model by using data from the "N closest users" and optimizes the professional development/mentor/content selection with the refined dynamic programming model.

[00146] Personalization systems typically reflect "if-and-then" statements building adaptation based on known trajectory, data, and contents that are fed up to the users under pre-determined structures. The problem with this approach is that users cannot use the expanding and more interesting and relevant content, experts, and resources from outside a closed system that reflects the "current" need for the content, expert, or resources. At least some prior approaches on the market only adapt to "known" or pre-defined archetypes with rather reflecting the context driven situational learning needs. In some cases, the

social/collaborative learning platform as described herein can turn this notion of

personalization on its head by tailoring the user experience/pathway to meet their "declared" intent (goal) by recommending content, experts/mentors, learning or behavior pathways from an open "system" perspective.

[00147] By observing pre-conditions and conditions associated to real time behaviors within the social/collaborative learning platform as well as through other connected social and legacy data platforms, the social/collaborative learning platform can be configured to provide dynamic archetypes of users, the social/collaborative learning platform can be configured to approach archetypes as dynamic because behaviors and associated outcomes change based on conditions (context associated with time, location, social, content, sentiment/ affect, locus of control, etc.). Thus, the social/collaborative learning platform may be configured to observe behaviors in context of intent/goals and develop probabilistic models of user path/behaviors and potential outcomes associated with the variable state of perceived "success" and/or "failure" and the probable states in between. The system can be configured to learn the conditions that lead to various outcomes for the user based on the context/conditions associated with success and failure and all the points in between, and begin to make predictions about resources, experts and content that could ultimately change the path the user is on to move them towards their intended or desired goal. Hence, since goals are not static, the processor system comprises instructions to approach the dynamism

pro gr ammat ic ally .

Cognitive graph

[00148] The artificial intelligence logic is able to analyze various types of data in the digital space to create a cognitive graph. FIG. 8 shows inputs, layers and intent based outputs to generate a cognitive graph in accordance with a method and apparatus as described herein. Cognitive Graph provides the personalization layer that combines the cognitive-behavioral, sentiment, social, geo-location, and declared intention together to build archetypes of users based on context or conditions.

[00149] The steps of the method of generating a cognitive graph include one or more of: receiving inputs, biasing the input data, providing initial behaviors, determining an archetype, determining conditions, determining success or failure logic, determining desired behavior, and generating the cognitive graph. The data from the cognitive graph can be input into the process and the steps repeated to provide an improved cognitive graph with repeated iterations.

[00150] The inputs at the input step may comprise inputs from one or more apps such as a capture app, a curate app, a journal app and an action app. The input may comprise inputs from one or more social networks such as Declara, Yo Solution, Emphasis, Twitter, Google and Facebook. Additional social networks can be provided.

[00151] The biasing at the biasing step may comprise biasing in response to one or more of Geo-Spatial or temporal biasing.

[00152] The initial behaviors at the initial behavior step may comprise one or more of an Intent Graph, a social graph, or a learning graph. The intent graph can be provided in response to one or more search parameters such as what, why, planning activities or crowd source. The social graph can be provided in response to who data such as one or more of when, where, who and with whom. The learning graph can be generated in response to one or more behavioral processes such as a progression, knowledge maps, or vocational frameworks, for example.

[00153] The archetype of the archetype step can be generated in response to one or more of a plurality of archetypes of the user. The archetypes of the user can be related to a professional role of the user, for example a professional role of the user as chief executive officer or board member or teacher, for example.

[00154] The conditions step may comprise logic in response to one or more of many conditions of the user or the context of the search, and combinations thereof.

[00155] The success or Failure step can be used to determine an output as successful or as a failure in response to the preceding steps of inputs, bias, initial behaviors, archetype and conditions.

[00156] The desired behavior step determines the desired behavior in response to one or more of motivation, pro-social behavior, perseverance, transactions such as donating, giving and funding, and additional behavior as appropriate.

[00157] At the cognitive graph step, the cognitive graph is generated in response to one or more of the preceding steps. The output of the cognitive graph can then be input into the input step, and the graph steps repeated in order to generate the cognitive graph iteratively.

[00158] The cognitive graph enables one to create a "behavioral trajectory," where is parameterized by a declared intent or goal. Given people behave differently under different types of conditions and locations, and social context, the processor system of the

social/collaborative learning platform comprises instructions to build a dynamic cognitive graph for all archetypes observed and to develop predictions based on these observations, which take into consideration the variability observed. Ultimately, content, experts, resources, and conversations can be recommended to users based on their archetype under specific conditions. Also, users could potentially adjust their filter for accessing information from being very tightly personalized or to very loosely reflecting them or even access information based on a different archetype within their social graph or a shared social graph.

[00159] The artificial intelligence logic may be coupled with permeable membrane logic and social networking logic. Two or more ways to create a profile in the digital space comprise: (1) signing into the social/collaborative learning platform integrating a public social network (Linkedln, Facebook, Twitter, Foursquare); or (2) create profile within an App and/or the social/collaborative learning platform. The platform may provide the user profile creation with the ability to allow users to describe themselves, show their interests, goals and friends. The platform can be configured to also allow a profile picture or video to be uploaded, for example. The platform can be configured to provide social graph visualization from integrated social networks in order for users to have an explicit view of their social graph, intention graph, learning graph, their emergent "Cognitive Graph" (Archetype) and their interactions with the graphs. [00160] Content searches may be crowdsourced to other network users or out through other social networks, social graphs, or systems identified by the user. In many conditions, the social/collaborative learning platform may make searching intuitive. The processor system may comprise instructions to give a filter bar to the user so they can control their own "filter bubble" such that their searches can be reflective of their profile and search patterns (highly personalized) or not personalized at all and allow everything in between.

[00161] The social/collaborative learning platform's recommendation system may be capable of identifying experts, mentors and/or collaborators from across other social networks if the user opts to open their community beyond the social/collaborative learning platform network.

[00162] The social/collaborative learning platform is built around a notion of "intent", for example a purpose, associated with self-discovery and learning. The social/collaborative learning platform uses the data analytics tools, (badging framework, search, ratings, trending of topics and user content, and more) to build an "expert" profile that can be optimized and visualized into an expert graph. Expertise can be defined around a "person", network, or contents. Intents of a single user or across multiple users can be described as graphs. The intention graph may be based on learning journals or events. The platform records the activities of the users, and infers the intents of the activities, followed by interpreting and predicting the future activities and/or recommending learning contents to users. The intent can be seeded by users; i.e., a user sets up a library of "plans", which may comprise keywords, interests, descriptions, actions, activities, events.

[00163] The artificial intelligence logic may be configured to create an intention graph, which is coupled with event calculus graph and behavior recognition. The behavior recognition may dynamically construct partial places, corresponding to the actions executed by actors/users. Intention recognition may be configured to be based on key hole to infer intention.

[00164] The platform may utilize the artificial intelligence logic to create an Intent App. The intent app may rely on social planning tools, social story, social search, and/or cloud search. The platform may comprise a Help Me App, which can help users to acquire knowledge. The platform may be configured to analyze knowledge seeking actions or physical actions to assist users for knowledge acquisition. On the other hand, the intent app may use event calculus to guide the user through required actions. In some cases, the intent app does not rely on a library of plans. The intent graph use graph search through state changes, for instance, using full or partial action goals to make prediction. In the knowledge seeking step, the platform may observe actions to provide users with information about what the user has known and what needs to be learned, based on weighted evidence calculations.

[00165] The artificial intelligence may be configured to analyze actions. The semantics of actions may include terms of preconditions and the influencing variables or time dependent properties. The variables have initial states and terminal states, from which the analysis can identify crucial properties.

[00166] The artificial intelligence logic may comprise automatically observing actions and provides users with information. The information may comprise what have been known and not known. Analyzing the information with weighted evidence leads to summarizing the knowledge already possessed by users, and the knowledge can be further acquired/learned in the (near) future. The actions already taking place can be recorded with the timestamps; thus the time-dependent actions analysis can enhance prediction accuracy. For instance, let A, P, T denote action, property, time, respectively; mathematically (A, P, T). In an initial condition, we have (Α,Ρο,Το), along with the time, we can record (A,Pt,Tt). Analyzing the progression of (A,Pt,Tt) can infer properties specific to the action A.

[00167] Referring to FIG. 9A, the intent recognition algorithm in general is described as follows. In the initial state, the property P can be dependent on action A and property variables Pi to Pn. The indices of P can denote time, or various other properties. After the user takes action, the values of A, Pi, Pn may change. At a termination state, the platform tracks the changes and infers what variables are relevant to property P. The identified variables Pi to Pm become precondition variables of property P. This analysis procedure is recursively implemented. Therefore, the platform can dynamically track actions, properties, and contents, resulting in making good recommendations for social learning as described herein.

[00168] The artificial intelligence logic may comprise primitive fluents/properties, or use of the same. In the preceding paragraphs, all variables are assumed universally quantified in front of the rule, unless otherwise specified. The first rule states that a fluent of holds at time T2 if an action A initiating it was done at an earlier time Ti. Moreover, all of the actions preconditions held at the time and the fluent P has not been clipped in the interval between Ti and T2. A fluent is clipped in a time interval if an action occurs in the interval that terminates the fluent. The artificial intelligence logic may comprise ramification, or use of the same. Fluents/properties may be dependent on others. For instance, a fluent Q may be dependent on Pi, P2, ... Pn.

[00169] Referring to FIG. 9B, in the intention recognition, observed fluent will typically be properties that can change without the intervention of the actions. Observations of fluent also facilitate dealing with partial observability of actions. Actions are directed towards achieving the preconditions of the intended actions thus making the action executable. Observer may not see actions of Al and A2 executed, but sees action A5. Al and A2 are needed only to establish the preconditions of the executability of A5, so not observing Al and A2 does not distract from possibility of being an intention A5 may have held.

[00170] FIG. 9C depicts a flowchart of intention recognition. When action/property is observed, the platform combines other observed fluent, profiles, and weighted constraints for intention recognition. On the other hand, the recognition also combines heuristics and hypothesis (or other weighted variables) to iteratively update intentions. The iteration is optimized till a static state, and the intent can be inferred. Profiles are positive variables, because they provide information (e.g., behaviors) on what we already know about the actors/users. Integrity constraints are negative variables because they indicate what actors/users do/will not do. Heuristics are domain dependent, attempting to distinguish between consequences of actions and intentions motivating them. Actions may be incidental or side effects of actions, which are consequences. Immediate intentions may include temporal effects. Domain independent specifics have cutoff points (numerical thresholds), beyond which the intention recognizer does not look further into the possible intentions. The intention recognition may contain several knowledge bases, including social networking, representations of current state of the actor/user, environment, plan libraries, a possibly empty library of plans, and a library of basic causality. When a fluent is observed, the intention recognizer updates the knowledge bases by assimilating the fluent.

[00171] The artificial intelligence logic may include rich media intelligence. Frequently, apps tethered to the social/collaborative learning platform or connecting into the platform through the APIs may provide photos with metadata (e.g., geolocation), annotation (e.g., intent, interest, and story data), tagged people (connected into contact list) and tagged objects. The functionality should provide an "Image Gallery" of content where the user can upload content or searches or curated URLs. The social/collaborative learning platform can support informal learners, which is equivalent to an aggregate of the enterprise, consumer curators, and published curator pages, etc. The social/collaborative learning platform may allow the possibility to organize and semantically search photos/content by interest, intent, expertise, subject, author, title and/or date. This technical feature allows acquired contents and curated contents to be tagged with date, author/contact data, annotation/intent data, description, and title as well as geolocation information. [00172] Contents and curated content pages may be crowdsourced to other network users or out through to other social networks, social graphs, or systems identified by the user.

Social networking

[00173] The methods, systems, media, and platforms disclosed herein may include social networking logic, or use of the same. Coupled with the artificial intelligence logic, the social networking logic can analyze access behaviors, the insights, the learning journals, and the user profiles. The social networking logic can use the analysis to create social networks for the plurality of users.

[00174] The users and social learning platform can use partnerships and other relationships to inspire growth into a consumer community by leveraging mobile technology that inspires informal learning twenty four hours a day, seven days a week (24/7). The

social/collaborative learning platform can use open content distribution and user generated content to become the open social semantic search platform for continuous learning globally.

[00175] In many examples, the social/collaborative learning platform may be based on mobility and location awareness, not only focused on providing location based information also by providing greater contextual awareness of whom and what is around the user. This capability will also allow for geo-location to be placed on resources and content as well. Geo- mapping will then allow for insights on geographic and regional trends on topics, interests, resources, expertise, and mentors, and help users to find out interesting information about places, add their own comments/content for the benefit of others, and help them to have greater direct access to people and resources, to support their learning and work path.

[00176] The social networking logic may incorporate existing open content management backend tools such as commercially available Amazon Web Service (AWS) technology and or Jack Rabbit to support the rich media or other content types. The value of the content management systems to the social/collaborative learning platform is to store, search, and manage large amounts of complex contents and rich media. The platform can be configured for clients to have the ability to build intelligence into their video, image, and audio files in the future.

[00177] The social/collaborative learning platform may comprise an extensible platform to meet the requirements of the business needs of the company. An essential feature is to provide the capacity for extensibility in the functionality in the platform through exposed APIs for App developers and users at the rate in which inspires creativity yet maintains stability in the system and user experience. [00178] The social/collaborative learning platform may have the ability to connect to other social networks via there APIs. The functionality may be dependent on the public API provided. Common features of interconnecting social networks include status upload, share content, messaging and finding friends.

[00179] The entities adopting the platform may include government, enterprise, and other types of platform users who want to have a private social network or enterprise- SaaS experience and public social media network to protect their users, content, and data. The private communication layer may be useful for cross department collaboration and learning.

[00180] Social networking logic may include Intelligent Persistent Chat (pChat),

Messages/pChat- workspaces. The social/collaborative learning platform users may exchange messages with other users. The message feature can provide synchronous and asynchronous conversation to occur and persist beyond the session such that the user can return to the conversation and with one or many other users. The user can set the permissions such that the pChat tool can "search" for similar conversations across networks through a "lateral" search feature in the system. The user can also set permissions such that they can use the "BRIDGE" tool in order to search out into the public social network and other connected networks such as Linkedln, Facebook, Twitter, etc.

[00181] Chat and forums have always been created to allow simple communication between users. As social technologies grow, chat systems have been developed to allow file transfers, group chat, and forums. Chat normally allows the users to rate and or vote on the topics the user finds important or of interest. However, without some kind of intelligence layer, chat and forums are still following the skeuomorphic design pattern of just allowing people to communicate with one another, without using important technological advancements in computing.

[00182] When chatting with others, file transfer created in the platform offers seamless user experience; this feature was a challenge in prior art because file transfer relies on a direct connection. The platform transfers files between users can easily resume from the interrupted point when the network connection is interfered, rather than resuming from resending the files. File transfers also may be limited to single recipients, or multiple recipients.

[00183] Managing multiple people in a chat is a difficult problem because it requires a server hosting the group chat and clients that connect to the server to communicate. An additional issue with this approach to group chat is accessing a log of chats given that the logging on the client only produces chats during the time that the client is logged in, and logging on the server is only available to those who have access to the server. [00184] The solution to managing chat among users is as follows. In persistent chat, all conversations take place in an environment that can store content and a history of all conversations on the server. When instantiating a conversation with someone (or a group of people), the conversation can be in real time or whenever the other members of the conversation are online. Users are always able to view all parts of their associated conversations, with any device anywhere.

[00185] When users log in the platform, they can see the conversations that they have missed from the last time the users were part of a conversation, regardless of what device they use to log into the system. All the conversations and content of a discussion are stored on the social/collaborative learning platform server and displayed to all participants in a conversation based on the permissions set by the owner of the conversation. Thus, new members are able to see everything that has been discussed before they were invited to the conversation.

[00186] Because contents are saved on the social/collaborative learning platform server, any member of the conversation is able to access the content from any device given they were granted the permission to access the content. Users can bookmark lines or words that are interesting to them for later. They can add notes to the bookmarked lines, can share their bookmarks with others, and also see a list of all their bookmarks for easy navigation and digestion of important content.

[00187] Users can also organize the discussion by separating the conversations by categories and make any or all categories visible to some or all members. While having a conversation, a user is able to see what the topic of the conversation is, and the trajectory of the

conversation by looking at the generated intelligent map of the conversation. A conversation will automatically have an auto -generated and organized table of contents of the subjects talked about in the conversation, which can be made discoverable within the platform and out to the "Public" network through the BRIDGE, which allows for a lateral and external search and "discovery process" for experts, interested others, and content that may be relevant to the conversation. Anyway, brought in from other networks or from the public social network, will be brought into the permeable space that sits between the public social network and the private and allows for collaboration with non-network users to collaborate with those within a private social network.

[00188] All of these features put together improve communication and collaboration by incorporating processor system technology available to a person of ordinary skill in the art. With the social/collaborative learning platform's intelligence, relevant networks and content can be revealed while having a conversation. This intelligence can make sure users are not doing redundant work and in fact can build upon each other's work and curate existing content together. PChat may suggest experts about the conversation, and allow users to ask the expert to contribute to the discussion. Duplicate conversations can be detected if multiple users are talking about the same things, and will allow users to join together if they choose to collaborate around particular topics, work streams, content creation, curation, and more. Keyword scanning (search) can allow the users to see other relevant discussions that have happened, allowing users to draw some of the conclusions that others have come to for the same or similar problem. A user can search for any keyword throughout all of their conversations and filter their search based on category, bookmark, or conversation.

[00189] Overall, Persistent chat will revolutionize how people communicate on the internet. Collaboration can happen in real time or posted for the next time users log in to see comments. In the past, forums, chat, and group chat all existed separately. Persistent chat allows all of these elements to work seamlessly together in one intelligent solution.

[00190] The social networking logic may create collections from Social Media cues. The platform may be configured to automatically creating collections based on combining previously shared articles in social media and additional attributes. The platform enables users to individually or collectively create, edit and share collections. One of the cold-start challenges is the ability to pre-populate the platform. The challenge can be solved by scraping public sources of information like social media platforms and pulling together all the hyperlinked articles a user has previously shared. The platform automates pre-population of collections so users have a head-start and are incented to take possession of their user profiles and then to share out their collections to their followers. The platform scraps various social media sites for URLs of articles shared users of the social network. The platform ingests the articles and categorizes them by content, time, source and other attributes. The automatic collection creator creates a set of collections for the user. Following this, the platform connects with the users to enable them to take control of their collections. They do so by authenticating their identity using the social networks authentication system. This feature enables users to collect popular articles and help create a library of collections. The value to the user is automatic migration of content that they love and have shared. The value to their followers is a single place to access all articles in one place (like a library). The feature of proactively pulling together previously published articles by an individual and creating a categorized library of collections. Creating a user's profile constitutes his/her depth of engagement and breadth of influence around topics/subjects. Overall, the advantage is to save time for the user in terms of migrating data and enrich the platform's content pool.

Mobile communication

[00191] The methods, systems, media, and platforms disclosed herein may include mobile communication logic, or use of the same. The mobile communication logic can allow the plurality of users to access the plurality of contents on mobile devices. Furthermore, the mobile communication logic may send push notifications of recommended contents to the plurality of users on mobile devices.

[00192] The methods, systems, media, and platforms disclosed herein may include mobile devices, or use of the same. A mobile device comprises mobile access logic to provide on- the-go access to the digital space, and the contents therein. The mobile access logic allows the mobile user to receive one or more push notifications from the server application.

[00193] The social/collaborative learning platform may track the location of users and content and store geolocated information to visualize where learning is happening on a map that is similar or accretive to the users. Being able to use questions based on geolocation of content and expertise would be helpful to the user.

[00194] In many instances, the instructions of the processor system of the

social/collaborative learning platform comprise an "archetype" app that lets a user know when someone with similar learning style, interests, or goals would be engaging and could be a way to ramify viral use by providing an improved user experience.

[00195] The social/collaborative learning platform's mobile technology will provide capacity for crowdsourcing for seeking experts and information, sharing information, gathering data, and sharing, curating, discovering, and collecting content. In many instances, leveraging and understanding the expert graph is helpful to the success of the users of the platform.

Add-on features

[00196] The social/collaborative learning platform can be configured with additional features, which are technical by nature but whose focus is not the direct interaction with the user but impacts indirectly. The add-on features can be provided with the social/collaborative learning platform for its correct functioning in a social network environment, for example.

[00197] The social/collaborative learning platform may be based on a trust on-line social network, that users can develop new relationships and share information without the fear of been cheating by fake users or malicious information. The system can be configured to guarantee user privacy and to allow users to define the privacy degree of their information. The users will have the option to select what they want to share and for whom (visibility of their information).

[00198] The social/collaborative learning platform may comprise a new on-line loosely coupled mobile social network/ semantic search tool that is scalable, in order to accommodate crowdsourcing content, data, information, and receiving data, content, images and video from crowdsourced users. In many instances, the social/collaborative learning platform is configured to accommodate a growing demand of new users without the need of

redevelopment.

[00199] The social/collaborative learning platform may provide a quality of service for all users connecting through to the platform through Apps, APIs, and the Web presence equally. Users may range in ages from young to old and all levels of technology skills. The processor system of the social learning system can be configured to provide a response time which is deemed reasonable for a non-critical system and which does not impede normal use. This platform can be configured to serve its users across time zones, languages, and needs to be accessible 24/7.

[00200] Permeable membrane logic of the social/collaborative learning platform may be configured to handle security, such as in accordance with the cyber security standards (e.g., ISO27001). The social/collaborative learning platform users can rely on confidentiality, integrity and availability of their information.

[00201] Extension of the social/collaborative learning platform can be provided with the addition of new functionality or through modification of existing functionality, with little impact on the overall structure of the social/collaborative learning platform. New services may be combined with the services already provided without an impact on the architecture. The extensibility of the platform could be achieved as simply as adding additional logic or adjusting existing logic, for example with one or more additional software modules or libraries such as a dynamic link library.

[00202] The social/collaborative learning platform may conform to relevant standards thus enabling the platform to be extended as well as being extended to incorporate other social networks, platforms, and applications. The user may not be restricted to a social network in particular but may instead be able to link to wider communities from and into the

social/collaborative learning platform.

[00203] The platform may include human-computer interaction, computer accessibility to all people, regardless of disability or severity of impairment. The software platform may comprise applications that enable the use of a computer or a mobile device by every person, independently of any possible disability, and any special device (Assistive Technology) that they have to use.

[00204] The social/collaborative learning platform may incorporate existing content management and social network tools into The social/collaborative learning platform. Open Source tools that can be integrated in accordance with instances disclosed herein comprise one or more of data and data analytics, Social Semantic Search and recommendation engines, and business design, such open source tools, for example.

Underlying computer systems

[00205] The present disclosure provides computer control systems that are programmed to implement methods, media, and platform of the disclosure. FIG. 10 shows a computer system 1001 that is programmed or otherwise configured to provide a platform described herein. The computer system 1001 can regulate various aspects of artificial intelligence logic, permeable membrane logic, content control logic, social networking logic, and mobile communication logic of the present disclosure. The computer system 1001 can be a server of an enterprise or a computer system that is remotely located with respect to the electronic device.

[00206] The computer system 1001 includes a central processing unit (CPU, also

"processor" and "computer processor" herein) 1005, which can be a single core or multi core processor, or a plurality of processors for parallel processing. The computer system 1001 also includes memory or memory location 1010 (e.g., random-access memory, read-only memory, flash memory), electronic storage unit 1015 (e.g., hard disk), communication interface 1020 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 1025, such as cache, other memory, data storage and/or electronic display adapters. The memory 1010, storage unit 1015, interface 1020 and peripheral devices 1025 are in communication with the CPU 1005 through a communication bus (solid lines), such as a motherboard. The storage unit 1015 can be a data storage unit (or data repository) for storing data. The computer system 1001 can be operatively coupled to a computer network ("network") 1030 with the aid of the communication interface 1020. The network 1030 can be the Internet, an internet and/or extranet, or an intranet and/or extranet that is in communication with the Internet. The network 1030 in some cases is a telecommunication and/or data network. The network 1030 can include one or more computer servers, which can enable distributed computing, such as cloud computing. The network 1030, in some cases with the aid of the computer system 1001, can implement a peer-to-peer network, which may enable devices coupled to the computer system 1001 to behave as a client or a server.

[00207] The CPU 1005 can execute a sequence of machine-readable instructions, which can be embodied in a program or software. The instructions may be stored in a memory location, such as the memory 1010. The instructions can be directed to the CPU 1005, which can subsequently program or otherwise configure the CPU 1005 to implement methods of the present disclosure. Examples of operations performed by the CPU 1005 can include fetch, decode, execute, and write back.

[00208] The CPU 1005 can be part of a circuit, such as an integrated circuit. One or more other components of the system 1001 can be included in the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC).

[00209] The storage unit 1015 can store files, such as drivers, libraries and saved programs. The storage unit 1015 can store user data, e.g., user preferences and user programs. The computer system 1001 in some cases can include one or more additional data storage units that are external to the computer system 1001, such as located on a remote server that is in communication with the computer system 1001 through an intranet or the Internet.

[00210] The computer system 1001 can communicate with one or more remote computer systems through the network 1030. For instance, the computer system 1001 can

communicate with a remote computer system of a user. Examples of remote computer systems include personal computers (e.g., portable PC), slate or tablet PC's (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones (e.g., Apple® iPhone, Android- enabled device, Blackberry®), or personal digital assistants. The user can access the computer system 1001 via the network 1030.

[00211] Methods as described herein can be implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system 1001, such as, for example, on the memory 1010 or electronic storage unit 1015. The machine executable or machine readable code can be provided in the form of software.

During use, the code can be executed by the processor 1005. In some cases, the code can be retrieved from the storage unit 1015 and stored on the memory 1010 for ready access by the processor 1005. In some situations, the electronic storage unit 1015 can be precluded, and machine-executable instructions are stored on memory 1010.

[00212] The code can be pre-compiled and configured for use with a machine have a processer adapted to execute the code, or can be compiled during runtime. The code can be supplied in a programming language that can be selected to enable the code to execute in a pre-compiled or as-compiled fashion.

[00213] Aspects of the systems and methods provided herein, such as the computer system 1001, can be embodied in programming. Various aspects of the technology may be thought of as "products" or "articles of manufacture" typically in the form of machine (or processor) executable code and/or associated data that is carried on or embodied in a type of machine readable medium. Machine-executable code can be stored on an electronic storage unit, such memory (e.g., read-only memory, random-access memory, flash memory) or a hard disk. "Storage" type media can include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which may provide non-transitory storage at any time for the software programming. All or portions of the software may at times be communicated through the Internet or various other telecommunication networks. Such communications, for example, may enable loading of the software from one computer or processor into another, for example, from a management server or host computer into the computer platform of an application server. Thus, another type of media that may bear the software elements includes optical, electrical and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links. The physical elements that carry such waves, such as wired or wireless links, optical links or the like, also may be considered as media bearing the software. As used herein, unless restricted to non-transitory, tangible "storage" media, terms such as computer or machine "readable medium" refer to any medium that participates in providing instructions to a processor for execution.

[00214] Hence, a machine readable medium, such as computer-executable code, may take many forms, including but not limited to, a tangible storage medium, a carrier wave medium or physical transmission medium. Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer(s) or the like, such as may be used to implement the databases, etc. shown in the drawings. Volatile storage media include dynamic memory, such as main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that comprise a bus within a computer system. Carrier-wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media therefore include for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with patterns of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which a computer may read programming code and/or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

[00215] The computer system 1001 can include or be in communication with an electronic display 1035 that comprises a user interface (UI) 1040 for providing, for example, Examples of UFs include, without limitation, a graphical user interface (GUI) and web-based user interface.

[00216] Methods and systems of the present disclosure can be implemented by way of one or more algorithms. An algorithm can be implemented by way of software upon execution by the central processing unit 1005. The algorithm can, for example, be artificial intelligence algorithm.

EXAMPLES

Example 1— Social Platform and Providing Text Insight

[00217] FIG. 11 shows an example of collections of a social platform based on the disclosure described herein. A user can acquire several collections of articles. The collection of articles may comprise articles that the user acknowledges with a "Got It" indication. The collections of the user may comprise one or more of many articles that the user has indicated are to be stored in the corrections. Alternatively or in combination, articles can be automatically stored based on user preferences. The user collections can be fed as input into the cognitive graph generating method as disclosed herein.

[00218] This platform comprises a graphical user interface as described herein. The header may include a HOME button; pressing the button leads the user going back to the homepage of the platform. Next to the HOME button is a COLLECTIONS button; pressing the

COLLECTIONS button can bring the user to the library of collected contents. The header may further comprise an IMPORT CONTENT button; by pressing the IMPORT CONTENT button, the newly recommended contents made by the backend server can be imported the personal library of the user. Next to the IMPORT CONTENT button is a bell icon, which shows 4 unread messages. The right corner of the header shows the name and picture of the user; in this example, the user is Holly.

[00219] The left sidebar comprises various components. The top component is an article titled "Advice from Successful Product Managers" authored by Matt Bariletti. Below this article information is a FOLLOW button; pressing the FOLLW button may allow the user to follow the author. Further down the left sidebar includes various contents automatically recommended by the platform. In this example, there are 7 articles: "Product Management vs. Engineering"; "Where Should Product Management Live?"; "Roman's Product Management Framework"; "How to Write Great Product Specs"; "Why I love User Stories"; "10 Tips for Writing Good User Stories"; "Single Window Interfaces."

[00220] The main body area shows the article "Where Should Product Management Live?" that the user was reading. This article further contains an insight already provided by another user, where the insight corresponds to the highlighted sentences of the third paragraph. Note that next to the insight are insight value (which shows 4) and insight indicator (which shows a lighting bulb).

[00221] In the footnote area, the platform may provide few buttons and information about the article. The LIKE button allows the user to express the like for this article. The SHARE button allows the user to share the article with other community members. On the right hand side of the footnote area includes the status of the article: Norman Tran and 23 others collected this.

Example 2—Video Insight

[00222] The platform may be configured to allow a user to provide insights on videos by creating, reordering and playing clips from a video. Audio files can be similarly processed to provide audio insights. The series of selected insights (insight playlist) from multiple videos can also be stringed together to create an insights playlist. Individual insights and insight playlists can be shared to others through a public network (e.g., Internet) using public social networks/emails or enterprise social networks/emails. The insight is created using a simple selection of the start and the end of the clipping from a video. The user can also textually annotate the insight to describe it. Advantages of providing video insights include (1) Insights can be used to create a summarized version of the video; and (2) Parts of different videos that share the same concept can be used to create an insight playlist which will allow the users to consume relevant information faster.

[00223] Providing insights on videos is to create an alternative method of curating videos without needing a specialized software or training. This insight creation process is based on selecting a clip from the video. Referring to FIG 12(a), when a video content is viewed by a user, the user selects the starting point in the video timeline and as a result, the system displays that the insight creation process has started. Now when the user selects another point in the video timeline, the insight selection is completed and is reflected in the video display itself. The user can now create an insight from this selection by clicking on a button and optionally add annotation to this insight to more information about this insight, shown in FIG. 12B. In FIG. 12B, after marking the clips, an interactive window pops up to allow the user to enter text descriptions annotating the importance of the clips. Once the text annotation is entered, the user can press the CREATE button to complete the insight on the video.

Referring to FIG. 12C, once the insight is created, an insight indicator (which is a lighting bulb) is displayed next to the video control bar. Furthermore, the system further displays to the user further actionable buttons: Play the insight; Share the insight; Remove the insight (if viewer is the user who created the insight.

[00224] The user can create multiple insights on the same video and the insights can even overlap each other. These insights can also be combined together to create a playlist of insights by selecting all the insights the user has created from different videos.

[00225] The video insight can present video segments out of order with most important video insight data segment first, followed by less important segments. The video insight is not limited to one video clip, and may comprise a plurality of video clips. The video insight can be combined with other data such as the location of the video, a user of the video, a person in the video, and the video can be combined with the declared intent platform to determine one or more intentions of the person shown in the video, for example.

Example 3— Portable ID and Insight Collection

[00226] The digital collaboration platform may be configured to allow user to use portable IDs and collect/organize insight contents. Referring to FIG. 13, the platform may comprise a cloud based infrastructure 1300, which creates a digital space for storing various types of contents as described herein. A first user can access the contents in the digital collaboration platform through a first displayl301, and a second user through a second display 1302. Users are associated with portable IDs 1303. The portable IDs may comprise unique IDs to access the digital collaboration platform. Moreover, the portable IDs may be linked to other public social networks. Non-limiting examples of possible public social networks include Facebook, Twitter, Linkedln, and/or YouTube. The user has access to contents of these networks and publicly available networks such as the Internet and Internet forums. When accessing the contents, the users may provide insights to the contents as described herein. The contents provided with insights may be organized and collected into a folder such as "My Insight Collection" 1304 within the digital space of the platform as described herein. Non- limiting examples of the types of insight contents, can be referred to FIG. 14, including to images 1401, news 1402, tables/databases 1403, audios 1404, videos 1405, emails 1406,

documents/articles 1407, and presentations 1408. Reference is made to FIG. 14 which shows similar insight icons for similar types of data. In the example shown in FIG. 13, the first user provides various insights such as video insights 1315, which can be configured as described herein. The insights are attached with insight indicators (e.g., light bulbs). When the insights 1315 are created, the digital collaboration platform may propagate the insights 1316 to the second user in one or more of many ways. For example, the platform may be configured for the second user to receive a notification that the first user has created and insight, and an invitation to view the insight of the first user. The insights of the first user can be provided in a folder 1305 for the second user to view and add to the "My Insight Collection".

Alternatively or in combination, in the Insight Collection 1304 of the second user, the video insights 1317 may be synchronized with the first user, for example. For teams of people working closely with each other in a fast paced environment such as firefighters, police and tactical teams, it can be helpful for the insight of one user to be synched with an insight of another user in order to respond quickly. Alternatively, in some configurations such as with crowd sourcing and large groups of people, it may be helpful for the second user to sort through insights of other users to determine whether these insights of other uses should be populated in to the second user. The second user may have a folder of new insights to review, and select insights to add to his folder with an input acknowledgement such as "I GOT IT", as described herein, and the cloud based server system can track the

acknowledgement of the second user with crowdsourcing as described herein. Although reference is made to first and second users, the system is scalable to numbers of users as described herein, from 1 to 10, 100, 1000, 10,000, 100,000 or 1,000,000 users or more, for example.

[00227] The "My Insights Collection" can be configured in many ways. For example, a user may click on a type of insight, e.g. text, to see a folder of text insights, or click on a video icon to see a folder of video insights. An icon can be provided in the "My Insights

Collection" for the user to select and view insights provided by other users with a prompt for the user to accept or reject the proposed insight from another user.

[00228] The user can be associated with other users in many ways. For example, a close association can exist in which the insights of strongly associated other users are automatically synched to insights of the user. There can be a weaker association, in which the user selects which insights of other users are added to the my insights folder. Alternatively or in combination, a user may have a "My Synched Insights Collection" for insights that are synched with other users, or a "My Selected Insights Collection" for insights that are selected with acknowledgement and input as described herein. The "My Insight Collection Folder" may have separate sub collections of insights based on 1) selection or 2) synching; and 3) combinations thereof, for example.

The insights can be made available to many other users through networks as described herein, for example with publishing.

[00229] Each insight stored on a user's device and associated with a user is not limited to a single type of data. For example complex insights can be created based on several different types of data input. Such insights provide greater amounts of information, and can be used to associate the insights with a particular item, such as an object, a person, a location, a structure, or a target, for example.

Example 4— Insight Complex and Aggregation

[00230] The platform may be configured to allow users to provide insights on various types of contents, and aggregate one or more the insights together. Referring to FIG. 14, users may have insights provided to images 1401, news 1402, tables/databases 1403, audios 1404, videos 1405, emails 1406, documents/articles 1407, and presentations 1408. Alternatively or in combination, the users may further provide insights associated with another person or a group of people (e.g., an existing user, a new user, a potential collaborator, an employee of the enterprise, an external person to the enterprise, an content creator, an expert, a suspect, a professional, a target, a visitor, an invitee, a family member, an enforcement person, an interviewee, a contractor, a government official, a military soldier, etc). The current users can provide insights to the person's face/picture 1409 or profile 1410, or can link the

face/picture/profile to a content or to an insight. Moreover, the users can provide insights to locations 1411 and/or events 1412; alternatively, the users may link the information of locations/time/events to a content or to an insight. Each of the users can collect insights of several users and combine these insights to form a combined insight. The combined insight can be distributed to other users in many ways as described herein. For example a user can post the combined insight to a public network or a secure network, and combinations thereof.

[00231] The processor of the user device may comprise instructions for the user to create combined insights. For example, the user can drag and drop insights, or portions of insights, into a folder or symbol designating the combined insight. The user can then publish the combined insight for crowdsourcing or other use by other users as described herein.

Example 5— Portable ID, Permeable Membrane and Contents

[00232] Referring to FIG. 15, the platform may be configured to permit some contents flow between the digital collaboration environment and public domains. When a user becomes a member of the secure environment, the user may have initial identification (herein "IDs") such as IDs from other accounts as described herein. The permeable membrane readily allows the public information to enter the secure enterprise environment as described herein. The software comprises instructions to create the portable ID 1511 when the user joins the secure environment as described herein. The user can generate insights and collaborate within the semi-permeable space of the secure environment as described herein. The portable ID is updated while the user works in the secure environment, and can be made public as the user works in the secure environment with abstraction of the data as described herein.

[00233] The digital collaboration platform is housed in a secured environment 1501 with permeable membrane 1502 as described herein. In the platform, users are associated with portable IDs 1511 as described herein. Various types of contents/insights 1512 (e.g., referring to FIG 14: images 1401, news 1402, tables/databases 1403, audios 1404, videos 1405, emails 1406, documents/articles 1407, and presentations 1408, insights, influencers, etc) are stored in the platform as described herein, and can be pushed out to devices of the user. Through the permeable membrane 1502, some of these contents/insights 1521 and 1522 can be made publicly available, for example. The software may comprise instructions to track influence "I" (i in Fig. 15) as described herein, and allows the user to make publicly available his influence (I) within the organization with the publicly available ID. In some conditions, when a person 1515 exiting the enterprise (e.g., a leaving employee, a leaving visitor), some publicly available data or non-confidential data associated with this person can be released to the public domains. Alternatively or in combination the non-confidential data can be made publicly available while the user works in the secure environment.

[00234] The software of the secured environment can be configured to scrub, for example abstract or remove, some of the insight data stored within the secure network prior to public release, for example.

Example 6— Cognitive Graph

[00235] Referring to FIG. 16, the cognitive graph is described herein. The platform collects various types of contents 1601 from public domains and private domains. The publicly collected contents are aggregated with the contents/insights stored in the enterprise. The platform uses a single, unified knowledge engine to aggregate massive amounts of contents and insights and makes it discoverable and connectable. The aggregated contents and insights may comprise one or more of temporal, spatial or geographic information.

Furthermore, the contents may be associated with actions; for instance, a document has been read for a number of times such as 1500 times; a video has been watched and shared for a number of times such as a million times; a dress has been sold, for example along with many copies; a stock has been rising, for example 7-fold since IPO. The contents, insights, information, and actions are collectively analyzed by a graphical model 1602, which determines and infers the strong associations among the contents, insights, information, and actions, represented by linked nodes. The resulting cognitive graph 1603 can enhance purposeful connections to people and contents; intelligent recommendations get people the information and resources they need to accomplish tasks and projects efficiently and get their jobs done.

[00236] The platform aggregates data contents by elastic search, natural language processing, makes sense of massive volumes of content, transcending the information that exist on the Internet and in distinct systems. The platform uses profiles to catalyze big data, institutional data and proprietary algorithms (e.g., an analysis method developed by an enterprise IT department) to understand each user's dynamic learning profile, breadth of influence and depths of engagement.

[00237] By use of the cognitive graph, the platform's predictive analytics save professionals time by pushing recommended contents and connections tailored to each individual.

[00238] The cognitive graph comprises a plurality of nodes and a plurality of edges, with the edges serving as a bidirectional link between pairs of nodes. Some nodes are associated with users and some nodes with contents; the former may be described as user nodes and the latter as content nodes. Further nodes, which may be described as concept nodes, can lie intermediate between user nodes and/or content nodes; accordingly, the cognitive graph comprises a plurality of edges linking concept nodes to user nodes and to content nodes. These further nodes are described as concept nodes because they embody, by their association with particular users and particular items of content, a concept. If an edge connects a concept node to a user or content node, we may say that the two nodes are associated, as reflected by the edge. For example, a concept node representing the color "red" might be connected to a variety of content nodes of content embodying this concept, such as images and video of red objects, articles or other documents associated with the word red, audio files associated with "red," users who like or are interested in red things, etc. Similar concept nodes for a wide range of concepts may be similarly created and associated with respective content items. By applying the techniques for updating the cognitive graph as described herein, each concept node reflects a concept as implicitly identified by the users and content of the system. Thus, if users and content only use the word "red" to describe objects colored red, a "red" concept node might represent this exclusive concept; by contrast, if users also associate "red" with communism, then the "red" concept node may be connected to content discussing communism and users interested in communism. Which of these two meanings applies to the "red" concept node is implicit in the connections between the concept node and user and content nodes: in the former case, the connected content and user nodes will only be associated with the literal color red; in the latter case, the concept node may also be associated with users and content linked to the "communism" idea.

[00239] The attribution of meaning to a node based on the nodes to which it is connected applies to content nodes and user nodes as well as concept nodes. For a user node, the concept nodes to which it is connected reflect the interests of the user. For a content node, the concept nodes to which it is connected reflect the ideas or subject matter that the content embodies. Because a user may be interested in different subjects to different degrees, content may reflect ideas to a different extent, etc., it may be desirable to have a magnitude associated with each edge in a graph. For example, an edge with greater magnitude may represent a stronger association, while one with smaller magnitude a lesser association. An edge with zero magnitude may represent no association, and be treated as though there were no edge at all. Negative magnitude connections may in some cases be employed to indicate an anti-correlation between nodes, such as a user explicitly disliking a particular concept, for example. This also allows the gradual change in, e.g., user preferences over time, as reflected in the changing magnitudes of the edges connecting the user node to other nodes. In any case, the set of nodes connected to a given node, along with their magnitudes, may be understood to represent a digital "fingerprint" of a user, content, or concept, as appropriate. As disclosed herein, this fingerprint can change over time as users and content interact with the system, resulting in changes to pattern and magnitudes of edges between nodes.

[00240] The mechanics by which a cognitive graph evolves over time in the present system are disclosed herein. New user nodes and content nodes are created when a new user or piece of content enters the system. A concept node may be originally generated by the system from a portion of content, such as component of the content, for example. Depending on the content from which he portion of content may be any of a variety of types of content portion, such as a word, a phrase, a sentence, a table, an image segment, a video segment, or an audio segment. The concept node may comprise a pointer to the portion of content from which it was created; for example, a pointer to a particular part of the content, and/or a pointer to a location in a database to which that part of the content can be copied for quicker access. When a concept node is created in this fashion, it can comprise an edge linking it to the content itself, as well as edges added based on the portion of the content from which it was generated; for example, if the portion from which the new concept node was generated is closely related to a portion from which a previous concept node was generated, the new concept node can have added to it edges connecting to each of the other nodes, such as user and content nodes, of the previous concept node. Because the connections represented by these new edges are less certain, their magnitudes may be appropriately adjusted, such as by making them smaller than those of the concept node from which they are being copied. This general idea, of copying the fingerprint of one node onto another, with appropriate scaling of the magnitude, is also useful for updating the fingerprints of content nodes and user nodes.

[00241] When a user accesses a particular piece of content, both the fingerprints of the user and of the content can be shifted. To the user's fingerprint is added an (appropriately scaled) copy of the content's fingerprint, and vice versa. The scaling factors used may be the same in some cases, but they may also be chosen differently. Furthermore, the scaling factors may be varied based on the type of interaction between a user and a content item. For example, a user viewing a content item may have a small scaling factor, while providing an insight, discussing an insight in the content, liking an insight in the content, discussing the content as a whole, or sharing the content may each give correspondingly larger scaling factors to be used in adjusting the fingerprints of the user and the content. Similarly, if a user is presented a content item but chooses not to view it, the scaling factor used may be negative, indicating that the user and the content are less alike. In such case, the fingerprints may be copied with a negative factor after confirming that the user has chosen not to view the content; e.g., a predetermined period of time passes, or after the user access other content presented. Thus, as users access content items, the concepts to which their user nodes are associated become more like those to which the content items are associated. And correspondingly, the content items' fingerprints shift to more closely mirror the fingerprints of the users who access them. It will be understood that, because the edges represent bidirectional links, these changes also cause shifts in the fingerprints of the concept nodes.

[00242] Further concept nodes may be generated as representative of a predefined category, such as a category identified by a user in a user profile or elsewhere. Thus, users can be given the option of choosing one or more categories of interest, each associated with a concept node. The system can represent these choices by generating connections between the user and the concept nodes associated with the user's choices.

[00243] The existence of fingerprints for users and content allows the system to efficiently generate recommendations of content with which users are likely to interact. For example, the fingerprints of a user and a content item may be compared (such as by comparing the patterns and magnitudes of edges to which each is connected), and if the degree of overlap exceeds a threshold, the content item may be identified as likely to be of interest. This comparison may be represented as an affinity between the user and the content. Alternatively or additionally, the fingerprints of the user and a plurality of content items may be compared, and one or more of the highest-affinity contents may be chosen for recommendation to the user. Because content items can be given fingerprints before users interact with them, this allows the system to determine good content items to be viewed by the user even if the content has not yet been accessed by the user, or indeed any user. Because unaccessed content can be desirable to present to users, the system may record which content a user has accessed, for example, by generating edges from the user's node to the content's node. Learning journals, as disclosed herein, can be used to efficiently store this information.

[00244] A further feature of the cognitive graph of the system disclosed herein is the ability of the system to identify concepts that are closely associated. For example, the system can determine that two terms are viewed as synonymous by users. If, for example, the cognitive graph had a pair of concept nodes, the first associated with "flammable" and the second with "inflammable," these concept nodes might begin by chance with substantially different fingerprints, despite the fact that they are synonyms. But as users interact with content in a manner that shows that they view the terms interchangeably, the fingerprints of the two content nodes will grow increasingly similar. By comparing the content nodes' fingerprints to each other, the system can assess the degree to which they represent the same idea. If two content nodes become sufficiently similar, the system may even treat them as essentially identical, such as by joining them into a single node or by generating an edge between them representing their connection. Alternatively, the system can simply continue to leave them as separate, but effectively redundant nodes, representing the same basic concept in essentially the same way. By contrast, if users do not treat the terms synonymously— for example, by associating each term with particular, unique circumstances— then each will develop a fingerprint reflecting its term' s unique use, thereby allowing the system to understand and respond to nuances of different concepts, even nuances that no user is consciously aware of. [00245] While new concept nodes can be continually generated, as new items of content representing new ideas keep being added, it may also be desirable to remove concept nodes, so as to allow more efficient computation with the remainder of the nodes in the cognitive graph. One manner in which concept nodes may be selected for deletion is based on an assessment of their fingerprints. If a concept node's fingerprint contains a small number of edges, each with small magnitude, it may be identified as a candidate for deletion.

[00246] On the other hand, nodes having "strong" fingerprints, such as those with many edges of large magnitude, may be interpreted as ideas, users, or content of great importance. For users, a strong fingerprint may indicate an expertise, and strong individual edges may indicate specialized expertise. For content, a strong fingerprint may indicate especially interesting or important content, especially as related to those concepts with the strongest edges. Such content may be preferentially recommended to users. For concepts, strong fingerprints may indicate that the concepts are important ones of significant interest to the community, or to a subset thereof.

Example 7— Enterprise and Prosumers

[00247] Referring to FIG. 17, interactions are shown among the enterprise server, prosumer teams, individuals, prosumer individuals. The enterprise server comprises the enterprise server as described herein. The enterprise server can be configured as described herein to combine the individual knowledge with knowledge of the community and content, to provide one or more of a learning journal, connections and messaging, collections, and insights and questions and answers (herein "Q&A"), within the back end server. The enterprise server can communicate with the prosumer team. The prosumer sub-teams may comprise teams linked to the prosumer team. Each team and sub-team can comprise individual members. Prosumer individuals can be located separately from a prosumer team outside the secure environment and on an opposite side of the semi-permeable membrane from the prosumer team. The influence and engagement of the prosumer individuals with the prosumer team can be shown outside the secure environment.

[00248] The enterprise server comprises software instructions and logic to accommodate the archetypes of a person for different roles of the person and provide information to the user and update insights in response to the archetype of the user. For example, at head of an organization such as, a person may have an archetype Al. Another person who is in charge of the prosumer team may have an archetype A2. Each person in charge of a sub-team may have an archetype A3. The prosumer individuals may have a fourth archetype A4. For example, a person with Archetype Al at work as head of an organization may have an archetype A4 as an individual.

[00249] The system can be configured to transmit and/or broadcast messages to prosumers and individuals. For example, the enterprise may comprise a large organization the serves both prosumers and consumers. The enterprise can be configured to transmit and receive data from both prosumers and consumers. For an organization, the ability to collect data direction from consumers can be quite valuable, in addition to prosumers who serve the consumers. For example, it can be helpful for a health product related company such as pharmaceutical company to have communication with health care provider prosumers and communication with patient consumers. The enterprise server can be configured in many ways to transmit messages to prosumers and consumers, and can be switchable. For example, the system can be configured to transmit health information to prosumers.

However, if the information transmitted to prosumers may not be accurately related to consumers, the system can be configured to directly transmit information to consumers. Also, if information from the consumers may not be adequately relayed from the prosumers to the enterprise, the enterprise may establish direct communication with consumers. Each of the prosumers and consumers can provide and receive insights and other data as described herein, including publication to people logged into the system which may comprise individuals receiving treatment under the care of a physician. This can give the organization the ability to publish information directly to patients, and also to track success and other data of patients receiving therapy.

[00250] The platform can provide security credentials for each of archetypes Al to A4, and the user access with each archetype can be appropriate to the archetype. For example, in the secure enterprise space, the user with archetype Al can be allowed access to data not intended to become public as described herein, such as highly confidential data available only on a need to know basis. As archetype A2 for prosumer team, the security credentials can allow access to each team, for example to each team having archetype A3, but not data exclusively available to archetype Al, for example. At the level of archetype A3 data may not be accessible among the teams, for example, but available within each team. For level A4, the prosumer individual may not have access to all data visible to Archetypes Al to A3, for example. Consumers may have access only to data intended to be publicly available, such as broadcast data transmitted through networks based on login IDs as described herein. Example 8— Methods of Permeable Membrane

[00251] FIG. 18 shows steps of how the permeable membrane logic functions.

[00252] At step 1801, the platform creates a permeable membrane (PM).

[00253] At step 1802, PM configures the accessibility of contents stored in the platform.

[00254] At step 1803, PM allows publicly available contents to be accessed by internal users and external users.

[00255] At step 1804, PM limits the private contents to be accessed by some groups of internal users.

[00256] At step 1805, PM allows contents from public domains to enter the digital space of the platform.

[00257] At step 1806, PM allows publication to public domains of non-confidential private contents.

[00258] At step 1807, PM determines confidential content suitable for release to public as non-confidential content.

[00259] At step 1808, PM acquires specific private contents for publication, e.g. contacts, interaction, influence.

[00260] The use of a permeable membrane logic in conjunction with a recommendation engine and cognitive graph allows the identification of content interesting to users on one side of a permeable membrane based on the activities of users on the other side. For example, behavior of users in a private digital space, such as providing insights and accessing content, can be reflected in a changing pattern in the cognitive graph, which may in turn cause the recommendation of content to a user in a public digital space, or in a second private digital space.

Example 9— Methods of Providing Text Insights

[00261] FIG. 19 shows steps of how to provide text insights.

[00262] At step 1901, a user highlights valuable text components of a content.

[00263] At step 1902, the platform places insight indicator and insight value in a

neighborhood of the insight.

[00264] At step 1903, the platform propagates the insight to other users who have access to the content.

[00265] At step 1904, when a second user views the content, the second user can accept or decline the insight.

[00266] At step 1905, when a second user has not viewed the content or the insight, the platform indicates proposed insight to the second user. [00267] At step 1906, the platform allows the second user to provide additional insights.

[00268] At step 1907, the platform alternatively allows the second user to comment the insight.

[00269] At step 1908, once the second user provides comments, the platform propagates the insight provided by the second user to other users.

Example 10— Methods of Providing Video Insights

[00270] FIG. 20 shows steps of how to provide video insights.

[00271] At step 2001, a user marks starting and ending points on the timeline of a video, and/or adds comments to the marked clips.

[00272] At step 2002, the platform highlights the marked timeline, and places insight indicator and insight value in a neighborhood of the marked timeline.

[00273] At step 2003, the platform propagates the insight to other users who have access to the video, and/or order insight segments based on the insight value.

[00274] At step 2004, when a second user views the video, the second user accepts or declines the insight.

[00275] At step 2005, when a second user has not viewed the video or the insight, the platform indicates proposed insight.

[00276] At step 2006, the platform allows users to rank a plurality of video insight.

[00277] At step 2007, the platform allows the second user to provide comments and/or additional insights.

[00278] At step 2008, the platform propagates the comments and/or the additional insights to other users.

Example 11—Methods of Cognitive Graph

[00279] FIG. 21 shows steps of how cognitive graph logic functions.

[00280] At step 2101, the platform collects contents and information (e.g., actions, properties, profiles) stored pubic domains (e.g., Facebook, Twitter, YouTube).

[00281] At step 2102, the platform collects information associated with contents, actions, properties, profiles stored within the entire platform.

[00282] At step 2103, the platform aggregates all the contents and information, and further analyzes the geolocation, temporal and spatial properties of the contents and information.

[00283] At step 2104, the platform infers graphs of intentions, social networking, and learning activities.

[00284] At step 2105, the platform infers archetypes and conditions.

[00285] At step 2106, the platform predicts desired behaviors. Example 12— Methods of Acknowledging Receiving Contents

[00286] FIG. 22 shows steps of how a user acknowledges receiving

contents/insights/information.

[00287] At step 2201, a first user shares a content/insight with a second user.

[00288] At step 2202, the platform transmits the content/insight and an acknowledgement button (e.g., "Got It!") with the content/insight.

[00289] At step 2203, the second user acknowledges receiving the content/insight by clicking the "Got It" button.

[00290] At step 2204, the platform receives "Got It," and transmits second user's "got it" to all the users as described herein.

Example 13— Methods of Collecting Contents from Social Media Cues

[00291] FIG. 23 shows steps of collecting contents from social media cues.

[00292] At step 2301, the platform analyzes contents in social media networks.

[00293] At step 2302, the platform collects the contents associated with the internal users.

[00294] At step 2303, the platform collects the popular contents in the social media networks.

[00295] At step 2304, the platform collects the highly recommend contents in the social media networks.

[00296] At step 2305, the platform collects the contents in a trending in the social media networks.

[00297] At step 2306, the platform collects the contents written by experts in social media networks.

[00298] At step 2307, the platform collects the contents associated with professional practices in social media networks.

[00299] At step 2308, the platform collects the contents relevant to the knowledge demanded by the internal enterprise.

Example 14— Methods of Portable Identification

[00300] FIG. 24 shows steps of how portable ID works in the platform.

[00301] At step 2401, the platform creates a portable ID for a user.

[00302] At step 2402, the platform links the portable ID to the user's IDs in other public domains (non-limiting examples include Linkedln, Facebook, Twitter, and YouTube) with the privacy setting of the public domains. [00303] At step 2403, the platform gets access to the non-private contents/insights stored in the public domains and collects some of the non-private contents/insights into the platform of the enterprise.

[00304] At step 2404, when the user leaves the enterprise, the non-confidential and publicly available contents can be ported into the public domains, and the user's platform ID may be unlinked from the public domain IDs. Non-confidential data remains associated with public domain IDs

[00305] Although the above steps show method of a collaboration platform in accordance with an example, a person of ordinary skill in the art will recognize many variations based on the teaching described herein. The steps may be completed in a different order. Steps may be added or deleted. Some of the steps may comprise sub-steps. Many of the steps may be repeated as often as if beneficial to the platform.

[00306] COMPUTER GENERATED INSIGHTS

[00307] The computer platform as described herein can be configured to generate insights. One or more of a processor of the server or the processors of user device can be configured with instructions to generate insights. The processor may comprise instructions to search data for insights in response to one or more search parameters. The insight search parameters to identify insights of data may comprise one or more of search strings, word searches, text searches, object searches, video searches, image searches, audio searches, pattern recognition, facial recognition searches, biometric searches, or location searching, for example. The one or more types of data as described herein can be searched for insights with one or more insights as described herein in order to automatically generate insights with the processor system.

[00308] The input search parameters for automatically searching for insights can be configured in one or more of many ways. The input search parameters to identify and generate insights may comprise searches for specific data. For example a search string for an insight may comprise a search to identify and generate insights based on the words used in a string of text. For example, the search string may include a search for the phrase "bottom line" and if a searched text says "the bottom line is x", that text would be identified as an insight (auto-insighted) by the one or more computer processors as described herein. The auto-sighted text can be processed and transmitted to other users similarly to text that has been identified with user input as described herein.

[00309] The processors as described herein can be configured to generate libraries of insights based on user insights, and then automatically search data as described herein for additional insights based on the library of insights, such as the library of insights for a user. For example, each user of a plurality of users may have an insight library, and one or more of the user processor device or the backend server such as the enterprise server can be configured to search for insights corresponding to the insights of the user's library. The processor generated and identified insights can be made available to the user as described herein. The insight libraries of prosumers as described herein can be used to similarly search data and offer insights as described herein. Alternatively or in combination, a community of a plurality of users may have an insight library and data searched and insights identified in response to one or more insights of the plurality of insights contained in the insights library.

[00310] The insights library may comprise one or more insights as described herein, such as one or more of images 1401, news 1402, tables/databases 1403, audios 1404, videos 1405, emails 1406, documents/articles 1407, and presentations 1408. Alternatively or in

combination, the insight of the library may comprise on more of: an insight associated with another person or a group of people (e.g., an existing user, a new user, a potential

collaborator, an employee of the enterprise, an external person to the enterprise, an content creator, an expert, a suspect, a professional, a target, a visitor, an invitee, a family member, an enforcement person, an interviewee, a contractor, a government official, a military soldier, etc), for example. The insight of the library may comprise an insight comprising a combination of insights as described herein.

[00311] Each of the examples as described herein can be combined with one or more other examples. Further, one or more components of one or more examples can be combined with other examples.

[00312] Reference is made to the following claims which recite combinations that are part of the present disclosure, including combinations recited by multiple dependent claims dependent upon multiple dependent claims, which combinations will be understood by a person of ordinary skill in the art and are part of the present disclosure.

[00313] While preferred embodiments and examples of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such

embodiments and examples are provided by way of example only. It is not intended that the invention be limited by the specific examples and embodiments provided within the specification. While the invention has been described with reference to the aforementioned specification, the descriptions and illustrations of the embodiments and examples herein are not meant to be construed in a limiting sense. Numerous variations, changes, and

substitutions will now occur to those skilled in the art without departing from the invention. Furthermore, it shall be understood that all aspects of the invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. It should be understood that various alternatives to the embodiments and examples of the invention described herein may be employed in practicing the invention. It is therefore contemplated that the invention shall also cover any such alternatives, modifications, variations or equivalents. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims

CLAIMS WHAT IS CLAIMED IS:
1. A digital platform to provide recommendations for content comprising: a server comprising a server processor and memory, the memory comprising instructions executable by the processor to cause the platform to at least:
a. create a digital space comprising user profiles for each of a plurality of users and a plurality of contents;
b. collect the plurality of contents;
c. allow the plurality of users to provide insights on one or more accessed contents; d. record learning journals of the plurality of users reflecting access of the plurality of contents by the plurality of users; and
e. generate recommendations of content by:
i. generating a graphical model with a plurality of content nodes, a plurality of user nodes, and a plurality of concept nodes;
ii. assigning each of the plurality of users to a corresponding user node in the plurality of user nodes and each of the plurality of contents to a corresponding content node in plurality of content nodes;
iii. associating the plurality of user nodes with concept nodes by analyzing access behaviors, the insights, the learning journals, and the user profiles;
iv. associating the plurality of content nodes with concept nodes in response to user access of the contents, user insights provided for the contents, and one or more components of the contents;
v. comparing the plurality of contents to the plurality of users; and
vi. recommending one or more unaccessed contents of the plurality of contents to the plurality of users in response to said comparing.
2. The platform of claim 1, wherein comparing the plurality of contents to the plurality of users comprises determining affinities between the plurality of contents and the plurality of users.
3. The platform of claim 2, wherein the affinities are determined by comparing one or more concept nodes associated with the plurality of users with one or more concept nodes associated with the contents.
4. The platform of claim 3, wherein the recommending one or more unaccessed contents comprises comparing the affinities of a plurality of unaccessed contents to each user in the plurality of users, and recommending to each user in the plurality of users an unaccessed content with highest affinity to that user.
5. The platform of claim 1, wherein the association of the one or more components with the one or more concept nodes is determined by a comparison of properties of the component with properties of other components associated with the one or more concept nodes.
6. The platform of claim 1, wherein associating the plurality of contents with concept nodes comprises associating a concept node with each of a video content node, an audio content node, and a text content node.
7. The platform of claim 6, wherein associating the plurality of contents with concept nodes further comprises assigning to a concept node a content node, wherein the content node is assigned to a content that has not yet been accessed by users and has not yet had insights provided for it.
8. The platform of claim 7, wherein the association of the content to the concept node is made by matching a classification of one or more components of the content with a classification associated with the concept node.
9. The platform of claim 1, wherein the one or more components of the contents comprise one or more of a keyword, a sentence, a table, an audio segment, or a video segment.
10. The platform of claim 1, further comprising instructions to recommend one or more contents previously accessed by at least one user.
11. The platform of claim 1, wherein the unaccessed contents have not been accessed by any user of the plurality of users.
12. A digital platform to provide recommendations for content comprising: a server comprising a server processor and memory, the memory comprising instructions executable by the processor to cause the platform to at least:
a. create a digital space comprising user profiles for each of a plurality of users and a plurality of contents;
b. divide the digital space into a private digital space and a public digital space;
c. associate each of the plurality of contents with one or more of the private digital space or the public digital space;
d. determine access rights for the plurality of users with regard to the private digital space;
e. control user access of contents associated with the private digital space in response to the access rights;
f. collect the plurality of contents;
g. allow the plurality of users to provide insights on one or more accessed contents; h. record learning journals of the plurality of users reflecting access of the plurality of contents by the plurality of users; and
i. generate recommendations of content by:
i. generating a graphical model with a plurality of content nodes, a plurality of user nodes, and a plurality of concept nodes;
ii. assigning each of the plurality of users to a corresponding user node in the plurality of user nodes and each of the plurality of contents to a corresponding content node in plurality of content nodes;
iii. associating the plurality of user nodes with concept nodes by analyzing the access behaviors, the insights, the learning journals, and the user profiles;
iv. associating the plurality of content nodes with concept nodes in response to user access of the contents, user insights provided for the contents, and one or more components of the contents;
v. comparing the plurality of contents to the plurality of users; and
vi. recommending one or more contents of the plurality of contents to the plurality of users in response to said comparing.
13. The platform of claim 12, wherein the access behaviors comprise access behaviors in the public digital space and access behaviors in the private digital space.
14. The platform of claim 13, wherein the recommendation of one or more contents comprises recommending one or more contents in the public space in response to access behaviors and insights in the private digital space.
15. The platform of claim 12, wherein the access rights allow all users to access the public digital space, but only allow a portion of the users to access the private digital space.
16. The platform of claim 12, wherein the private digital space comprises a first private digital space and a second private digital space, and wherein the access rights for the plurality of users selectively allow users to access the public digital space as well as one of: the first private digital space, the second private digital space, both private digital spaces, or neither private digital space.
17. The platform of claim 16, wherein the recommendation of one or more contents comprises recommending one or more contents in the second private space in response to access behaviors and insights in the first private digital space.
18. The platform of claim 12, wherein the one or more components of the contents comprise one or more of a keyword, a sentence, a table, an audio segment, or a video segment.
19. The platform of claim 1 or 12, wherein the platform is further configured to create a trending.
20. The platform of claim 1 or 12, wherein the providing insight comprises labeling a component of the plurality of contents.
21. The platform of claim 20, wherein the labeling comprises highlighting, marking, drawing, writing, taking notes, summarizing, and recording.
22. The platform of claim 1 or 12, wherein the user profiles comprise one or more of the following: one or more knowledge domains, one or more expertise skills, one or more preferences, one or more likes, and one or more dislikes.
23. The platform of claim 1 or 12, further comprising instructions to cause the platform to update the user profiles in response to an association of corresponding user nodes to one or more concept nodes.
24. The platform of claim 1 or 12, wherein the associations of content nodes and user nodes with concept nodes comprise magnitudes of association.
25. The platform of claim 24, further comprising instructions to cause the platform to update the magnitudes of association between concept nodes and user nodes and content nodes by: determining that a user assigned to a user node has accessed or provided an insight for content assigned to a content node; increasing an association between the user node and one or more concept nodes associated with the content node in response to the magnitudes of association between the content node and the one or more concept nodes associated with the content node; and increasing an association between the content node and one or more concept nodes associated with the user node in response to the magnitudes of association between the user node and the one or more concept nodes associated with the user node.
26. The platform of claim 25, wherein the amount of increase provided to each magnitude of association is greater if the platform determines that the user provided an insight for the content than if the platform determines that the user accessed the content.
27. The platform of claim 25, further comprising instructions to cause the platform to: determine that a user has not accessed a content associated with a content node that was recommended to the user within a predetermined period of time; and decrease the magnitudes of association between a user node associated with the user and one or more concept nodes associated with the content node in response to the determination that the user has not accessed the content.
28. The platform of claim 27, further comprising instructions to cause the platform to decrease the magnitudes of association between the content node and one or more concept nodes associated with the user node in response to the determination that the user has not accessed the content.
29. The platform of claim 1 or 12, further comprising instructions executable by the processor to cause the platform to provide a summary of a content by: identifying one or more insights provided for the content by one or more users; identifying portions of the content associated with the insights; generating a summary of the content from the identified portions; and providing the summary to a user that has not yet accessed the content.
30. The platform of claim 1 or 12, further configured to prevent users from providing an insight for a content that overlaps with a previously provided insight.
31. The platform of claim 30, further configured to allow users to vote up or down the previously provided insight.
32. The platform of claim 31, further configured to identify the user that originally provided the previously provided insight and display a net voting score for the insight.
33. The platform of claim 32, further configured to determine expertise of users in response to net voting scores for insights originally provided by the users.
34. The platform of claim 33, further configured to determine the expertise of users for each of a plurality of concept nodes associated with the user nodes of the users.
35. The platform of claim 34, wherein the expertise of the user for a concept node is determined in response to a combination of the voting score of insights provided by the user for at least one content and a magnitude of association between the at least one content and the concept node.
36. The platform of claim 1 or 12, further comprising a mobile communication logic configured to communicate with a mobile device.
37. The platform of claim 36, wherein the mobile device includes a mobile processor configured to provide a mobile user with a mobile application, the mobile application comprising a mobile access logic configured to access the plurality of contents.
38. The platform of claim 1 or 12, wherein a processor coupled to a display comprises instructions to display one or more insights identified by other users to a user and for the user to transmit an acknowledgement of the one or more insights identified by the other users.
39. A method of providing recommendations for content comprising: creating a digital space comprising user profiles for each of a plurality of users and a plurality of contents;
collecting the plurality of contents; allowing the plurality of users to provide insights on one or more accessed contents;
recording learning journals of the plurality of users reflecting access of the plurality of contents by the plurality of users; and
generating recommendations of content by:
generating a graphical model with a plurality of content nodes, a plurality of user nodes, and a plurality of concept nodes;
assigning each of the plurality of users to a corresponding user node in the plurality of user nodes and each of the plurality of contents to a corresponding content node in plurality of content nodes;
associating the plurality of user nodes with concept nodes by analyzing access behaviors, the insights, the learning journals, and the user profiles;
associating the plurality of content nodes with concept nodes in response to user access of the contents, user insights provided for the contents, and one or more components of the contents;
comparing the plurality of contents to the plurality of users; and
recommending one or more unaccessed contents of the plurality of contents to the plurality of users in response to said comparing.
40. The method of claim 39, wherein comparing the plurality of contents to the plurality of users comprises determining affinities between the plurality of contents and the plurality of users.
41. The method of claim 40, wherein the affinities are determined by comparing one or more concept nodes associated with the plurality of users with one or more concept nodes associated with the contents.
42. The method of claim 41, wherein the recommending one or more unaccessed contents comprises comparing the affinities of a plurality of unaccessed contents to each user in the plurality of users, and recommending to each user in the plurality of users an unaccessed content with highest affinity to that user.
43. The method of claim 39, wherein the association of the one or more components with the one or more concept nodes is determined by a comparison of properties of the component with properties of other components associated with the one or more concept nodes.
44. The method of claim 39, wherein associating the plurality of contents with concept nodes comprises associating a concept node with each of a video content node, an audio content node, and a text content node.
45. The method of claim 44, wherein associating the plurality of contents with concept nodes further comprises assigning to a concept node a content node, wherein the content node is assigned to a content that has not yet been accessed by users and has not yet had insights provided for it.
46. The method of claim 45, wherein the association of the content to the concept node is made by matching a classification of one or more components of the content with a classification associated with the concept node.
47. The method of claim 39, wherein the one or more components of the contents comprise one or more of a keyword, a sentence, a table, an audio segment, or a video segment.
48. The method of claim 47, further comprising instructions to recommend one or more contents previously accessed by at least one user.
49. The method of claim 47, wherein the unaccessed contents have not been accessed by any user of the plurality of users.
50. A method of providing recommendations for content comprising: creating a digital space comprising user profiles for each of a plurality of users and a plurality of contents;
dividing the digital space into a private digital space and a public digital space;
associating each of the plurality of contents with one or more of the private digital space or the public digital space;
determining access rights for the plurality of users with regard to the private digital space; controlling user access of contents associated with the private digital space in response to the access rights;
collecting the plurality of contents;
allowing the plurality of users to provide insights on one or more accessed contents;
recording learning journals of the plurality of users reflecting access of the plurality of contents by the plurality of users; and generating recommendations of content by:
generating a graphical model with a plurality of content nodes, a plurality of user nodes, and a plurality of concept nodes;
assigning each of the plurality of users to a corresponding user node in the plurality of user nodes and each of the plurality of contents to a corresponding content node in plurality of content nodes;
associating the plurality of user nodes with concept nodes by analyzing the access behaviors, the insights, the learning journals, and the user profiles;
associating the plurality of content nodes with concept nodes in response to user access of the contents, user insights provided for the contents, and one or more components of the contents;
comparing the plurality of contents to the plurality of users; and
recommending one or more contents of the plurality of contents to the plurality of users in response to said comparing.
51. The method of claim 50, wherein the access behaviors comprise access behaviors in the public digital space and access behaviors in the private digital space.
52. The method of claim 51, wherein the recommendation of one or more contents comprises recommending one or more contents in the public space in response to access behaviors and insights in the private digital space.
53. The method of claim 50, wherein the access rights allow all users to access the public digital space, but only allow a portion of the users to access the private digital space.
54. The method of claim 50, wherein the private digital space comprises a first private digital space and a second private digital space, and wherein the access rights for the plurality of users selectively allow users to access the public digital space as well as one of: the first private digital space, the second private digital space, both private digital spaces, or neither private digital space.
55. The method of claim 54, wherein the recommendation of one or more contents comprises recommending one or more contents in the second private space in response to access behaviors and insights in the first private digital space.
56. The method of claim 50, wherein the one or more components of the contents comprise one or more of a keyword, a sentence, a table, an audio segment, or a video segment.
57. The method of claim 39 or 50, wherein the method is further configured to create a trending.
58. The method of claim 39 or 50, wherein the providing insight comprises labeling a component of the plurality of contents.
59. The method of claim 58, wherein the labeling comprises highlighting, marking, drawing, writing, taking notes, summarizing, and recording.
60. The method of claim 39 or 50, wherein the user profiles comprise one or more of the following: one or more knowledge domains, one or more expertise skills, one or more preferences, one or more likes, and one or more dislikes.
61. The method of claim 39 or 50, further comprising instructions to cause the method to update the user profiles in response to an association of corresponding user nodes to one or more concept nodes.
62. The method of claim 39 or 50, wherein the associations of content nodes and user nodes with concept nodes comprise magnitudes of association.
63. The method of claim 62, further comprising instructions to cause the method to update the magnitudes of association between concept nodes and user nodes and content nodes by: determining that a user assigned to a user node has accessed or provided an insight for content assigned to a content node; increasing an association between the user node and one or more concept nodes associated with the content node in response to the magnitudes of association between the content node and the one or more concept nodes associated with the content node; and increasing an association between the content node and one or more concept nodes associated with the user node in response to the magnitudes of association between the user node and the one or more concept nodes associated with the user node.
64. The method of claim 63, wherein the amount of increase provided to each magnitude of association is greater if the method determines that the user provided an insight for the content than if the method determines that the user accessed the content.
65. The method of claim 63, further comprising instructions to cause the method to: determine that a user has not accessed a content associated with a content node that was recommended to the user within a predetermined period of time; and decrease the magnitudes of association between a user node associated with the user and one or more concept nodes associated with the content node in response to the determination that the user has not accessed the content.
66. The method of claim 65, further comprising instructions to cause the method to decrease the magnitudes of association between the content node and one or more concept nodes associated with the user node in response to the determination that the user has not accessed the content.
67. The method of claim 39 or 50, further comprising instructions executable by the processor to cause the method to provide a summary of a content by: identifying one or more insights provided for the content by one or more users; identifying portions of the content associated with the insights; generating a summary of the content from the identified portions; and providing the summary to a user that has not yet accessed the content.
68. The method of claim 39 or 50, further configured to prevent users from providing an insight for a content that overlaps with a previously provided insight.
69. The method of claim 68, further configured to allow users to vote up or down the previously provided insight.
70. The method of claim 69, further configured to identify the user that originally provided the previously provided insight and display a net voting score for the insight.
71. The method of claim 70, further configured to determine expertise of users in response to net voting scores for insights originally provided by the users.
72. The method of claim 71, further configured to determine the expertise of users for each of a plurality of concept nodes associated with the user nodes of the users.
73. The method of claim 72, wherein the expertise of the user for a concept node is determined in response to a combination of the voting score of insights provided by the user for at least one content and a magnitude of association between the at least one content and the concept node.
74. The method of claim 39 or 50, further comprising a mobile communication logic configured to communicate with a mobile device.
75. The method of claim 74, wherein the mobile device includes a mobile processor configured to provide a mobile user with a mobile application, the mobile application comprising a mobile access logic configured to access the plurality of contents.
76. The method of claim 39 or 50, wherein a processor coupled to a display comprises instructions to display one or more insights identified by other users to a user and for the user to transmit an acknowledgement of the one or more insights identified by the other users.
77. A digital collaboration platform comprising:
(a) a server including a server processor and a server operating system configured to provide an enterprise with a server application comprising:
(1) an artificial intelligence logic;
(2) a permeable membrane logic configured to create a digital space, wherein the digital space comprises (i) user profiles of a plurality of users and (ii) a plurality of contents;
(3) a content learning logic configured to:
(i) use the artificial intelligence logic to collect the plurality of contents;
(ii) allow the plurality of users to provide insights on one or more accessed contents;
(iii) record learning journals of the plurality of users after accessing the plurality of contents;
(iv) use the artificial intelligence logic to (i) analyze access behaviors, the insights, the learning journals, and the user profiles and (ii) recommend one or more unaccessed contents to the plurality of users;
(4) a mobile communication logic configured to communicate with a mobile device; (b) the mobile device including a mobile processor configured to provide a mobile user with a mobile application, the mobile application comprising a mobile access logic configured to access the plurality of contents.
78. The platform of claim 77, wherein the artificial intelligence logic is configured to realize one or more artificial intelligence algorithms.
79. The platform of claim 78, wherein the one or more artificial intelligence algorithms comprise a graphical model.
80. The platform of claim 77, wherein the digital space is created within the enterprise.
81. The platform of claim 77, wherein the permeable membrane logic is further configured to configure content accessibility of the digital space.
82. The platform of claim 77, wherein the permeable membrane logic is further configured to configure security of the digital space.
83. The platform of claim 77, wherein the user profiles comprise one or more of the following: one or more knowledge domains, one or more expertise skills, one or more preferences, one or more likes, and one or more dislikes.
84. The platform of claim 77, wherein the plurality of contents comprise one or more of the following: one or more images, one or more video files, one or more audio files, one or more articles, one or more spreadsheets, and one or more RSS feeds.
85. The platform of claim 77, wherein the content learning logic is further configured to tag a section of a content.
86. The platform of claim 77, wherein the content learning logic is further configured to create personal libraries of the plurality of users.
87. The platform of claim 77, wherein the collecting the plurality of contents comprises collecting one or more contents from a plurality of public domains.
88. The platform of claim 87, wherein the public domains comprise one or more of the following: one or more websites and one or more repositories.
89. The platform of claim 77, wherein the collecting the plurality of contents comprises given one or more contents from a platform administrator.
90. The platform of claim 77, wherein the accessing the plurality of contents comprises one or more of: viewing, reading, watching, and listening.
91. The platform of claim 77, wherein the providing insight comprises label one or more components of the plurality of contents.
92. The platform of claim 91, wherein the labeling comprises highlighting, marking, drawing, writing, taking notes, summarizing, and recording.
93. The platform of claim 91, wherein the labeling is based on texts or speaking.
94. The platform of claim 91, wherein the one or more components comprise one or more of the following: one or more keywords, one or more tables, one or more audio segments, and one or more video segments.
95. The platform of claim 77, wherein the content learning logic is further configured to use the artificial intelligence logic to create a trending.
96. The platform of claim 95, wherein the trending is associated with a social event.
97. The platform of claim 95, wherein the trending is associated with knowledge.
98. The platform of claim 95, wherein the trending is associated with the user profiles.
99. The platform of claim 95, wherein the trending is associated with the access behaviors.
100. The platform of claim 77, wherein the learning journal comprises one or more records of learning the plurality of the contents.
101. The platform of claim 100, wherein the learning the plurality of the contents comprises discussing one or more contents by a first user with a second user.
102. The platform of claim 100, wherein the learning the plurality of the contents comprises asking one or more questions.
103. The platform of claim 100, wherein the learning the plurality of the contents comprises answering one or more questions.
104. The platform of claim 100, wherein the learning the plurality of the contents comprises providing one or more comments.
105. The platform of claim 100, wherein the learning the plurality of the contents comprises recommending one or more contents.
106. The platform of claim 100, wherein the learning the plurality of the contents comprises contributing one or more contents to the plurality of contents.
107. The platform of claim 100, wherein the learning the plurality of the contents comprises influencing one or more other users.
108. The platform of claim 100, wherein the learning journal further comprises association between the user profiles and the one or more records of learning the plurality of the contents.
109. The platform of claim 77, wherein the server application further comprises a social networking logic.
110. The platform of claim 109, wherein the social networking logic is configured to analyze access behaviors, the insights, the learning journals, and the user profiles.
111. The platform of claim 109, wherein the social networking logic is configured to use the analysis to create social networks for the plurality of users.
112. The platform of claim 77, wherein the mobile communication logic is configured to allow the plurality of users to access the plurality of contents on mobile devices.
113. The platform of claim 77, wherein the mobile communication logic is configured to send push notifications of recommended contents to the plurality of users on mobile devices.
114. The platform of claim 77, wherein the mobile access logic is configured to receive one or more push notifications from the server application.
115. The platform of claim 77, wherein the mobile access logic is configured to access the digital space by the mobile user.
116. A computer-implemented platform for improving digital collaboration comprising:
(a) an artificial intelligence logic;
(b) a permeable membrane logic configured to create a digital space, wherein the digital space comprises (1) user profiles of a plurality of users and (2) a plurality of contents;
(c) a content learning logic configured to:
(1) use the artificial intelligence logic to collect the plurality of contents;
(2) allow the plurality of users to access the plurality of contents;
(3) allow the plurality of users to provide insights on one or more accessed contents;
(4) record learning journals of the plurality of users after accessing the plurality of contents;
(5) use the artificial intelligence logic to (i) analyze access behaviors, the insights, the learning journals, and the user profiles and (ii) recommend one or more unaccessed contents to the plurality of users;
117. The platform of claim 116, wherein the artificial intelligence logic is configured to realize one or more artificial intelligence algorithms.
118. The platform of claim 117, wherein the one or more artificial intelligence algorithms comprise a graphical model.
119. The platform of claim 116, wherein the digital space is created within an enterprise.
120. The platform of claim 116, wherein the permeable membrane logic is further configured to configure content accessibility of the digital space.
121. The platform of claim 116, wherein the permeable membrane logic is further configured to configure security of the digital space.
122. The platform of claim 116, wherein the user profiles comprise one or more of the following: one or more knowledge domains, one or more expertise skills, one or more preferences, one or more likes, and one or more dislikes.
123. The platform of claim 116, wherein the plurality of contents comprise one or more of the following: one or more images, one or more video files, one or more audio files, one or more articles, one or more spreadsheets, and one or more RSS feeds.
124. The platform of claim 116, wherein the content learning logic is further configured to tag a section of a content.
125. The platform of claim 116, wherein the content learning logic is further configured to create personal libraries of the plurality of users.
126. The platform of claim 116, wherein the collecting the plurality of contents comprises collecting one or more contents from a plurality of public domains.
127. The platform of claim 126, wherein the public domains comprise one or more of the following: one or more websites and one or more repositories.
128. The platform of claim 116, wherein the collecting the plurality of contents comprises given one or more contents from a platform administrator.
129. The platform of claim 116, wherein the accessing the plurality of contents comprises one or more of: viewing, reading, watching, and listening.
130. The platform of claim 116, wherein the providing insight comprises label one or more components of the plurality of contents.
131. The platform of claim 130, wherein the labeling comprises highlighting, marking, drawing, writing, taking notes, summarizing, and recording.
132. The platform of claim 130, wherein the labeling is based on texts or speaking.
133. The platform of claim 130, wherein the one or more components comprise one or more of the following: one or more keywords, one or more tables, one or more audio segments, and one or more video segments.
134. The platform of claim 116, wherein the content learning logic is further configured to use the artificial intelligence logic to create a trending.
135. The platform of claim 134, wherein the trending is associated with a social event.
136. The platform of claim 134, wherein the trending is associated with knowledge.
137. The platform of claim 134, wherein the trending is associated with the user profiles.
138. The platform of claim 134, wherein the trending is associated with the access behaviors.
139. The platform of claim 116, wherein the learning journal comprises one or more records of learning the plurality of the contents.
140. The platform of claim 139, wherein the learning the plurality of the contents comprises discussing one or more contents by a first user with a second user.
141. The platform of claim 139, wherein the learning the plurality of the contents comprises asking one or more questions.
142. The platform of claim 139, wherein the learning the plurality of the contents comprises answering one or more questions.
143. The platform of claim 139, wherein the learning the plurality of the contents comprises providing one or more comments.
144. The platform of claim 139, wherein the learning the plurality of the contents comprises recommending one or more contents.
145. The platform of claim 139, wherein the learning the plurality of the contents comprises contributing one or more contents to the plurality of contents.
146. The platform of claim 139, wherein the learning the plurality of the contents comprises influencing one or more other users.
147. The platform of claim 139, wherein the learning journal further comprises association between the user profiles and the one or more records of learning the plurality of the contents.
148. The platform of claim 116, further comprising a social networking logic.
149. The platform of claim 148, wherein the social networking logic is configured to analyze access behaviors, the insights, the learning journals, and the user profiles.
150. The platform of claim 148, wherein the social networking logic is configured to use the analysis to create social networks for the plurality of users.
151. The platform of claim 116, further comprising a mobile communication logic.
152. The platform of claim 151, wherein the mobile communication logic is configured to allow the plurality of users to access the plurality of contents on mobile devices.
153. The platform of claim 151, wherein the mobile communication logic is configured to send push notifications of recommended contents to the plurality of users on mobile devices.
154. A computer- implemented platform for improving digital collaborating and learning comprising: a digital processing device comprising an operating system configured to perform executable instructions and a memory device; and a computer program including instructions executable by the digital processing device to create an enterprise application comprising:
(a) a software module configured to create one or more contents using an artificial intelligence engine;
(b) a software module configured to let a first user learn the one or more contents and to record a learning journal of the first user; and
(c) a software module configured to create a network of the first user using the artificial intelligence engine, the network is derived based on the learning journal.
155. The platform of claim 154, wherein the one or more contents comprise an image.
156. The platform of claim 154, wherein the one or more contents comprise a video.
157. The platform of claim 154, wherein the one or more contents comprise an audio.
158. The platform of claim 154, wherein the one or more contents comprise an article.
159. The platform of claim 154, wherein the one or more contents comprise an RSS feed.
160. The platform of claim 154, wherein the one or more contents comprise tagging a section of an image.
161. The platform of claim 154, wherein the one or more contents comprise tagging a section of a video.
162. The platform of claim 154, wherein the one or more contents comprise tagging a section of an audio.
163. The platform of claim 154, wherein the one or more contents comprise tagging a section of an article.
164. The platform of claim 154, wherein the creating the one or more contents comprises collecting the one or more contents from the Internet.
165. The platform of claim 164, wherein the creating the one or more contents comprises using the artificial intelligence engine to crawl a plurality of websites or repositories.
166. The platform of claim 154, wherein the creating the one or more contents comprises collecting a contributed content from the first user.
167. The platform of claim 154, wherein the creating the one or more contents comprises collecting a contributed content from a second user.
168. The platform of claim 154, wherein the creating the one or more contents comprises using the artificial intelligence engine to identify a topic of the one or more contents.
169. The platform of claim 154, wherein the creating the one or more contents comprises using the artificial intelligence engine to identify a trend of the one or more contents.
170. The platform of claim 154, wherein the creating the one or more contents comprises using the artificial intelligence engine to evaluate relevance of the one or more contents with respect to the first user.
171. The platform of claim 154, wherein the artificial intelligence engine comprises one or more artificial intelligence algorithms.
172. The platform of claim 171, wherein the one or more artificial intelligence algorithms comprise a graphical model.
173. The platform of claim 154, wherein the learning journal comprises accessing the one or more contents.
174. The platform of claim 154, wherein the learning journal comprises viewing, reading, watching, or hearing the one or more contents.
175. The platform of claim 154, wherein the learning journal comprises discussing the one or more contents with a second user.
176. The platform of claim 154, wherein the learning journal comprises asking a question regarding the one or more contents.
177. The platform of claim 154, wherein the learning journal comprises providing a comment to the one or more contents.
178. The platform of claim 154, wherein the learning journal comprises providing an insight to the one or more contents.
179. The platform of claim 154, wherein the learning journal comprises recommending the one or more contents.
180. The platform of claim 154, wherein the learning journal comprises contributing a content to the one or more contents.
181. The platform of claim 154, wherein the learning journal comprises a learning behavior.
182. The platform of claim 154, wherein the learning journal comprises a preference.
183. The platform of claim 154, wherein the learning journal comprises an interest.
184. The platform of claim 154, further comprising a mobile device.
185. The platform of claim 184, wherein the mobile device is configured to allow the first user to access the enterprise application via the mobile device.
186. The platform of claim 184, wherein the enterprise application comprises a software module configured to send the mobile device a push notification.
187. The platform of claim 186, wherein the push notification comprises.
188. The platform of claim 154, wherein the enterprise application comprises a software module configured to store the one or more contents in a database.
189. The platform of claim 154, wherein the enterprise application comprises a software module configured to create a community using the artificial intelligence engine.
190. The platform of claim 154, wherein the enterprise application comprises a software module configured to recommend the first user to join a community based on a community analysis performed by the artificial intelligence engine.
191. The platform of claim 190, wherein the community analysis comprises analyzing an interest of the first user with the community.
192. The platform of claim 154, wherein the enterprise application comprises a software module configured to allow the first user to message a second user.
193. A digital collaboration platform comprising: A digital display;
A digital processor coupled to the digital display and configured to create cognitive graph logic.
194. The platform in claim 193, wherein the creating the cognitive graph logic comprises automatically inferring one or more properties associated with one or more user actions.
195. The platform in claim 193, wherein the creating the cognitive graph logic comprises inferring one or more properties able to predict a future action.
196. The platform in claim 193, wherein the creating the cognitive graph logic comprises inferring one or more user actions able to predict one or more future actions.
197. The platform in claim 193, wherein the creating the cognitive graph logic comprises preconditioning one or more properties associated with one or more user actions or with one or more future actions.
198. A digital collaboration platform comprising: A digital display;
A digital processor coupled to the digital display and configured to create permeable membrane logic.
199. The platform of claim 198, wherein the permeable membrane logic is configured to control accessibility of contents.
200. The platform of claim 198, wherein the permeable membrane logic is configured to sift and allow external contents entering the platform.
201. The platform of claim 198, wherein the permeable membrane logic is configured to control internal contents sharing out of the platform.
202. The platform of claim 198, wherein the permeable membrane logic is configured to allow internal contents to be publicly available.
203. The platform of claim 198, wherein the permeable membrane logic is configured to limit access of internal contents to one or more predefined people.
204. A digital collaboration platform comprising: A digital display;
A digital processor coupled to the digital display and configured to create a social networking logic.
205. The platform of claim 204, wherein the social networking logic is configured to collect highly recommended contents from a social network.
206. The platform of claim 204, wherein the social networking logic is configured to collect popular contents from a social network.
207. The platform of claim 204, wherein the social networking logic is configured to collect contents in a rising trending from a social network.
208. The platform of claim 204, wherein the social networking logic is configured to collect classified contents from a social network.
209. A digital collaboration platform comprising: A digital display;
A digital processor coupled to the digital display and configured to create a portable identification.
210. The platform of claim 209, wherein the portable identification is linked to a social media identification.
211. The platform of claim 209, wherein the portable identification is used to access contents stored in the platform. The platform of claim 209, wherein the portable identification is linked to a social media identification.
212. The platform of claim 209, wherein the portable identification comprises an identification of a user and a profile of the user.
213. The platform of claim 209, wherein the portable identification comprises a user profile in response to influence of the user in a confidential data space.
214. A social learning platform comprising: a processor system configured with instructions to determine one or more intentions of a user in response to one or more user inputs.
215. The social/collaborative learning platform of claim 214, wherein the processor system comprises instructions to display a cognitive map to a user in response to the one or more intentions.
216. The social/collaborative learning platform of claim 214, wherein the processor system comprises instructions to provide a permeable membrane software layer.
217. The social/collaborative learning platform of claim 214, wherein the processor system comprises instructions to determine a plurality of intentions of a first user and a plurality of intentions of a second user and to identify one or more common intentions of the first user and the second user and promote communication between the first user and the second user in response to the one or more common intentions.
218. The social/collaborative learning platform of claim 214, wherein the processor system comprises instructions to adjust information shown on a display in response to the one or more intentions of the user.
219. A device comprising: a processor configured with instructions to generate a got it acknowledgement.
220. A device comprising: a processor configured with instructions to generate a permeable data space.
221. A device comprising: a processor configured with instructions to store collections from social media
222. A device comprising: a processor configured with instructions to generate a portable identification.
223. The device of claim 222, wherein the portable identification identifies influence of a user from a confidential environment.
224. A device comprising: a display; and a processor coupled to the display, the processor configured with instructions to provide a cognitive graph to a user.
225. A device comprising: a display; and a processor coupled to the display, the processor configured with instructions to provide a collections of a social media.
226. A mobile device comprising: a mobile processor configured to provide a mobile user with a mobile application, the mobile application comprising a mobile access logic configured to access a plurality of contents.
227. A platform or device as in any one of the preceding claims, wherein a processor coupled to a display comprises instructions to configure a user interface for a user to identify insights for text, and instructions to display insights for text of other users.
228. A platform or device as in any one of the preceding claims, wherein a processor coupled to a display comprises instructions to configure a user interface for a user to identify insights for video, and instructions to display insights for video of other users.
229. A platform or device as in any one of the preceding claims, wherein a processor coupled to a display comprises instructions to display a cognitive graph to a user, the cognitive graph determined in response to one or more insights identified by the user.
230. A platform or device as in any one of the preceding claims, wherein a processor coupled to a display comprises instructions to display one or more insights identified by other users to a user and for the user to transmit an acknowledgement of the one or more insights identified by the other users.
231. A platform or device as in any one of the preceding claims, wherein a processor coupled to a display comprises instructions to display one or more insights identified by other users to a user and for the user to transmit an acknowledgement of the one or more insights identified by the other users and wherein the acknowledgement comprises one a statement comprising one or more of "Got It", "Get It", "Declared It" "Declare It".
232. A platform or device as in any one of the preceding claims, wherein a processor coupled to a display comprises instructions to received data from a permeable space of a back end server
233. A platform or device as in any one of the preceding claims, wherein a processor coupled to a display comprises instructions to store collections from one or more social media cues.
234. A platform or device as in any one of the preceding claims, wherein a processor coupled to a display comprises instructions to receive a portable ID from a permeable space, the portable ID comprising an identification of a user and a profile of the user, the profile determined in response to influence of the user in a confidential data space.
235. A method, the method comprising providing a social learning apparatus as in any one of the preceding claims.
236. A digital collaboration device to improve the effectiveness of computer-implemented collaboration, the device comprising: a display; and a processor coupled to the display, the processor configured with instructions to provide a cognitive graph to a user.
237. A digital collaboration device to improve the effectiveness of computer- implemented collaboration, the device comprising: a display; and a processor coupled to the display, the processor configured with instructions to provide a collections of a social media.
238. A digital collaboration mobile device to improve the effectiveness of computer- implemented collaboration, the mobile device comprising: a mobile processor configured to provide a mobile user with a mobile application, the mobile application comprising a mobile access logic configured to access a plurality of contents.
239. A digital collaboration device to improve the effectiveness of computer-implemented collaboration, the device comprising: a processor configured with instructions to generate a got it acknowledgement.
240. A digital collaboration device to improve the effectiveness of computer-implemented collaboration, the device comprising: a processor configured with instructions to generate a permeable data space.
241. A digital collaboration device to improve the effectiveness of computer- implemented collaboration, the device comprising: a processor configured with instructions to store collections from social media
242. A digital collaboration device to improve the effectiveness of computer-implemented collaboration, the device comprising: a processor configured with instructions to generate a portable identification.
243. A computer-implemented method to improve the effectiveness of digital
collaboration, the method comprising providing a social learning apparatus as in any one of the preceding claims.
244. A digital collaboration platform comprising: a processor system configured with instructions to determine one or more intentions of a user in response to one or more user inputs.
245. A digital collaboration platform comprising:
(a) a server including a server processor and a server operating system configured to provide an enterprise with a server application comprising:
(1) an artificial intelligence logic;
(2) a permeable membrane logic configured to create a digital space, wherein the digital space comprises (i) user profiles of a plurality of users and (ii) a plurality of contents;
(3) a content learning logic configured to:
(i) use the artificial intelligence logic to collect the plurality of contents;
(ii) allow the plurality of users to provide insights on one or more accessed contents;
(iii) record learning journals of the plurality of users after accessing the plurality of contents;
(iv) use the artificial intelligence logic to (i) analyze access behaviors, the insights, the learning journals, and the user profiles and (ii) recommend one or more unaccessed contents to the plurality of users;
(4) a mobile communication logic configured to communicate with a mobile device;
(b) the mobile device including a mobile processor configured to provide a mobile user with a mobile application, the mobile application comprising a mobile access logic configured to access the plurality of contents.
246. A computer-implemented collaboration platform comprising:
(a) an artificial intelligence logic;
(b) a permeable membrane logic configured to create a digital space, wherein the digital space comprises (1) user profiles of a plurality of users and (2) a plurality of contents;
(c) a content learning logic configured to:
(1) use the artificial intelligence logic to collect the plurality of contents; (2) allow the plurality of users to access the plurality of contents;
(3) allow the plurality of users to provide insights on one or more accessed contents;
(4) record learning journals of the plurality of users after accessing the plurality of contents;
(5) use the artificial intelligence logic to (i) analyze access behaviors, the insights, the learning journals, and the user profiles and (ii) recommend one or more unaccessed contents to the plurality of users;
247. A computer-implemented collaboration platform comprising: a digital processing device comprising an operating system configured to perform executable instructions and a memory device; and a computer program including instructions executable by the digital processing device to create an enterprise application comprising:
(a) a software module configured to create one or more contents using an artificial intelligence engine;
(b) a software module configured to let a first user learn the one or more contents and to record a learning journal of the first user; and
(c) a software module configured to create a network of the first user using the artificial intelligence engine, the network is derived based on the learning journal.
248. A digital collaboration platform or device as in any one of the preceding claims, wherein a processor coupled to a display comprises instructions to received data from a permeable space of a back end server
249. A digital collaboration platform or device as in any one of the preceding claims, wherein a processor coupled to a display comprises instructions to store collections from one or more social media cues.
250. A digital collaboration platform or device as in any one of the preceding claims, wherein a processor coupled to a display comprises instructions to receive a portable ID from a permeable space, the portable ID comprising an identification of a user and a profile of the user, the profile determined in response to influence of the user in a confidential data space.
251. A digital collaboration platform comprising a server including a server processor and a server operating system configured to provide an enterprise with a server application comprising an artificial intelligence logic.
252. A digital collaboration platform comprising a server including a server processor and a server operating system configured to provide an enterprise with a server application comprising a permeable membrane logic configured to create a digital space, wherein the digital space comprises (i) user profiles of a plurality of users and (ii) a plurality of contents.
253. A digital collaboration platform comprising a server including a server processor and a server operating system configured to provide an enterprise with a server application comprising a content learning logic.
254. A digital collaboration platform or device as in any one of the preceding claims, wherein the content learning logic is configured to use the artificial intelligence logic to collect the plurality of contents.
255. A digital collaboration platform or device as in any one of the preceding claims, wherein the content learning logic is configured to allow the plurality of users to provide insights on one or more accessed contents.
256. A digital collaboration platform or device as in any one of the preceding claims, wherein the content learning logic is configured to record learning journals of the plurality of users after accessing the plurality of contents.
257. A digital collaboration platform or device as in any one of the preceding claims, wherein the content learning logic is configured to use the artificial intelligence logic to (i) analyze access behaviors, the insights, the learning journals, and the user profiles and (ii) recommend one or more unaccessed contents to the plurality of users.
258. A digital collaboration platform or device as in any one of the preceding claims comprising a server including a server processor and a server operating system configured to provide an enterprise with a server application comprising a mobile communication logic configured to communicate with a mobile device.
259. A digital collaboration platform or device as in any one of the preceding claims comprising a mobile device including a mobile processor configured to provide a mobile user with a mobile application, the mobile application comprising a mobile access logic configured to access the plurality of contents.
260. The platform or device as in any one of the preceding claims, wherein the artificial intelligence logic is configured to realize one or more artificial intelligence algorithms.
261. The platform or device as in any one of the preceding claims, wherein the one or more artificial intelligence algorithms comprise a graphical model.
262. The platform or device as in any one of the preceding claims, wherein the digital space is created within the enterprise.
263. The platform or device as in any one of the preceding claims, wherein the permeable membrane logic is further configured to configure content accessibility of the digital space.
264. The platform or device as in any one of the preceding claims, wherein the permeable membrane logic is further configured to configure security of the digital space.
265. The platform or device as in any one of the preceding claims, wherein the user profiles comprise one or more of the following: one or more knowledge domains, one or more expertise skills, one or more preferences, one or more likes, and one or more dislikes.
266. The platform or device as in any one of the preceding claims, wherein the plurality of contents comprise one or more of the following: one or more images, one or more video files, one or more audio files, one or more articles, one or more spreadsheets, and one or more RSS feeds.
267. The platform or device as in any one of the preceding claims, wherein the content learning logic is further configured to tag a section of a content.
268. The platform or device as in any one of the preceding claims, wherein the content learning logic is further configured to create personal libraries of the plurality of users.
269. The platform or device as in any one of the preceding claims, wherein the collecting the plurality of contents comprises collecting one or more contents from a plurality of public domains.
270. The platform or device as in any one of the preceding claims, wherein the public domains comprise one or more of the following: one or more websites and one or more repositories.
271. The platform or device as in any one of the preceding claims, wherein the collecting the plurality of contents comprises given one or more contents from a platform administrator.
272. The platform or device as in any one of the preceding claims, wherein the accessing the plurality of contents comprises one or more of: viewing, reading, watching, and listening.
273. The platform or device as in any one of the preceding claims, wherein the providing insight comprises label one or more components of the plurality of contents.
274. The platform or device as in any one of the preceding claims, wherein the labeling comprises highlighting, marking, drawing, writing, taking notes, summarizing, and recording.
275. The platform or device as in any one of the preceding claims, wherein the labeling is based on texts or speaking.
276. The platform or device as in any one of the preceding claims, wherein the one or more components comprise one or more of the following: one or more keywords, one or more tables, one or more audio segments, and one or more video segments.
277. The platform or device as in any one of the preceding claims, wherein the content learning logic is further configured to use the artificial intelligence logic to create a trending.
278. The platform or device as in any one of the preceding claims, wherein the trending is associated with a social event.
279. The platform or device as in any one of the preceding claims, wherein the trending is associated with knowledge.
280. The platform or device as in any one of the preceding claims, wherein the trending is associated with the user profiles.
281. The platform or device as in any one of the preceding claims, wherein the trending is associated with the access behaviors.
282. The platform or device as in any one of the preceding claims, wherein the learning journal comprises one or more records of learning the plurality of the contents.
283. The platform or device as in any one of the preceding claims, wherein the learning the plurality of the contents comprises discussing one or more contents by a first user with a second user.
284. The platform or device as in any one of the preceding claims, wherein the learning the plurality of the contents comprises asking one or more questions.
285. The platform or device as in any one of the preceding claims, wherein the learning the plurality of the contents comprises answering one or more questions.
286. The platform or device as in any one of the preceding claims, wherein the learning the plurality of the contents comprises providing one or more comments.
287. The platform or device as in any one of the preceding claims, wherein the learning the plurality of the contents comprises recommending one or more contents.
288. The platform or device as in any one of the preceding claims, wherein the learning the plurality of the contents comprises contributing one or more contents to the plurality of contents.
289. The platform or device as in any one of the preceding claims, wherein the learning the plurality of the contents comprises influencing one or more other users.
290. The platform or device as in any one of the preceding claims, wherein the learning journal further comprises association between the user profiles and the one or more records of learning the plurality of the contents.
291. The platform or device as in any one of the preceding claims, wherein the server application further comprises a social networking logic.
292. The platform or device as in any one of the preceding claims, wherein the social networking logic is configured to analyze access behaviors, the insights, the learning journals, and the user profiles.
293. The platform or device as in any one of the preceding claims, wherein the social networking logic is configured to use the analysis to create social networks for the plurality of users.
294. The platform or device as in any one of the preceding claims, wherein the mobile communication logic is configured to allow the plurality of users to access the plurality of contents on mobile devices.
295. The platform or device as in any one of the preceding claims, wherein the mobile communication logic is configured to send push notifications of recommended contents to the plurality of users on mobile devices.
296. The platform or device as in any one of the preceding claims, wherein the mobile access logic is configured to receive one or more push notifications from the server application.
297. The platform or device as in any one of the preceding claims, wherein the mobile access logic is configured to access the digital space by the mobile user.
298. The platform or device as in any one of the preceding claims, wherein the artificial intelligence logic is configured to realize one or more artificial intelligence algorithms.
299. The platform or device as in any one of the preceding claims, wherein the one or more artificial intelligence algorithms comprise a graphical model.
300. The platform or device as in any one of the preceding claims, wherein the digital space is created within an enterprise.
301. The platform or device as in any one of the preceding claims, wherein the permeable membrane logic is further configured to configure content accessibility of the digital space.
302. The platform or device as in any one of the preceding claims, wherein the permeable membrane logic is further configured to configure security of the digital space.
303. The platform or device as in any one of the preceding claims, wherein the user profiles comprise one or more of the following: one or more knowledge domains, one or more expertise skills, one or more preferences, one or more likes, and one or more dislikes.
304. The platform or device as in any one of the preceding claims, wherein the plurality of contents comprise one or more of the following: one or more images, one or more video files, one or more audio files, one or more articles, one or more spreadsheets, and one or more RSS feeds.
305. The platform or device as in any one of the preceding claims, wherein the content learning logic is further configured to tag a section of a content.
306. The platform or device as in any one of the preceding claims, wherein the content learning logic is further configured to create personal libraries of the plurality of users.
307. The platform or device as in any one of the preceding claims, wherein the collecting the plurality of contents comprises collecting one or more contents from a plurality of public domains.
308. The platform or device as in any one of the preceding claims, wherein the public domains comprise one or more of the following: one or more websites and one or more repositories.
309. The platform or device as in any one of the preceding claims, wherein the collecting the plurality of contents comprises given one or more contents from a platform administrator.
310. The platform or device as in any one of the preceding claims, wherein the accessing the plurality of contents comprises one or more of: viewing, reading, watching, and listening.
311. The platform or device as in any one of the preceding claims, wherein the providing insight comprises label one or more components of the plurality of contents.
312. The platform or device as in any one of the preceding claims, wherein the labeling comprises highlighting, marking, drawing, writing, taking notes, summarizing, and recording.
313. The platform or device as in any one of the preceding claims, wherein the labeling is based on texts or speaking.
314. The platform or device as in any one of the preceding claims, wherein the one or more components comprise one or more of the following: one or more keywords, one or more tables, one or more audio segments, and one or more video segments.
315. The platform or device as in any one of the preceding claims, wherein the content learning logic is further configured to use the artificial intelligence logic to create a trending.
316. The platform or device as in any one of the preceding claims, wherein the trending is associated with a social event.
317. The platform or device as in any one of the preceding claims, wherein the trending is associated with knowledge.
318. The platform or device as in any one of the preceding claims, wherein the trending is associated with the user profiles.
319. The platform or device as in any one of the preceding claims, wherein the trending is associated with the access behaviors.
320. The platform or device as in any one of the preceding claims, wherein the learning journal comprises one or more records of learning the plurality of the contents.
321. The platform or device as in any one of the preceding claims, wherein the learning the plurality of the contents comprises discussing one or more contents by a first user with a second user.
322. The platform or device as in any one of the preceding claims, wherein the learning the plurality of the contents comprises asking one or more questions.
323. The platform or device as in any one of the preceding claims, wherein the learning the plurality of the contents comprises answering one or more questions.
324. The platform or device as in any one of the preceding claims, wherein the learning the plurality of the contents comprises providing one or more comments.
325. The platform or device as in any one of the preceding claims, wherein the learning the plurality of the contents comprises recommending one or more contents.
326. The platform or device as in any one of the preceding claims, wherein the learning the plurality of the contents comprises contributing one or more contents to the plurality of contents.
327. The platform or device as in any one of the preceding claims, wherein the learning the plurality of the contents comprises influencing one or more other users.
328. The platform or device as in any one of the preceding claims, wherein the learning journal further comprises association between the user profiles and the one or more records of learning the plurality of the contents.
329. The platform or device as in any one of the preceding claims, further comprising a social networking logic.
330. The platform or device as in any one of the preceding claims, wherein the social networking logic is configured to analyze access behaviors, the insights, the learning journals, and the user profiles.
331. The platform or device as in any one of the preceding claims, wherein the social networking logic is configured to use the analysis to create social networks for the plurality of users.
332. The platform or device as in any one of the preceding claims, further comprising a mobile communication logic.
333. The platform or device as in any one of the preceding claims, wherein the mobile communication logic is configured to allow the plurality of users to access the plurality of contents on mobile devices.
334. The platform or device as in any one of the preceding claims, wherein the mobile communication logic is configured to send push notifications of recommended contents to the plurality of users on mobile devices.
335. The platform or device as in any one of the preceding claims, wherein the one or more contents comprise an image.
336. The platform or device as in any one of the preceding claims, wherein the one or more contents comprise a video.
337. The platform or device as in any one of the preceding claims, wherein the one or more contents comprise an audio.
338. The platform or device as in any one of the preceding claims, wherein the one or more contents comprise an article.
339. The platform or device as in any one of the preceding claims, wherein the one or more contents comprise an RSS feed.
340. The platform or device as in any one of the preceding claims, wherein the one or more contents comprise tagging a section of an image.
341. The platform or device as in any one of the preceding claims, wherein the one or more contents comprise tagging a section of a video.
342. The platform or device as in any one of the preceding claims, wherein the one or more contents comprise tagging a section of an audio.
343. The platform or device as in any one of the preceding claims, wherein the one or more contents comprise tagging a section of an article.
344. The platform or device as in any one of the preceding claims, wherein the creating the one or more contents comprises collecting the one or more contents from the Internet.
345. The platform or device as in any one of the preceding claims, wherein the creating the one or more contents comprises using the artificial intelligence engine to crawl a plurality of websites or repositories.
346. The platform or device as in any one of the preceding claims, wherein the creating the one or more contents comprises collecting a contributed content from the first user.
347. The platform or device as in any one of the preceding claims, wherein the creating the one or more contents comprises collecting a contributed content from a second user.
348. The platform or device as in any one of the preceding claims, wherein the creating the one or more contents comprises using the artificial intelligence engine to identify a topic of the one or more contents.
349. The platform or device as in any one of the preceding claims, wherein the creating the one or more contents comprises using the artificial intelligence engine to identify a trend of the one or more contents.
350. The platform or device as in any one of the preceding claims, wherein the creating the one or more contents comprises using the artificial intelligence engine to evaluate relevance of the one or more contents with respect to the first user.
351. The platform or device as in any one of the preceding claims, wherein the artificial intelligence engine comprises one or more artificial intelligence algorithms.
352. The platform or device as in any one of the preceding claims, wherein the one or more artificial intelligence algorithms comprise a graphical model.
353. The platform or device as in any one of the preceding claims, wherein the learning journal comprises accessing the one or more contents.
354. The platform or device as in any one of the preceding claims, wherein the learning journal comprises viewing, reading, watching, or hearing the one or more contents.
355. The platform or device as in any one of the preceding claims, wherein the learning journal comprises discussing the one or more contents with a second user.
356. The platform or device as in any one of the preceding claims, wherein the learning journal comprises asking a question regarding the one or more contents.
357. The platform or device as in any one of the preceding claims, wherein the learning journal comprises providing a comment to the one or more contents.
358. The platform or device as in any one of the preceding claims, wherein the learning journal comprises providing an insight to the one or more contents.
359. The platform or device as in any one of the preceding claims, wherein the learning journal comprises recommending the one or more contents.
360. The platform or device as in any one of the preceding claims, wherein the learning journal comprises contributing a content to the one or more contents.
361. The platform or device as in any one of the preceding claims, wherein the learning journal comprises a learning behavior.
362. The platform or device as in any one of the preceding claims, wherein the learning journal comprises a preference.
363. The platform or device as in any one of the preceding claims, wherein the learning journal comprises an interest.
364. The platform or device as in any one of the preceding claims, further comprising a mobile device.
365. The platform or device as in any one of the preceding claims, wherein the mobile device is configured to allow the first user to access the enterprise application via the mobile device.
366. The platform or device as in any one of the preceding claims, wherein the enterprise application comprises a software module configured to send the mobile device a push notification.
367. The platform or device as in any one of the preceding claims, wherein the enterprise application comprises a software module configured to store the one or more contents in a database.
368. The platform or device as in any one of the preceding claims, wherein the enterprise application comprises a software module configured to create a community using the artificial intelligence engine.
369. The platform or device as in any one of the preceding claims, wherein the enterprise application comprises a software module configured to recommend the first user to join a community based on a community analysis performed by the artificial intelligence engine.
370. The platform or device as in any one of the preceding claims, wherein the community analysis comprises analyzing an interest of the first user with the community.
371. The platform or device as in any one of the preceding claims, wherein the enterprise application comprises a software module configured to allow the first user to message a second user.
372. The digital collaboration platform or device as in any one of the preceding claims, wherein the processor system comprises instructions to display a cognitive map to a user in response to the one or more intentions.
373. The digital collaboration platform or device as in any one of the preceding claims, wherein the processor system comprises instructions to provide a permeable membrane software layer.
374. The digital collaboration platform or device as in any one of the preceding claims, wherein the processor system comprises instructions to determine a plurality of intentions of a first user and a plurality of intentions of a second user and to identify one or more common intentions of the first user and the second user and promote communication between the first user and the second user in response to the one or more common intentions.
375. The digital collaboration platform or device as in any one of the preceding claims, wherein the processor system comprises instructions to adjust information shown on a display in response to the one or more intentions of the user.
376. The digital collaboration device as in any one of the preceding claims, wherein the portable identification identifies influence of a user from a confidential environment.
377. The digital collaboration platform or device as in any one of the preceding claims, wherein a processor coupled to a display comprises instructions to configure a user interface for a user to identify insights for text, and instructions to display insights for text of other users.
378. The digital collaboration platform or device as in any one of the preceding claims, wherein a processor coupled to a display comprises instructions to configure a user interface for a user to identify insights for video, and instructions to display insights for video of other users.
379. The digital collaboration platform or device as in any one of the preceding claims, wherein a processor coupled to a display comprises instructions to display a cognitive graph to a user, the cognitive graph determined in response to one or more insights identified by the user.
380. The platform or device as in any one of the preceding claims, wherein a processor coupled to a display comprises instructions to display one or more insights identified by other users to a user and for the user to transmit an acknowledgement of the one or more insights identified by the other users.
381. The digital collaboration platform or device as in any one of the preceding claims, wherein a processor coupled to a display comprises instructions to display one or more insights identified by other users to a user and for the user to transmit an acknowledgement of the one or more insights identified by the other users and wherein the acknowledgement comprises one a statement comprising one or more of "Got It", "Get It", "Declared It" "Declare It".
382. The apparatus as in any one of the preceding claims wherein the processor is configured with instructions to automatically search text and identify insights of the text.
383. The apparatus as in any one of the preceding claims wherein the processor is configured with instructions to automatically search data and identify insights of the data in response to one or more input search parameters.
384. The apparatus as in any one of the preceding claims wherein the processor is configured with instructions to search data and identify insights.
385. The apparatus as in any one of the preceding claims wherein the processor is configured with instructions to search data and identify insights of the data in response to an insight library of a user.
386. The apparatus as in any one of the preceding claims wherein the processor is configured with instructions to search data and identify insights of the data and construct a database of insights in response to an insight library of a community of users.
387. A method, the method comprising providing an apparatus as in any one of the preceding claims.
388. A digital collaboration apparatus to display information viewed by a community of users on a computer display of a user, the apparatus comprising:
A computer display; and
A computer processor coupled to the computer display, the computer processor comprising instructions to show a plurality of insights on the computer display, the plurality of insights arranged on the display in response to the community of users identifying the plurality of insights.
389. The apparatus of claim 388 wherein the plurality of insights is arranged on the display in response to an aggregate ranking of the plurality of insights by the users of the community identifying the plurality of insights shown on the display.
390. The apparatus of claim 389 wherein the plurality of insights is ordered on the display in response to an aggregate ranking of the plurality of insights by the users of the community identifying the plurality of insights shown on the display.
391. The apparatus of claim 388 wherein the processor comprises instructions to receive a user input identifying the plurality of insights on the display and instructions for the user to acknowledge one or more of the plurality of insights arranged on the display.
392. The apparatus of claim 388 wherein the processor comprises instructions to share the acknowledged one or more of the plurality of insights arranged on the display with another user in response to a user input.
393. The apparatus of claim 388 wherein the processor comprises an input to display a plurality of insights of the other users with a first configuration and to display an article comprising one or more of the plurality of insights of the other users with a second configuration.
394. The apparatus of claim 388 wherein the users of the community comprise a plurality of users of a portion of the community of users.
395. The apparatus of claim 388 wherein the plurality of insights comprises one or more of a text insight, a video insight, an audio insight, a speech insight, a location insight, or an identity insight.
396. The apparatus of claim 388 wherein the plurality of insights on the computer display is arranged in response to at least ten users of the community identifying the plurality of insights.
397. The apparatus of claim 388 wherein the plurality of insights on the computer display is arranged in response to at least one hundred users of the community identifying the plurality of insights.
398. The apparatus of claim 388 wherein the plurality of insights on the computer display is arranged in response to at least one thousand users of the community identifying the plurality of insights.
399. The apparatus of claim 388 wherein the plurality of insights on the computer display is arranged in response to at least ten thousand users of the community identifying the plurality of insights.
400. The apparatus of claim 388 wherein the plurality of insights on the computer display is arranged in response to at least one hundred thousand users of the community identifying the plurality of insights.
401. The apparatus of claim 388 wherein the plurality of insights on the computer display is arranged in response to at least one million users of the community identifying the plurality of insights.
402. The apparatus of claim 388 wherein the processor comprises instructions to transmit the plurality of insights to a plurality of computer devices of a plurality of users.
403. The apparatus of claim 388 wherein the processor comprises instructions to transmit one or more of the plurality of insights to a plurality of computer devices of a plurality of the users in response to the user identifying with an input the one or more of the plurality of insights, and wherein processors of each of the computer devices of the plurality of users comprises instructions to display the one or more of the plurality of insights to the plurality of users.
404. The apparatus of claim 388 wherein the processor comprises instructions to transmit one or more of the plurality of insights to a plurality of data stores of the users in response to the user identifying with an input the one or more of the plurality of insights, and wherein the plurality of data stores comprises one or more of a collection from social media cues or a cognitive graph.
405. A digital collaboration apparatus to improve the effectiveness of computer-based collaboration, comprising:
A server comprising a processor having instructions to receive a plurality of insights from a plurality of users, the processor comprising instructions to compare the plurality of insights and provide the plurality of insights in response to a search request.
406. The apparatus of claim 405 wherein the processor comprises instructions to display the plurality of insights on a user device in response to the search request.
407. The apparatus of claim 406 wherein the instructions comprise instructions to transmit a plurality of pointers to the user device, the user device comprising instructions to receive the pointers and download the plurality of insights to the user device display the plurality of insights to the user.
408. A digital collaboration apparatus comprising:
A first display; and
A processor configured to allow a first user to provide an insight for content on the first display.
409. The apparatus of claim 408, further comprising a second display of a second user and a second processor configured with instructions to provide the insight on the display of the second user in response to a first user identifying a passage of text with highlighting of the content to define the insight and wherein the second processor comprises instructions configured to receive a second input from the second user identifying the insight as valuable.
410. The apparatus of claim 408 wherein the processor is configured to show an insight indicator on the first display, the insight indicator being located in a neighborhood of the insight.
411. The apparatus of claim 410 wherein the processor is configured to show an insight value on the first display, the insight value being located in a neighborhood of the insight and indicating a number of users having provided the same insight.
412. The apparatus of claim 411 wherein the processor is configured to show the content with the insight, the insight indicator and the insight value on a second display to a second user.
413. The apparatus of claim 412 wherein the processor is configured to allow the second user to provide the same insight.
414. The apparatus of claim 413 wherein the processor is configured to increment the insight value by 1 and propagates the insight value to the first display.
415. An apparatus, comprising:
A display; and
A processor coupled to the display, the processor comprising instructions to show one or more user identified insights on the display.
416. A digital collaboration apparatus comprising:
A display; and
A processor coupled to the display and configured to allow a first user to provide a valuable component of a video.
417. The apparatus of claim 416 wherein providing the valuable component of the video comprises marking a starting point and an ending point along a timeline of the video.
418. The apparatus of claim 417 wherein providing the valuable component of the video comprises adding comments.
419. The apparatus of claims 416-418 wherein the processor is configured to allow the first user to share the valuable component with a second user.
420. A digital collaboration apparatus comprising:
A display; and
A processor coupled to the display and configured to allow a first user to identify and share a valuable component of a content.
421. The apparatus of claim 420 wherein identifying the valuable component comprises interacting with the display.
422. The apparatus of claim 420 wherein identifying the valuable component comprises highlighting the valuable component.
423. The apparatus of claim 420 wherein identifying the valuable component comprises marking the valuable component.
424. The apparatus of claim 420 wherein identifying the valuable component comprises adding comments to the valuable component.
425. The apparatus of claim 420 wherein the valuable component overlaps with an existing valuable component of the content.
426. The apparatus of claim 420 wherein sharing the valuable component comprises interacting with the display.
427. The apparatus of claim 420 wherein sharing the valuable component comprises transmitting the valuable component by the apparatus to a second user.
428. The apparatus of claim 420 wherein the valuable component comprises one or more of: a text, a video clip, an audio segment, an image segment, a table cell, and a combination of the same.
429. The apparatus of claim 420 wherein the valuable component comprises a plurality of valuable components of the content, and wherein the plurality of components are arranged in a first order prior to identification and a second order different from the first order subsequent to identification, the first order different from the second order.
430. A digital collaboration apparatus comprising:
A display; and
A processor coupled to the display and configured to allow a first user to receive a valuable component of a content.
431. The apparatus of claim 430 wherein the valuable component is identified and shared by a second user.
432. The apparatus of claim 430 wherein the valuable component comprises interacting with the first display.
433. The apparatus of claim 430 wherein sharing the valuable component comprises transmitting the valuable component to another user.
434. The apparatus of claim 430 wherein the valuable component is identified and shared by a second user.
435. The apparatus of claim 430 wherein the processor is configured to allow the first user to emphasize the valuable component.
436. The apparatus of claim 430 wherein the processor is configured to allow the first user to de-emphasize the valuable component.
437. A digital collaboration apparatus comprising:
A display; and
A processor coupled to the display and configured to allow a user to receive a first valuable component and a second valuable component of content.
438. The apparatus of claim 437 wherein the processor is configured to allow the user to combine the first valuable component with the second valuable component.
439. A digital collaboration apparatus comprising:
A display; and
A processor configured to analyze a plurality of valuable components of contents.
440. The apparatus of claim 439 wherein the analyzing the plurality of the valuable components comprises analyzing actions on the valuable components.
441. The apparatus of claim 440 wherein the actions comprise one or more of: identifying the valuable components, de-identifying the valuable components, sharing the valuable components, adding comments to the valuable components, accessing the valuable components, ranking the valuable components, voting the valuable components,
acknowledging receipt of the valuable components.
442. The apparatus as in any of preceding claims, wherein the plurality of insights is arranged on the display in response to an aggregate ranking of the plurality of insights by the users of the community identifying the plurality of insights shown on the display.
443. The apparatus as in any of preceding claims, wherein the plurality of insights is ordered on the display in response to an aggregate ranking of the plurality of insights by the users of the community identifying the plurality of insights shown on the display.
444. The apparatus as in any of preceding claims, wherein the processor comprises instructions to receive a user input identifying the plurality of insights on the display and instructions for the user to acknowledge one or more of the plurality of insights arranged on the display.
445. The apparatus as in any of preceding claims, wherein the processor comprises instructions to share the acknowledged one or more of the plurality of insights arranged on the display with another user in response to a user input.
446. The apparatus as in any of preceding claims, wherein the processor comprises an input to display a plurality of insights of the other users with a first configuration and to display an article comprising one or more of the plurality of insights of the other users with a second configuration.
447. The apparatus as in any of preceding claims, wherein the users of the community comprise a plurality of users of a portion of the community of users.
448. The apparatus as in any of preceding claims, wherein the plurality of insights comprises one or more of a text insight, a video insight, an audio insight, a speech insight, a location insight, or an identity insight.
449. The apparatus as in any of preceding claims, wherein the plurality of insights on the computer display is arranged in response to at least ten users of the community identifying the plurality of insights.
450. The apparatus as in any of preceding claims, wherein the plurality of insights on the computer display is arranged in response to at least one hundred users of the community identifying the plurality of insights.
451. The apparatus as in any of preceding claims, wherein the plurality of insights on the computer display is arranged in response to at least one thousand users of the community identifying the plurality of insights.
452. The apparatus as in any of preceding claims, wherein the plurality of insights on the computer display is arranged in response to at least ten thousand users of the community identifying the plurality of insights.
453. The apparatus as in any of preceding claims, wherein the plurality of insights on the computer display is arranged in response to at least one hundred thousand users of the community identifying the plurality of insights.
454. The apparatus as in any of preceding claims, wherein the plurality of insights on the computer display is arranged in response to at least one million users of the community identifying the plurality of insights.
455. The apparatus as in any of preceding claims, wherein the processor comprises instructions to transmit the plurality of insights to a plurality of computer devices of a plurality of users.
456. The apparatus as in any of preceding claims, wherein the processor comprises instructions to transmit one or more of the plurality of insights to a plurality of computer devices of a plurality of the users in response to the user identifying with an input the one or more of the plurality of insights, and wherein processors of each of the computer devices of the plurality of users comprises instructions to display the one or more of the plurality of insights to the plurality of users.
457. The apparatus as in any of preceding claims, wherein the processor comprises instructions to transmit one or more of the plurality of insights to a plurality of data stores of the users in response to the user identifying with an input the one or more of the plurality of insights, and wherein the plurality of data stores comprises one or more of a collection from social media cues or a cognitive graph.
458. The apparatus as in any of preceding claims, wherein the processor comprises instructions to display the plurality of insights on a user device in response to the search request.
459. The apparatus as in any of preceding claims, wherein the instructions comprise instructions to transmit a plurality of pointers to the user device, the user device comprising instructions to receive the pointers and download the plurality of insights to the user device display the plurality of insights to the user.
460. The apparatus as in any of preceding claims, wherein providing the insight comprises highlighting one or more text components of the content.
461. The apparatus as in any of preceding claims, wherein the processor is configured to show an insight indicator on the first display, the insight indicator being located in a neighborhood of the insight.
462. The apparatus as in any of preceding claims, wherein the processor is configured to show an insight value on the first display, the insight value being located in a neighborhood of the insight and indicating a number of users having provided the same insight.
463. The apparatus as in any of preceding claims, wherein the processor is configured to show the content with the insight, the insight indicator and the insight value on a second display to a second user.
464. The apparatus as in any of preceding claims, wherein the processor is configured to allow the second user to provide the same insight.
465. The apparatus as in any of preceding claims, wherein the processor is configured to increment the insight value by 1 and propagates the insight value to the first display.
466. The apparatus as in any of preceding claims, wherein providing the valuable component of the video comprises marking a starting point and an ending point along a timeline of the video.
467. The apparatus as in any of preceding claims, wherein providing the valuable component of the video comprises adding comments.
468. The apparatus as in any of preceding claims, wherein the processor is configured to allow the first user to share the valuable component with a second user.
469. The apparatus as in any of preceding claims, wherein identifying the valuable component comprises interacting with the display.
470. The apparatus as in any of preceding claims, wherein identifying the valuable component comprises highlighting the valuable component.
471. The apparatus as in any of preceding claims, wherein identifying the valuable component comprises marking the valuable component.
472. The apparatus as in any of preceding claims, wherein identifying the valuable component comprises adding comments to the valuable component.
473. The apparatus as in any of preceding claims, wherein the valuable component overlaps with an existing valuable component of the content.
474. The apparatus as in any of preceding claims, wherein sharing the valuable component comprises interacting with the display.
475. The apparatus as in any of preceding claims, wherein sharing the valuable component comprises transmitting the valuable component by the apparatus to a second user.
476. The apparatus as in any of preceding claims, wherein the valuable component comprises one or more of: a text, a video clip, an audio segment, an image segment, a table cell, and a combination of the same.
477. The apparatus as in any of preceding claims, wherein the valuable component comprises a plurality of valuable components of the content, and wherein the plurality of components are arranged in a first order prior to identification and a second order different from the first order subsequent to identification, the first order different from the second order.
478. The apparatus as in any of preceding claims, wherein the valuable component is identified and shared by a second user.
479. The apparatus as in any of preceding claims, wherein the valuable component comprises interacting with the first display.
480. The apparatus as in any of preceding claims, wherein sharing the valuable component comprises transmitting the valuable component to another user.
481. The apparatus as in any of preceding claims, wherein the valuable component is identified and shared by a second user.
482. The apparatus as in any of preceding claims, wherein the processor is configured to allow the first user to emphasize the valuable component.
483. The apparatus as in any of preceding claims, wherein the processor is configured to allow the first user to de-emphasize the valuable component.
484. The apparatus as in any of preceding claims, wherein the processor is configured to allow the user to combine the first valuable component with the second valuable component.
485. The apparatus as in any of preceding claims, wherein the analyzing the plurality of the valuable components comprises analyzing actions on the valuable components.
486. The apparatus as in any of preceding claims, wherein the actions comprise one or more of: identifying the valuable components, de-identifying the valuable components, sharing the valuable components, adding comments to the valuable components, accessing the valuable components, ranking the valuable components, voting the valuable components, acknowledging receipt of the valuable components.
487. The apparatus as in any one of the preceding claims wherein the processor is configured with instructions to automatically search text and identify insights of the text.
488. The apparatus as in any one of the preceding claims wherein the processor is configured with instructions to automatically search data and identify insights of the data in response to one or more input search parameters.
489. The apparatus as in any one of the preceding claims wherein the processor is configured with instructions to search data and identify insights.
490. The apparatus as in any one of the preceding claims wherein the processor is configured with instructions to search data and identify insights of the data in response to an insight library of a user.
491. The apparatus as in any one of the preceding claims wherein the processor is configured with instructions to search data and identify insights of the data in response to an insight library of a community of users.
492. A method, the method comprising providing an apparatus as in any one of the preceding claims.
PCT/US2015/064118 2014-12-05 2015-12-04 Intent based digital collaboration platform architecture and design WO2016090326A1 (en)

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US201562111063P true 2015-02-02 2015-02-02
US62/111,063 2015-02-02
US62/111,056 2015-02-02
US201562111623P true 2015-02-03 2015-02-03
US201562111626P true 2015-02-03 2015-02-03
US62/111,623 2015-02-03
US62/111,626 2015-02-03
US201562112144P true 2015-02-04 2015-02-04
US201562112139P true 2015-02-04 2015-02-04
US62/112,139 2015-02-04
US62/112,144 2015-02-04
US201562112631P true 2015-02-05 2015-02-05
US201562112633P true 2015-02-05 2015-02-05
US62/112,631 2015-02-05
US62/112,633 2015-02-05
US201562113319P true 2015-02-06 2015-02-06
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Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070150464A1 (en) * 2005-12-27 2007-06-28 Scott Brave Method and apparatus for predicting destinations in a navigation context based upon observed usage patterns
US20080104679A1 (en) * 2006-10-27 2008-05-01 Xystar Technologies, Inc. Selective privacy management in virtual social networks
US20120001919A1 (en) * 2008-10-20 2012-01-05 Erik Lumer Social Graph Based Recommender
US8244848B1 (en) * 2010-04-19 2012-08-14 Facebook, Inc. Integrated social network environment
US20120271831A1 (en) * 2010-04-19 2012-10-25 Srinivas Narayanan Dynamically Generating Recommendations Based on Social Graph Information
US20130007787A1 (en) * 2011-06-29 2013-01-03 Avaya Inc. System and method for processing media highlights
US20130290339A1 (en) * 2012-04-27 2013-10-31 Yahoo! Inc. User modeling for personalized generalized content recommendations
US20140250180A1 (en) * 2013-03-04 2014-09-04 Erick Tseng Ranking Videos for a User

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070150464A1 (en) * 2005-12-27 2007-06-28 Scott Brave Method and apparatus for predicting destinations in a navigation context based upon observed usage patterns
US20080104679A1 (en) * 2006-10-27 2008-05-01 Xystar Technologies, Inc. Selective privacy management in virtual social networks
US20120001919A1 (en) * 2008-10-20 2012-01-05 Erik Lumer Social Graph Based Recommender
US8244848B1 (en) * 2010-04-19 2012-08-14 Facebook, Inc. Integrated social network environment
US20120271831A1 (en) * 2010-04-19 2012-10-25 Srinivas Narayanan Dynamically Generating Recommendations Based on Social Graph Information
US20130007787A1 (en) * 2011-06-29 2013-01-03 Avaya Inc. System and method for processing media highlights
US20130290339A1 (en) * 2012-04-27 2013-10-31 Yahoo! Inc. User modeling for personalized generalized content recommendations
US20140250180A1 (en) * 2013-03-04 2014-09-04 Erick Tseng Ranking Videos for a User

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