US20190087833A1

US20190087833A1 - Digital credential receiver performance model

Info

Publication number: US20190087833A1
Application number: US15/993,557
Authority: US
Inventors: Mark Mercury; Jarin Schmidt; Clarke Porter; Peter Pascale; Andrew Stockinger; Ronald Lancaster
Original assignee: Pearson Education Inc
Current assignee: Pearson Education Inc
Priority date: 2017-09-15
Filing date: 2018-05-30
Publication date: 2019-03-21
Also published as: US20190089692A1; US11341508B2; US20190087829A1; US20190087519A1; US20190087558A1; US20190087834A1; US10885530B2; US20190087781A1; US20190087830A1; WO2019055286A1; US11042885B2; US20190089701A1; US20190087832A1; US20190087831A1; US20210192539A1; US20190089691A1

Abstract

Techniques described herein relate to determining model profiles of credential receivers corresponding to particular field objects. A digital credential platform server may receive data corresponding to a plurality of credential receivers, identify particular subsets of the credential receivers associated with particular field data objects, and then determine and analyze credential receiver data to determine sets of capabilities associated with the receiver. The determined sets of capabilities associated with each credential receiver may be analyzed, and based on the analyses, one or more model profiles of capabilities may be generated for the field data object. The various analyses may include regression analyses, trained machine learning algorithms, and/or other mathematical analyses capable of identifying model capabilities profiles for credential receivers.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a non-provisional of and claims priority to U.S. Provisional Patent Application No. 62/559,433, entitled “DIGITAL CREDENTIAL PLATFORM,” filed Sep. 15, 2017, the entire contents of which are incorporated by reference herein for all purposes.

BACKGROUND

Changes in computing technologies have provided individuals with additional options for obtaining and validating technical skills and proficiencies. Rather than attending traditional educational institutions and professional training courses, many individuals may now obtain their technical skills and proficiencies from alternative sources, such as structured or unstructured and asynchronous eLearning programs using distance learning technology, self-study research without any direct supervision, or various alternative technical learning, training, and testing entities. Although such advances in technologies and increasing globalization trends provide many more options for individuals to obtain technical skills and proficiencies, they also present challenges in publishing, verifying and tracking the sets of technical skills and proficiencies that these individuals have obtained. Many individuals and institutions no longer rely on physical certificates such as diplomas, transcripts, certification statements, and physical licenses, to verify the authenticity of an individual's proficiencies or qualifications. Instead, certain institutions may issue digital credentials (or digital badges) to qualifying individuals, and these digital credential earners may use the digital credentials to certify the skills or qualifications that the earner obtained vis-à-vis the institution.

BRIEF SUMMARY

Various techniques are described herein for determining model profiles of digital credential receivers corresponding to particular field objects. In various embodiments, a digital credential platform server may receive data corresponding to a plurality of credential receivers, and may identify particular subsets of the credential receivers associated with particular field data objects. For each of the credential receiver in a determine subset, the digital credential platform server may analyze receiver data to determine a set of capabilities associated with the credential receiver. In some embodiments, set of capabilities associated with credential receivers may be determined by retrieving the portfolios of digital credentials issued to the credential receivers, retrieving data identifying capabilities associated with each of the issued digital credentials, and aggregating the identified capabilities over all of the receiver's issued credentials. The digital credential platform server then may perform various analyses on the determined sets of capabilities associated with each credential receiver, and based on the analyses, may generate a model capabilities profile for the field data object. In various embodiments, the analyses may include regression analyses, trained machine learning algorithms, and/or other mathematical analyses to identify the capabilities profile corresponding to a high-performing receiver (or low-performing receiver). Individual receiver capabilities may further be determined based on physical simulations, sensor-monitored environments, and the like. Model profiles may include, additionally or alternatively to capabilities, various user/receiver traits, location data, field data, and the like.
Additional techniques described herein relate to analyzing and valuating individual issuances of digital credentials to particular users within a digital credential platform. In certain embodiments, a digital credential platform server may receive and/or retrieve data relating to digital credential receivers and particular types of digital credentials, which may be issued or potentially issued to the credential receivers. The digital credential platform server then may query one or more data stores of field data objects, to determine various field data objects associated with the particular types of digital credentials. For any associated field data objects, the platform server may retrieve value data for the field data objects from various data sources, and may compute one or more values for the particular types of digital credentials based on the values for the corresponding field data objects. In some examples, valuation of a particular type of digital credential may depend on characteristics of a particular credential receiver, such as the set of additional digital credentials currently or previously issued to the credential receiver. Additional valuation factors for digital credentials may include location of a current or potential credential receiver, individual traits or characteristics of a current or potential credential receiver, etc.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing illustrating an example of a content distribution network.

FIG. 2 is a block diagram illustrating a computer server and computing environment within a content distribution network.

FIG. 3 is a block diagram illustrating an embodiment of one or more data store servers within a content distribution network.

FIG. 4 is a block diagram illustrating an embodiment of one or more content management servers within a content distribution network.

FIG. 5 is a block diagram illustrating the physical and logical components of a special-purpose computer device within a content distribution network.

FIG. 6 is a block diagram illustrating an example computing environment for generating, managing, and tracking digital credential templates and digital credentials, according to one or more embodiments of the disclosure.

FIG. 7 is an diagram illustrating an example computing environment for executing and monitoring physical simulations within a digital credential system, according to one or more embodiments of the disclosure.

FIG. 8 is a flow diagram illustrating an example process of executing and monitoring physical simulations for generation of digital credentials, according to one or more embodiments of the disclosure.

FIG. 9A is an diagram illustrating a computer terminal-based system for sensor-based monitoring, and generation of digital credentials, according to one or more embodiments of the disclosure.

FIG. 9B is an diagram illustrating a physical environment-based system for sensor-based monitoring, and generation of digital credentials, according to one or more embodiments of the disclosure.

FIG. 10 is a flow diagram illustrating an example process of generating and issuing digital credentials in a sensor-monitored environment, according to one or more embodiments of the disclosure.

FIG. 11 is an diagram illustrating an example computing environment for analyzing sensor-based activity monitoring within a digital credential system, according to one or more embodiments of the disclosure.

FIG. 12 is a flow diagram illustrating an example process of generating digital credentials and tracking the corresponding activities in a sensor-monitored environment, according to one or more embodiments of the disclosure.

FIG. 13 is a flow diagram illustrating an example process of analyzing activities in a sensor-monitored environment to determine digital credential expiration and/or recertification times, according to one or more embodiments of the disclosure.

FIG. 14 is an diagram illustrating an example computing environment for generating and analyzing digital credentials using received sensor monitoring data, according to one or more embodiments of the disclosure.

FIG. 15 is a flow diagram illustrating an example process of generating and storing digital credentials with associated data from sensor-monitored environments, according to one or more embodiments of the disclosure.

FIGS. 16A-16B are flow diagrams illustrating example processes of retrieving sensor data associated with issued digital credentials, and generating additional and/or updated digital credentials based on the retrieved sensor data, according to one or more embodiments of the disclosure.

FIGS. 17A-17B are diagrams illustrating facial recognition and analysis functionality performed during digital credential generation and analyses processes within sensor-monitored environments, according to one or more embodiments of the disclosure.

FIG. 18 is a flow diagram illustrating an example process of generating and storing digital credentials with associated user feedback data from sensor-monitored environments, according to one or more embodiments of the disclosure.

FIG. 19 is a block diagram illustrating an example computing environment for retrieving and analyzing performance data from external systems to determine performer model profiles, according to one or more embodiments of the disclosure.

FIG. 20 is a flow diagram illustrating an example process of retrieving and analyzing performance data from external systems to determine performer model profiles, according to one or more embodiments of the disclosure.

FIG. 21 is a flow diagram illustrating an example process of valuating a digital credential offering for a particular user within a digital credential platform, according to one or more embodiments of the disclosure.

FIG. 22 is an example user interface screen displaying the results of a prospective digital credential search for a particular credential receiver, according to one or more embodiments of the disclosure.

In the appended figures, similar components and/or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label by a dash and a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.

DETAILED DESCRIPTION

The ensuing description provides illustrative embodiment(s) only and is not intended to limit the scope, applicability or configuration of the disclosure. Rather, the ensuing description of the illustrative embodiment(s) will provide those skilled in the art with an enabling description for implementing a preferred exemplary embodiment. It is understood that various changes can be made in the function and arrangement of elements without departing from the spirit and scope as set forth in the appended claims.
Various techniques (e.g., systems, methods, computer-program products tangibly embodied in a non-transitory machine-readable storage medium, etc.) are described herein for determining model profiles of digital credential receivers corresponding to particular field objects. In various embodiments, a digital credential platform server may receive data corresponding to a plurality of credential receivers, and may identify particular subsets of the credential receivers associated with particular field data objects. For each of the credential receiver in a determine subset, the digital credential platform server may analyze receiver data to determine a set of capabilities associated with the credential receiver. In some embodiments, set of capabilities associated with credential receivers may be determined by retrieving the portfolios of digital credentials issued to the credential receivers, retrieving data identifying capabilities associated with each of the issued digital credentials, and aggregating the identified capabilities over all of the receiver's issued credentials. The digital credential platform server then may perform various analyses on the determined sets of capabilities associated with each credential receiver, and based on the analyses, may generate a model capabilities profile for the field data object. In various embodiments, the analyses may include regression analyses, trained machine learning algorithms, and/or other mathematical analyses to identify the capabilities profile corresponding to a high-performing receiver (or low-performing receiver). Individual receiver capabilities may further be determined based on physical simulations, sensor-monitored environments, and the like. Model profiles may include, additionally or alternatively to capabilities, various user/receiver traits, location data, field data, and the like.
Additional techniques described herein relate to analyzing and valuating individual issuances of digital credentials to particular users within a digital credential platform. In certain embodiments, a digital credential platform server may receive and/or retrieve data relating to digital credential receivers and particular types of digital credentials, which may be issued or potentially issued to the credential receivers. The digital credential platform server then may query one or more data stores of field data objects, to determine various field data objects associated with the particular types of digital credentials. For any associated field data objects, the platform server may retrieve value data for the field data objects from various data sources, and may compute one or more values for the particular types of digital credentials based on the values for the corresponding field data objects. In some examples, valuation of a particular type of digital credential may depend on characteristics of a particular credential receiver, such as the set of additional digital credentials currently or previously issued to the credential receiver. Additional valuation factors for digital credentials may include location of a current or potential credential receiver, individual traits or characteristics of a current or potential credential receiver, etc.
With reference now to FIG. 1, a block diagram is shown illustrating various components of a content distribution network (CDN) 100 which implements and supports certain embodiments and features described herein. Content distribution network 100 may include one or more content management servers 102. As discussed below in more detail, content management servers 102 may be any desired type of server including, for example, a rack server, a tower server, a miniature server, a blade server, a mini rack server, a mobile server, an ultra-dense server, a super server, or the like, and may include various hardware components, for example, a motherboard, a processing units, memory systems, hard drives, network interfaces, power supplies, etc. Content management server 102 may include one or more server farms, clusters, or any other appropriate arrangement and/or combination or computer servers. Content management server 102 may act according to stored instructions located in a memory subsystem of the server 102, and may run an operating system, including any commercially available server operating system and/or any other operating systems discussed herein.
The content distribution network 100 may include one or more data store servers 104, such as database servers and file-based storage systems. Data stores 104 may comprise stored data relevant to the functions of the content distribution network 100. Illustrative examples of data stores 104 that may be maintained in certain embodiments of the content distribution network 100 are described below in reference to FIG. 3. In some embodiments, multiple data stores may reside on a single server 104, either using the same storage components of server 104 or using different physical storage components to assure data security and integrity between data stores. In other embodiments, each data store may have a separate dedicated data store server 104.
Content distribution network 100 also may include one or more user devices 106 and/or supervisor devices 110. User devices 106 and supervisor devices 110 may display content received via the content distribution network 100, and may support various types of user interactions with the content. User devices 106 and supervisor devices 110 may include mobile devices such as smartphones, tablet computers, personal digital assistants, and wearable computing devices. Such mobile devices may run a variety of mobile operating systems, and may be enabled for Internet, e-mail, short message service (SMS), Bluetooth®, mobile radio-frequency identification (M-RFID), and/or other communication protocols. Other user devices 106 and supervisor devices 110 may be general purpose personal computers or special-purpose computing devices including, by way of example, personal computers, laptop computers, workstation computers, projection devices, and interactive room display systems. Additionally, user devices 106 and supervisor devices 110 may be any other electronic devices, such as thin-client computers, Internet-enabled gaming systems, business or home appliances, and/or personal messaging devices, capable of communicating over network(s) 120.
In different contexts of content distribution networks 100, user devices 106 and supervisor devices 110 may correspond to different types of specialized devices, for example, student devices and teacher devices in an educational network, employee devices and presentation devices in a company network, different gaming devices in a gaming network, etc. In some embodiments, user devices 106 and supervisor devices 110 may operate in the same physical location 107, such as a classroom or conference room. In such cases, the devices may contain components that support direct communications with other nearby devices, such as a wireless transceivers and wireless communications interfaces, Ethernet sockets or other Local Area Network (LAN) interfaces, etc. In other implementations, the user devices 106 and supervisor devices 110 need not be used at the same location 107, but may be used in remote geographic locations in which each user device 106 and supervisor device 110 may use security features and/or specialized hardware (e.g., hardware-accelerated SSL and HTTPS, WS-Security, firewalls, etc.) to communicate with the content management server 102 and/or other remotely located user devices 106. Additionally, different user devices 106 and supervisor devices 110 may be assigned different designated roles, such as presenter devices, teacher devices, administrator devices, or the like, and in such cases the different devices may be provided with additional hardware and/or software components to provide content and support user capabilities not available to the other devices.
The content distribution network 100 also may include a privacy server 108 that maintains private user information at the privacy server 108 while using applications or services hosted on other servers. For example, the privacy server 108 may be used to maintain private data of a user within one jurisdiction even though the user is accessing an application hosted on a server (e.g., the content management server 102) located outside the jurisdiction. In such cases, the privacy server 108 may intercept communications between a user device 106 or supervisor device 110 and other devices that include private user information. The privacy server 108 may create a token or identifier that does not disclose the private information and may use the token or identifier when communicating with the other servers and systems, instead of using the user's private information.
As illustrated in FIG. 1, the content management server 102 may be in communication with one or more additional servers, such as a content server 112, a user data server 112, and/or an administrator server 116. Each of these servers may include some or all of the same physical and logical components as the content management server(s) 102, and in some cases, the hardware and software components of these servers 112-116 may be incorporated into the content management server(s) 102, rather than being implemented as separate computer servers.
Content server 112 may include hardware and software components to generate, store, and maintain the content resources for distribution to user devices 106 and other devices in the network 100. For example, in content distribution networks 100 used for professional training and educational purposes, content server 112 may include data stores of training materials, presentations, interactive programs and simulations, course models, course outlines, and various training interfaces that correspond to different materials and/or different types of user devices 106. In content distribution networks 100 used for media distribution, interactive gaming, and the like, a content server 112 may include media content files such as music, movies, television programming games, and advertisements.
User data server 114 may include hardware and software components that store and process data for multiple users relating to each user's activities and usage of the content distribution network 100. For example, the content management server 102 may record and track each user's system usage, including their user device 106, content resources accessed, and interactions with other user devices 106. This data may be stored and processed by the user data server 114, to support user tracking and analysis features. For instance, in the professional training and educational contexts, the user data server 114 may store and analyze each user's training materials viewed, presentations attended, courses completed, interactions, evaluation results, and the like. The user data server 114 may also include a repository for user-generated material, such as evaluations and tests completed by users, and documents and assignments prepared by users. In the context of media distribution and interactive gaming, the user data server 114 may store and process resource access data for multiple users (e.g., content titles accessed, access times, data usage amounts, gaming histories, user devices and device types, etc.).
Administrator server 116 may include hardware and software components to initiate various administrative functions at the content management server 102 and other components within the content distribution network 100. For example, the administrator server 116 may monitor device status and performance for the various servers, data stores, and/or user devices 106 in the content distribution network 100. When necessary, the administrator server 116 may add or remove devices from the network 100, and perform device maintenance such as providing software updates to the devices in the network 100. Various administrative tools on the administrator server 116 may allow authorized users to set user access permissions to various content resources, monitor resource usage by users and devices 106, and perform analyses and generate reports on specific network users and/or devices (e.g., resource usage tracking reports, training evaluations, etc.).
The content distribution network 100 may include one or more communication networks 120. Although only a single network 120 is identified in FIG. 1, the content distribution network 100 may include any number of different communication networks between any of the computer servers and devices shown in FIG. 1 and/or other devices described herein. Communication networks 120 may enable communication between the various computing devices, servers, and other components of the content distribution network 100. As discussed below, various implementations of content distribution networks 100 may employ different types of networks 120, for example, computer networks, telecommunications networks, wireless networks, and/or any combination of these and/or other networks.
With reference to FIG. 2, an illustrative distributed computing environment 200 is shown including a computer server 202, four client computing devices 206, and other components that may implement certain embodiments and features described herein. In some embodiments, the server 202 may correspond to the content management server 102 discussed above in FIG. 1, and the client computing devices 206 may correspond to the user devices 106. However, the computing environment 200 illustrated in FIG. 2 may correspond to any other combination of devices and servers configured to implement a client-server model or other distributed computing architecture.
Client devices 206 may be configured to receive and execute client applications over one or more networks 220. Such client applications may be web browser based applications and/or standalone software applications, such as mobile device applications. Server 202 may be communicatively coupled with the client devices 206 via one or more communication networks 220. Client devices 206 may receive client applications from server 202 or from other application providers (e.g., public or private application stores). Server 202 may be configured to run one or more server software applications or services, for example, web-based or cloud-based services, to support content distribution and interaction with client devices 206. Users operating client devices 206 may in turn utilize one or more client applications (e.g., virtual client applications) to interact with server 202 to utilize the services provided by these components.
Various different subsystems and/or components 204 may be implemented on server 202. Users operating the client devices 206 may initiate one or more client applications to use services provided by these subsystems and components. The subsystems and components within the server 202 and client devices 206 may be implemented in hardware, firmware, software, or combinations thereof. Various different system configurations are possible in different distributed computing systems 200 and content distribution networks 100. The embodiment shown in FIG. 2 is thus one example of a distributed computing system and is not intended to be limiting.
Although exemplary computing environment 200 is shown with four client computing devices 206, any number of client computing devices may be supported. Other devices, such as specialized sensor devices, etc., may interact with client devices 206 and/or server 202.
As shown in FIG. 2, various security and integration components 208 may be used to transmit, receive, and manage communications between the server 202 and user devices 206 over one or more communication networks 220. The security and integration components 208 may include separate servers, such as web servers and/or authentication servers, and/or specialized networking components, such as firewalls, routers, gateways, load balancers, and the like. In some cases, the security and integration components 208 may correspond to a set of dedicated hardware and/or software operating at the same physical location and under the control of same entities as server 202. For example, components 208 may include one or more dedicated web servers and network hardware in a datacenter or a cloud infrastructure. In other examples, the security and integration components 208 may correspond to separate hardware and software components which may be operated at a separate physical location and/or by a separate entity.
Security and integration components 208 may implement various security features for data transmission and storage, such as authenticating users and restricting access to unknown or unauthorized users. In various implementations, security and integration components 208 may provide, for example, a file-based integration scheme or a service-based integration scheme for transmitting data between the various devices in the content distribution network 100. Security and integration components 208 also may use secure data transmission protocols and/or encryption for data transfers, for example, File Transfer Protocol (FTP), Secure File Transfer Protocol (SFTP), and/or Pretty Good Privacy (PGP) encryption.
In some embodiments, one or more web services may be implemented within the security and integration components 208 and/or elsewhere within the content distribution network 100. Such web services, including cross-domain and/or cross-platform web services, may be developed for enterprise use in accordance with various web service standards, such as RESTful web services (i.e., services based on the Representation State Transfer (REST) architectural style and constraints), and/or web services designed in accordance with the Web Service Interoperability (WS-I) guidelines. Some web services may use the Secure Sockets Layer (SSL) or Transport Layer Security (TLS) protocol to provide secure connections between the server 202 and user devices 206. SSL or TLS may use HTTP or HTTPS to provide authentication and confidentiality. In other examples, web services may be implemented using REST over HTTPS with the OAuth open standard for authentication, or using the WS-Security standard which provides for secure SOAP messages using XML, encryption. In other examples, the security and integration components 208 may include specialized hardware for providing secure web services. For example, security and integration components 208 may include secure network appliances having built-in features such as hardware-accelerated SSL and HTTPS, WS-Security, and firewalls. Such specialized hardware may be installed and configured in front of any web servers, so that any external devices may communicate directly with the specialized hardware.
Communication network(s) 220 may be any type of network familiar to those skilled in the art that can support data communications using any of a variety of commercially-available protocols, including without limitation, TCP/IP (transmission control protocol/Internet protocol), SNA (systems network architecture), IPX (Internet packet exchange), Secure Sockets Layer (SSL) or Transport Layer Security (TLS) protocols, Hyper Text Transfer Protocol (HTTP) and Secure Hyper Text Transfer Protocol (HTTPS), Bluetooth®, Near Field Communication (NFC), and the like. Merely by way of example, network(s) 220 may be local area networks (LAN), such as one based on Ethernet, Token-Ring and/or the like. Network(s) 220 also may be wide-area networks, such as the Internet. Networks 220 may include telecommunication networks such as a public switched telephone networks (PSTNs), or virtual networks such as an intranet or an extranet. Infrared and wireless networks (e.g., using the Institute of Electrical and Electronics (IEEE) 802.11 protocol suite or other wireless protocols) also may be included in networks 220.
Computing environment 200 also may include one or more data stores 210 and/or back-end servers 212. In certain examples, the data stores 210 may correspond to data store server(s) 104 discussed above in FIG. 1, and back-end servers 212 may correspond to the various back-end servers 112-116. Data stores 210 and servers 212 may reside in the same datacenter or may operate at a remote location from server 202. In some cases, one or more data stores 210 may reside on a non-transitory storage medium within the server 202. Other data stores 210 and back-end servers 212 may be remote from server 202 and configured to communicate with server 202 via one or more networks 220. In certain embodiments, data stores 210 and back-end servers 212 may reside in a storage-area network (SAN), or may use storage-as-a-service (STaaS) architectural model.
With reference to FIG. 3, an illustrative set of data stores and/or data store servers is shown, corresponding to the data store servers 104 of the content distribution network 100 discussed above in FIG. 1. One or more individual data stores 301-309 may reside in storage on a single computer server 104 (or a single server farm or cluster) under the control of a single entity, or may reside on separate servers operated by different entities and/or at remote locations. In some embodiments, data stores 301-309 may be accessed by the content management server 102 and/or other devices and servers within the network 100 (e.g., user devices 106, supervisor devices 110, administrator servers 116, etc.). Access to one or more of the data stores 301-309 may be limited or denied based on the processes, user credentials, and/or devices attempting to interact with the data store.
The paragraphs below describe examples of specific data stores that may be implemented within some embodiments of a content distribution network 100. It should be understood that the below descriptions of data stores 301-309, including their functionality and types of data stored therein, are illustrative and non-limiting. Data stores server architecture, design, and the execution of specific data stores 301-309 may depend on the context, size, and functional requirements of a content distribution network 100. For example, in content distribution systems 100 used for professional training and educational purposes, separate databases or file-based storage systems may be implemented in data store server(s) 104 to store trainee and/or student data, trainer and/or professor data, training module data and content descriptions, training results, evaluation data, and the like. In contrast, in content distribution systems 100 used for media distribution from content providers to subscribers, separate data stores may be implemented in data stores server(s) 104 to store listings of available content titles and descriptions, content title usage statistics, subscriber profiles, account data, payment data, network usage statistics, etc.
A user profile data store 301 may include information relating to the end users within the content distribution network 100. This information may include user characteristics such as the user names, access credentials (e.g., login and passwords), user preferences, and information relating to any previous user interactions within the content distribution network 100 (e.g., requested content, posted content, content modules completed, training scores or evaluations, other associated users, etc.).
An accounts data store 302 may generate and store account data for different users in various roles within the content distribution network 100. For example, accounts may be created in an accounts data store 302 for individual end users, supervisors, administrator users, and entities such as companies or educational institutions. Account data may include account types, current account status, account characteristics, and any parameters, limits, restrictions associated with the accounts.
A content library data store 303 may include information describing the individual content items (or content resources) available via the content distribution network 100. In some embodiments, the library data store 303 may include metadata, properties, and other characteristics associated with the content resources stored in the content server 112. Such data may identify one or more aspects or content attributes of the associated content resources, for example, subject matter, access level, or skill level of the content resources, license attributes of the content resources (e.g., any limitations and/or restrictions on the licensable use and/or distribution of the content resource), price attributes of the content resources (e.g., a price and/or price structure for determining a payment amount for use or distribution of the content resource), rating attributes for the content resources (e.g., data indicating the evaluation or effectiveness of the content resource), and the like. In some embodiments, the library data store 303 may be configured to allow updating of content metadata or properties, and to allow the addition and/or removal of information relating to the content resources. For example, content relationships may be implemented as graph structures, which may be stored in the library data store 303 or in an additional store for use by selection algorithms along with the other metadata.
A pricing data store 304 may include pricing information and/or pricing structures for determining payment amounts for providing access to the content distribution network 100 and/or the individual content resources within the network 100. In some cases, pricing may be determined based on a user's access to the content distribution network 100, for example, a time-based subscription fee, or pricing based on network usage and. In other cases, pricing may be tied to specific content resources. Certain content resources may have associated pricing information, whereas other pricing determinations may be based on the resources accessed, the profiles and/or accounts of the user, and the desired level of access (e.g., duration of access, network speed, etc.). Additionally, the pricing data store 304 may include information relating to compilation pricing for groups of content resources, such as group prices and/or price structures for groupings of resources.
A license data store 305 may include information relating to licenses and/or licensing of the content resources within the content distribution network 100. For example, the license data store 305 may identify licenses and licensing terms for individual content resources and/or compilations of content resources in the content server 112, the rights holders for the content resources, and/or common or large-scale right holder information such as contact information for rights holders of content not included in the content server 112.
A content access data store 306 may include access rights and security information for the content distribution network 100 and specific content resources. For example, the content access data store 306 may include login information (e.g., user identifiers, logins, passwords, etc.) that can be verified during user login attempts to the network 100. The content access data store 306 also may be used to store assigned user roles and/or user levels of access. For example, a user's access level may correspond to the sets of content resources and/or the client or server applications that the user is permitted to access. Certain users may be permitted or denied access to certain applications and resources based on their subscription level, training program, course/grade level, etc. Certain users may have supervisory access over one or more end users, allowing the supervisor to access all or portions of the end user's content, activities, evaluations, etc. Additionally, certain users may have administrative access over some users and/or some applications in the content management network 100, allowing such users to add and remove user accounts, modify user access permissions, perform maintenance updates on software and servers, etc.
A source data store 307 may include information relating to the source of the content resources available via the content distribution network. For example, a source data store 307 may identify the authors and originating devices of content resources, previous pieces of data and/or groups of data originating from the same authors or originating devices, and the like.
An evaluation data store 308 may include information used to direct the evaluation of users and content resources in the content management network 100. In some embodiments, the evaluation data store 308 may contain, for example, the analysis criteria and the analysis guidelines for evaluating users (e.g., trainees/students, gaming users, media content consumers, etc.) and/or for evaluating the content resources in the network 100. The evaluation data store 308 also may include information relating to evaluation processing tasks, for example, the identification of users and user devices 106 that have received certain content resources or accessed certain applications, the status of evaluations or evaluation histories for content resources, users, or applications, and the like. Evaluation criteria may be stored in the evaluation data store 308 including data and/or instructions in the form of one or several electronic rubrics or scoring guides for use in the evaluation of the content, users, or applications. The evaluation data store 308 also may include past evaluations and/or evaluation analyses for users, content, and applications, including relative rankings, characterizations, explanations, and the like.
In addition to the illustrative data stores described above, data store server(s) 104 (e.g., database servers, file-based storage servers, etc.) may include one or more external data aggregators 309. External data aggregators 309 may include third-party data sources accessible to the content management network 100, but not maintained by the content management network 100. External data aggregators 309 may include any electronic information source relating to the users, content resources, or applications of the content distribution network 100. For example, external data aggregators 309 may be third-party data stores containing demographic data, education related data, consumer sales data, health related data, and the like. Illustrative external data aggregators 309 may include, for example, social networking web servers, public records data stores, learning management systems, educational institution servers, business servers, consumer sales data stores, medical record data stores, etc. Data retrieved from various external data aggregators 309 may be used to verify and update user account information, suggest user content, and perform user and content evaluations.
With reference now to FIG. 4, a block diagram is shown illustrating an embodiment of one or more content management servers 102 within a content distribution network 100. As discussed above, content management server(s) 102 may include various server hardware and software components that manage the content resources within the content distribution network 100 and provide interactive and adaptive content to users on various user devices 106. For example, content management server(s) 102 may provide instructions to and receive information from the other devices within the content distribution network 100, in order to manage and transmit content resources, user data, and server or client applications executing within the network 100.
A content management server 102 may include a content customization system 402. The content customization system 402 may be implemented using dedicated hardware within the content distribution network 100 (e.g., a content customization server 402), or using designated hardware and software resources within a shared content management server 102. In some embodiments, the content customization system 402 may adjust the selection and adaptive capabilities of content resources to match the needs and desires of the users receiving the content. For example, the content customization system 402 may query various data stores and servers 104 to retrieve user information, such as user preferences and characteristics (e.g., from a user profile data store 301), user access restrictions to content recourses (e.g., from a content access data store 306), previous user results and content evaluations (e.g., from an evaluation data store 308), and the like. Based on the retrieved information from data stores 104 and other data sources, the content customization system 402 may modify content resources for individual users.
A content management server 102 also may include a user management system 404. The user management system 404 may be implemented using dedicated hardware within the content distribution network 100 (e.g., a user management server 404), or using designated hardware and software resources within a shared content management server 102. In some embodiments, the user management system 404 may monitor the progress of users through various types of content resources and groups, such as media compilations, courses or curriculums in training or educational contexts, interactive gaming environments, and the like. For example, the user management system 404 may query one or more databases and/or data store servers 104 to retrieve user data such as associated content compilations or programs, content completion status, user goals, results, and the like.
A content management server 102 also may include an evaluation system 406. The evaluation system 406 may be implemented using dedicated hardware within the content distribution network 100 (e.g., an evaluation server 406), or using designated hardware and software resources within a shared content management server 102. The evaluation system 406 may be configured to receive and analyze information from user devices 106. For example, various ratings of content resources submitted by users may be compiled and analyzed, and then stored in a data store (e.g., a content library data store 303 and/or evaluation data store 308) associated with the content. In some embodiments, the evaluation server 406 may analyze the information to determine the effectiveness or appropriateness of content resources with, for example, a subject matter, an age group, a skill level, or the like. In some embodiments, the evaluation system 406 may provide updates to the content customization system 402 or the user management system 404, with the attributes of one or more content resources or groups of resources within the network 100. The evaluation system 406 also may receive and analyze user evaluation data from user devices 106, supervisor devices 110, and administrator servers 116, etc. For instance, evaluation system 406 may receive, aggregate, and analyze user evaluation data for different types of users (e.g., end users, supervisors, administrators, etc.) in different contexts (e.g., media consumer ratings, trainee or student comprehension levels, teacher effectiveness levels, gamer skill levels, etc.).
A content management server 102 also may include a content delivery system 408. The content delivery system 408 may be implemented using dedicated hardware within the content distribution network 100 (e.g., a content delivery server 408), or using designated hardware and software resources within a shared content management server 102. The content delivery system 408 may receive content resources from the content customization system 402 and/or from the user management system 404, and provide the resources to user devices 106. The content delivery system 408 may determine the appropriate presentation format for the content resources based on the user characteristics and preferences, and/or the device capabilities of user devices 106. If needed, the content delivery system 408 may convert the content resources to the appropriate presentation format and/or compress the content before transmission. In some embodiments, the content delivery system 408 may also determine the appropriate transmission media and communication protocols for transmission of the content resources.
In some embodiments, the content delivery system 408 may include specialized security and integration hardware 410, along with corresponding software components to implement the appropriate security features content transmission and storage, to provide the supported network and client access models, and to support the performance and scalability requirements of the network 100. The security and integration layer 410 may include some or all of the security and integration components 208 discussed above in FIG. 2, and may control the transmission of content resources and other data, as well as the receipt of requests and content interactions, to and from the user devices 106, supervisor devices 110, administrative servers 116, and other devices in the network 100.
With reference now to FIG. 5, a block diagram of an illustrative computer system is shown. The system 500 may correspond to any of the computing devices or servers of the content distribution network 100 described above, or any other computing devices described herein. In this example, computer system 500 includes processing units 504 that communicate with a number of peripheral subsystems via a bus subsystem 502. These peripheral subsystems include, for example, a storage subsystem 510, an I/O subsystem 526, and a communications subsystem 532.
Bus subsystem 502 provides a mechanism for letting the various components and subsystems of computer system 500 communicate with each other as intended. Although bus subsystem 502 is shown schematically as a single bus, alternative embodiments of the bus subsystem may utilize multiple buses. Bus subsystem 502 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. Such architectures may include, for example, an Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus, which can be implemented as a Mezzanine bus manufactured to the IEEE P1386.1 standard.
Processing unit 504, which may be implemented as one or more integrated circuits (e.g., a conventional microprocessor or microcontroller), controls the operation of computer system 500. One or more processors, including single core and/or multicore processors, may be included in processing unit 504. As shown in the figure, processing unit 504 may be implemented as one or more independent processing units 506 and/or 508 with single or multicore processors and processor caches included in each processing unit. In other embodiments, processing unit 504 may also be implemented as a quad-core processing unit or larger multicore designs (e.g., hexa-core processors, octo-core processors, ten-core processors, or greater.
Processing unit 504 may execute a variety of software processes embodied in program code, and may maintain multiple concurrently executing programs or processes. At any given time, some or all of the program code to be executed can be resident in processor(s) 504 and/or in storage subsystem 510. In some embodiments, computer system 500 may include one or more specialized processors, such as digital signal processors (DSPs), outboard processors, graphics processors, application-specific processors, and/or the like.
I/O subsystem 526 may include device controllers 528 for one or more user interface input devices and/or user interface output devices 530. User interface input and output devices 530 may be integral with the computer system 500 (e.g., integrated audio/video systems, and/or touchscreen displays), or may be separate peripheral devices which are attachable/detachable from the computer system 500.
Input devices 530 may include a keyboard, pointing devices such as a mouse or trackball, a touchpad or touch screen incorporated into a display, a scroll wheel, a click wheel, a dial, a button, a switch, a keypad, audio input devices with voice command recognition systems, microphones, and other types of input devices. Input devices 530 may also include three dimensional (3D) mice, joysticks or pointing sticks, gamepads and graphic tablets, and audio/visual devices such as speakers, digital cameras, digital camcorders, portable media players, webcams, image scanners, fingerprint scanners, barcode reader 3D scanners, 3D printers, laser rangefinders, and eye gaze tracking devices. Additional input devices 530 may include, for example, motion sensing and/or gesture recognition devices that enable users to control and interact with an input device through a natural user interface using gestures and spoken commands, eye gesture recognition devices that detect eye activity from users and transform the eye gestures as input into an input device, voice recognition sensing devices that enable users to interact with voice recognition systems through voice commands, medical imaging input devices, MIDI keyboards, digital musical instruments, and the like.
Output devices 530 may include one or more display subsystems, indicator lights, or non-visual displays such as audio output devices, etc. Display subsystems may include, for example, cathode ray tube (CRT) displays, flat-panel devices, such as those using a liquid crystal display (LCD) or plasma display, light-emitting diode (LED) displays, projection devices, touch screens, and the like. In general, use of the term “output device” is intended to include all possible types of devices and mechanisms for outputting information from computer system 500 to a user or other computer. For example, output devices 530 may include, without limitation, a variety of display devices that visually convey text, graphics and audio/video information such as monitors, printers, speakers, headphones, automotive navigation systems, plotters, voice output devices, and modems.
Computer system 500 may comprise one or more storage subsystems 510, comprising hardware and software components used for storing data and program instructions, such as system memory 518 and computer-readable storage media 516. The system memory 518 and/or computer-readable storage media 516 may store program instructions that are loadable and executable on processing units 504, as well as data generated during the execution of these programs.
Depending on the configuration and type of computer system 500, system memory 318 may be stored in volatile memory (such as random access memory (RAM) 512) and/or in non-volatile storage drives 514 (such as read-only memory (ROM), flash memory, etc.) The RAM 512 may contain data and/or program modules that are immediately accessible to and/or presently being operated and executed by processing units 504. In some implementations, system memory 518 may include multiple different types of memory, such as static random access memory (SRAM) or dynamic random access memory (DRAM). In some implementations, a basic input/output system (BIOS), containing the basic routines that help to transfer information between elements within computer system 500, such as during start-up, may typically be stored in the non-volatile storage drives 514. By way of example, and not limitation, system memory 518 may include application programs 520, such as client applications, Web browsers, mid-tier applications, server applications, etc., program data 522, and an operating system 524.
Storage subsystem 510 also may provide one or more tangible computer-readable storage media 516 for storing the basic programming and data constructs that provide the functionality of some embodiments. Software (programs, code modules, instructions) that when executed by a processor provide the functionality described herein may be stored in storage subsystem 510. These software modules or instructions may be executed by processing units 504. Storage subsystem 510 may also provide a repository for storing data used in accordance with the present invention.
Storage subsystem 300 may also include a computer-readable storage media reader that can further be connected to computer-readable storage media 516. Together and, optionally, in combination with system memory 518, computer-readable storage media 516 may comprehensively represent remote, local, fixed, and/or removable storage devices plus storage media for temporarily and/or more permanently containing, storing transmitting, and retrieving computer-readable information.
Computer-readable storage media 516 containing program code, or portions of program code, may include any appropriate media known or used in the art, including storage media and communication media, such as but not limited to, volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage and/or transmission of information. This can include tangible computer-readable storage media such as RAM, ROM, electronically erasable programmable ROM (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disk (DVD), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other tangible computer readable media. This can also include nontangible computer-readable media, such as data signals, data transmissions, or any other medium which can be used to transmit the desired information and which can be accessed by computer system 500.
By way of example, computer-readable storage media 516 may include a hard disk drive that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive that reads from or writes to a removable, nonvolatile magnetic disk, and an optical disk drive that reads from or writes to a removable, nonvolatile optical disk such as a CD ROM, DVD, and Blu-Ray® disk, or other optical media. Computer-readable storage media 516 may include, but is not limited to, Zip® drives, flash memory cards, universal serial bus (USB) flash drives, secure digital (SD) cards, DVD disks, digital video tape, and the like. Computer-readable storage media 516 may also include, solid-state drives (SSD) based on non-volatile memory such as flash-memory based SSDs, enterprise flash drives, solid state ROM, and the like, SSDs based on volatile memory such as solid state RAM, dynamic RAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, and hybrid SSDs that use a combination of DRAM and flash memory based SSDs. The disk drives and their associated computer-readable media may provide non-volatile storage of computer-readable instructions, data structures, program modules, and other data for computer system 500.
Communications subsystem 532 may provide a communication interface from computer system 500 and external computing devices via one or more communication networks, including local area networks (LANs), wide area networks (WANs) (e.g., the Internet), and various wireless telecommunications networks. As illustrated in FIG. 5, the communications subsystem 532 may include, for example, one or more network interface controllers (NICs) 534, such as Ethernet cards, Asynchronous Transfer Mode NICs, Token Ring NICs, and the like, as well as one or more wireless communications interfaces 536, such as wireless network interface controllers (WNICs), wireless network adapters, and the like. Additionally and/or alternatively, the communications subsystem 532 may include one or more modems (telephone, satellite, cable, ISDN), synchronous or asynchronous digital subscriber line (DSL) units, FireWire® interfaces, USB® interfaces, and the like. Communications subsystem 536 also may include radio frequency (RF) transceiver components for accessing wireless voice and/or data networks (e.g., using cellular telephone technology, advanced data network technology, such as 3G, 4G or EDGE (enhanced data rates for global evolution), WiFi (IEEE 802.11 family standards, or other mobile communication technologies, or any combination thereof), global positioning system (GPS) receiver components, and/or other components.
The various physical components of the communications subsystem 532 may be detachable components coupled to the computer system 500 via a computer network, a FireWire® bus, or the like, and/or may be physically integrated onto a motherboard of the computer system 500. Communications subsystem 532 also may be implemented in whole or in part by software.
In some embodiments, communications subsystem 532 may also receive input communication in the form of structured and/or unstructured data feeds, event streams, event updates, and the like, on behalf of one or more users who may use or access computer system 500. For example, communications subsystem 532 may be configured to receive data feeds in real-time from users of social networks and/or other communication services, web feeds such as Rich Site Summary (RSS) feeds, and/or real-time updates from one or more third party information sources (e.g., data aggregators 309). Additionally, communications subsystem 532 may be configured to receive data in the form of continuous data streams, which may include event streams of real-time events and/or event updates (e.g., sensor data applications, financial tickers, network performance measuring tools, clickstream analysis tools, automobile traffic monitoring, etc.). Communications subsystem 532 may output such structured and/or unstructured data feeds, event streams, event updates, and the like to one or more data stores 104 that may be in communication with one or more streaming data source computers coupled to computer system 500.
Due to the ever-changing nature of computers and networks, the description of computer system 500 depicted in the figure is intended only as a specific example. Many other configurations having more or fewer components than the system depicted in the figure are possible. For example, customized hardware might also be used and/or particular elements might be implemented in hardware, firmware, software, or a combination. Further, connection to other computing devices, such as network input/output devices, may be employed. Based on the disclosure and teachings provided herein, a person of ordinary skill in the art will appreciate other ways and/or methods to implement the various embodiments.
With reference now to FIG. 6, a block diagram is shown illustrating an example of a digital credential management system 600 for generating, managing, and tracking digital credential templates and digital credentials. As shown in this example, a digital credential management system 600 may include a digital credential platform server 610 configured to communicate with various other digital credential systems 620-680. As discussed below, the digital credential platform server 610 may receive and store digital credential templates from various digital credential template owner systems 620. Systems 620 may correspond to the computer servers and/or devices of educational institutions or professional training organizations, which may have the primary responsibility for defining a digital credential template and controlling the content and requirements for users to receive a digital credential from the organization. The digital credential management system 600 may include one or more digital credential issuer systems 630. As discussed below, each issuer system 630 may communicate with the platform server to request and receive access to issue digital credentials based on specific digital credential templates. The platform server 610 may process template access requests from the credential issuer systems 630, permitting or denying a specific system 630 to generate (or issue) a digital credential based on a specific digital credential template.
As used herein, a digital credential template (or digital badge template) may refer to an electronic document or data structure storing a general (e.g., non-user specific) template or description of a specific type of digital credential that may be issued to an individual. Digital credential templates may include, for example, a description of the skills, proficiencies, and/or achievements that the digital credential represents. This description may take the form of diploma data, certification data, and/or license data, including the parent organization (i.e., the digital credential template owner) responsible for creating and defining the digital credential template. Examples of digital credential templates may include templates for various technology certifications, licensure exams, professional tests, training course completion certificates, and the like. In contrast to a digital credential template, a digital credential (or digital badge) may refer to an instance of an electronic document or data structure, generated for a specific individual (i.e., the credential receiver), and based on a digital credential template. Thus, a digital credential document or data structure may be based on a corresponding digital credential template, but may be customized and populated with user-specific information such as individual identification data (e.g., name, email address, and other user identifiers), credential issuance data (e.g., issue date, geographic location of issuance, authorized issuer of the credential, etc.), and links or embedded data that contain the specific user's supporting documentation or evidence relating to the credential.
As shown in this example, the system 600 also may include a digital credential receiver system 640 and a digital credential endorser system 650. The digital credential receiver system 640 may be a computing device associated with a credential receiver (or credential earner), for example, an individual user of an electronic learning system, professional training system, online certification course, etc. In some embodiments, credential receivers may access the platform server 610 via systems 640 to accept or reject newly issued digital credentials, review and update their own set of previously earned digital credentials, as well as to publish (or share) their digital credentials via communication applications or publishing platforms such as social media systems. Digital credential endorser system 650 may be a computing system associated with an endorsing entity, such as an educational institution, business, or technical organization that has chosen to review and endorse a specific digital credential template. The platform server 610 may receive and track the endorsements received from systems 650, and may associate the endorsements with the user-specific digital credentials issued based on the endorsed templates.
Additionally, the digital credential management system 600 in this example includes a number of external client devices 660 and external digital credential publishers 670. External client devices 660 may correspond to computing systems of third-party users that may interact with the platform server 610 to initiate various functionality or retrieve data relating to templates and/digital credentials managed by the platform 610. For example, a client device 660 may query the platform server 610 for data metrics and/or analyses relating to a subset of digital credentials stored in the digital credential data store 615. The third-party systems 660 also may provide data to the platform server 610 that may initiate updates to the templates and/digital credentials stored in the data store 615. External digital credential publishers 670 may correspond to third-party systems configured to receive digital credential data from the platform 610 and publish (or present) the digital credential data to users. Examples of publishers 670 may include social media website and systems, digital badge wallets, and/or other specialized servers or applications configured to store and present views of digital badges to users.
In various embodiments described herein, the generation and management of digital credentials, as well as the tracking and reporting of digital credential data, may be performed within CDNs 100, such as eLearning, professional training, and certification systems 100. For example, within the context of an eLearning CDN 100, a content management server 102 or other CDN server (e.g., 104, 112, 114, or 116) may create and store digital credential templates to describe and define various proficiencies, achievements, or certifications supported by the eLearning CDN 100. Additionally or alternatively, the content management server 102 or other servers of an eLearning CDN 100 may issue digital credentials to users, based on its own digital certificate templates and/or templates received from other systems or CDNs. Further, in some implementations, an eLearning CDN 100 may be configured to include a digital credential platform server 610 to store and manage templates and digital credentials between separate systems within the CDN 100. Thus, in various different implementations, the content management server(s) 102 of a CDN 100 may incorporate one or more digital certificate template owner system(s) 620, digital certificate issuer system(s) 630, and/or digital certificate platform server(s) 610. In such embodiments, the various components and functionalities described herein for the platform server 610, owner system 620, and/or issuer system 630 all may be implemented within one or more content management servers 102 (and/or other servers) of an eLearning or professional training CDN 100. In other examples, a digital credential platform server 610 may be implemented using one or more computer servers, and other specialized hardware and software components, separately from any other CDN components such as content servers 112, content management servers 102, data store servers 104, and the like. In these examples, the digital credential platform server 610 may be configured to communicate directly with related systems 620-670, or indirectly through content management servers 102 and/or other components and communications networks of the CDN 100.
In order to perform these features and other functionality described herein, each of the components and sub-components discussed in the example digital credential management system 600 may correspond to a single computer server or a complex computing system including a combination of computing devices, storage devices, network components, etc. Each of these components and their respective subcomponents may be implemented in hardware, software, or a combination thereof. Certain systems 620-670 may communicate directly with the platform server 610, while other systems 620-670 may communicate with the platform server 610 indirectly via one or more intermediary network components (e.g., routers, gateways, firewalls, etc.) or other devices (e.g., content management servers 102, content servers 112, etc.). Although the different communication networks and physical network components have not been shown in this example so as not to obscure the other elements depicted in the figure, it should be understood that any of the network hardware components and network architecture designs may be implemented in various embodiments to support communication between the systems, servers, and devices in the digital credential management system 600. Additionally, different systems 620-670 may use different networks and networks types to communicate with the platform server 610, including one or more telecommunications networks, cable networks, satellite networks, cellular networks and other wireless networks, and computer-based IP networks, and the like. Further, certain components within the digital credential management system 600 may include special purpose hardware devices and/or special purpose software, such as those included in I/O subsystem 611 and memory 614 of the platform server 610, as well as those within the memory of the other systems 620-670, and the digital credential data store 615 maintained by the platform server 610, discussed below.
Although the various interactions between the platform server 610 and other systems 620-670 may be described below in terms of a client-server model, it should be understood that other computing environments and various combinations of servers and devices may be used to perform the functionality described herein in other embodiments. For instance, although the requests/responses to determine the authorized issuers 630 for specific digital credential templates, the generation of digital credentials, and the retrieval and presentation of digital credential tracking and reporting data, may be performed by a centralized web-based platform server 610 in collaboration with various client applications at the other systems 620-670 (e.g., web browser applications or standalone client software), in other cases these techniques may be performed entirely by a specialized digital credential platform server 610, or entirely by one or more digital credential tools (e.g., software services) executing on any one of the systems 620-670. In other examples, a client-server model may be used as shown in system 600, but different functional components and processing tasks may be allocated to the client-side or the sever-side in different embodiments. Additionally, the digital credential data store 615 may be implemented as separate servers or storage systems in some cases, and may use independent hardware and software service components. However, in other implementations, some or all of the digital credential data store 615 may be incorporated into the platform server 610 (as shown in this example) and/or may be incorporated into various other systems 620-670.
In some embodiments, each of the systems 620-670 that collaborate and communicate with the platform server 610 may be implemented as client computing systems, such desktop or laptop computers, smartphones, tablet computers, and other various types of computing devices, each of which may include some or all of the hardware, software, and networking components discussed above. Specifically, any of client systems 620-670 may be implemented using any computing device with sufficient processing components, memory and software components, and I/O system components for interacting with users and supporting the desired set of communications with the platform server 610, as described herein. Accordingly, client systems 620-670 may include the necessary hardware and software components to establish the network interfaces, security and authentication capabilities, and capabilities for transmitting/receiving digital credential templates and digital credentials, digital credential data requests/responses to the platform server 610, etc. Each client system 620-670 may include an I/O subsystem, network interface controller, a processing unit, and memory configured to operate client software applications. The digital credential platform server 610 may be configured to receive and execute various programmatic and graphical interfaces for generating, managing, and tracking issued digital credentials, in collaboration with the various client systems 620-670. Accordingly, each client system 620-670 may include an I/O subsystem 611 having hardware and software components to support a specific set of output capabilities (e.g., LCD display screen characteristics, screen size, color display, video driver, speakers, audio driver, graphics processor and drivers, etc.), and a specific set of input capabilities (e.g., keyboard, mouse, touchscreen, voice control, cameras, facial recognition, gesture recognition, etc.). Different client systems 620-670 may support different input and output capabilities within their I/O subsystems, and thus different types of user interactions, and platform server 610 functionality may be compatible or incompatible with certain client systems 620-670. For example, certain types of digital credential generation and search functionality may require specific types of processors, graphics components, network components, or I/O components in order to be optimally designed and constructed using a client system 620-670.
In some embodiments, the digital credential platform server 610 may generate and provide software interfaces (e.g., via a web-based application, or using other programmatic or graphical interface techniques) used by the various client systems 620-670 to perform the various digital credential management functionality described herein. In response to receiving inputs from a client system 620-670 corresponding to digital credentials, templates, credential search requests and criteria, etc., the platform server 610 may access the underlying digital credential data store 615 perform the various functionality described herein. In other to perform the tasks described herein, platform server 610 may include components such as network interface controllers 612, processing units 613, and memory 614 configured to store server software, handle authentication and security, and to store, analyze, and manage the digital credentials, templates, and credential tracking data stored within the digital credential data store 615. As shown in this example, the digital credential data store 615 may be implemented as separate dedicated data stores (e.g., databases, file-based storage, etc.) used for storing digital credential template objects, issued digital credentials, credential tracking data, and authorized user/role data. The platform server 610 and data store 615 may be implemented as separate software (and/or storage) components within a single computer server 610 in some examples, while in other examples may be implemented as separate computer servers/systems having separate dedicated processing units, storage devices, and/or network components.
Certain aspects described herein related to the testing and certification processes used to verify the skills or qualifications that a user (or earner) has obtained in order to be awarded with a digital credential (or badge) or any other skill certification from an institution or credentialing body. In some embodiments, physical testing environments including “simulation laboratories” may use implemented to allow users to perform physical tasks (including mental and/or computer-based tasks) in a monitored environment. Such physical testing environments may use virtual reality and/or augmented reality in various cases. The simulation lab and/or the user may be monitored by various sensors during testing or certification processes, and the results may be analyzed to determine (at least in part) whether or not the user should be awarded a particular digital credential or certification. As discussed below in more detail, simulation labs may be implemented as testing environments for manual tasks, computer-based tasks, scenario training, etc., and various monitoring of the simulation lab environment during test may provide data metrics relating to successful completion of tasks, efficiency of task completion, user response times, user decision making behaviors, user biometrics and risk factors, etc. Further, as discussed below, certain simulation labs may provide the ability to change testing scenarios as well as environmental conditions (lighting noise, temperature, etc.) during testing.
Referring now to FIG. 7, an example is shown of a physical testing environment that may be used for badge testing, skills certification, and other behavior monitoring and digital credential generation, in accordance with certain aspects described herein. In this example, a basic testing environment 700 is shown to illustrate certain features and concepts that may be included in various embodiments. Depending on the particular digital credential, activity, skill or ability to be verified, different devices and components may be included in the simulation environments 700. For example, simulation environments 700 for standardized testing and completion of computer-based tasks may be setup to simulate an office environment, for instance, with a computer, keyboard, monitor, desk and chair, etc. Other testing environments 700 designed for other badges and/or skills certifications may be configured differently. For instance, testing environments 700 may be configured as a driving simulator (e.g., having front and side display screens, an installed automobile seat with steering wheel, pedals, vehicle controls and gauges, simulated mirror displays, etc.), or a flight simulator (e.g., having front and side display screens, up and down fields of vision, a pilot seat with a center stick and/or other airplane controls and gauges, etc.). Other testing environments 700 might not require or have any display screens, for example testing environments 700 for CPR certification may include one or more CPR manikins and other accessories to test CPR scenarios. Additional testing environments 700 may be implemented for law enforcement use of force or defensive tactics scenarios (with or with display screens, with or without live firearms capabilities, etc.). Still other testing environments 700 may be implemented for skills testing and verification on machine assembly tasks, and/or on machine use tasks. The machines in testing environments 700 in such scenarios may range from simple to complex, to allow users to any testable task on any machine, from bicycle assembly, to automobile maintenance, to semiconductor design, to electrical work, to laser fabrication, to welding. Other testing environments 700 may be implemented for skills testing and verification in performance of medical or dental procedures, and the like, and thus may resemble a hospital operating room or dentist office with a full complement of medical tools and devices necessary to perform the tasks to be verified. Still other testing environments 700 may be configured to test/verify skills with respect to sports or other physical activities, and thus the testing environments 700 may comprise a dance studio, gymnastics apparatus, golf driving range, or other sports equipment. For each of these examples, and many others, it should be understood that the different configuration of testing environments 700 may require different sets of testing equipment, as well as different monitoring and environmental control features. Further, although many examples and implementations described herein refer to human users as the subjects of testing and simulation scenarios, in some cases the test subjects may include mechanical devices (e.g., machines configured to assemble parts), artificial intelligences and/or other software programs configured to perform certain tasks, etc.
In addition to the testing equipment and apparatuses in the physical testing environment 700, the environment may have cameras 705 and sensors configured to monitor the performance and behavior of the user during the testing. As shown in this example, a number of cameras 705 may be installed throughout the testing environment 700 to capture image/video data of the user from different angles during the testing/skills verification process. In addition to cameras, in various embodiments (depending on the type of test or skill being evaluated), additional sensors may be deployed within the testing environment 700, including microphones, light sensors, heat sensors, vibration sensors, and any other sensor type, depending on the type of testing/evaluation being performed. For instance, for testing of computer-based tasks, additional sensors such as mouse movement trackers, keystroke loggers, and user eye-tracking software may be used. For machine usage tasks, scenario training, and the like, movement sensors may be placed on the user and/or on any objects with which the user may interact during the testing scenario. Additionally, for any testing or skills evaluation scenario, certain embodiments may include biometric sensors and devices 710 configured to detect and track the user's biometric data during the testing process. Such biometric sensors and devices may measure the user's temperature, heartrate, blood pressure, respiration, skin conductivity, body movements, brainwave activities, etc.
In some embodiments, the physical testing environment 700 also may include various environmental controls that allow a test administrator to control the physical environmental conditions during a test or skills evaluation. Such environmental controls may include lights 715 that allow the test administrator to control the light levels, angles, and/or colors during a test. By way of example, lighting control within the environment 700 may allow the test administrator to evaluate the user's ability to perform a driving maneuver or roadside maintenance task at night, etc. Additional environmental controls may include may include temperature controls, weather simulation (e.g., wind, rain, snow, sunshine, fog, etc.), speakers to provide background noise or distraction, olfactory control that provides scents/odors to simulate the smells that be present during a comparable real-life scenario, vibration control to simulate the activity, and so on.
Referring now to FIG. 8, a flow diagram is shown illustrating an example process of executing tests or simulations, as well as monitoring and analyzing the results of the tests or simulations. As described below, the steps in this process may be performed using various components of a simulation lab and/or other physical test (or simulation) environment 700, described above. For example, each of steps 801-810 may be performed by a computer server of a test administrator associated with a physical simulation environment 700. In other examples, physical simulation environments 700 might be configured to receive test content and configuration parameters, to execute the tests and monitor the execution, and then to transmit the test results and related observation data to a separate server (e.g., a digital credential platform server 610) for scoring and analysis.
In step 801, a computer server controlling the physical testing environment 700 may receive input relating to the test or skills evaluation scenario to be executed within the physical testing environment 700. In step 802, the server may receive data identifying the particular user designated to complete the test or skills evaluation scenario.
In step 803, the server may retrieve the test or scenario to be loaded/executed within the physical testing environment 700. As noted above, the test or scenario may include interactive user software (e.g., driving or flight simulator programs, law enforcement scenarios, etc.) and/or may include testing software or other software programs loaded onto a desktop, laptop, or tablet computer. For instance, the test or scenario may require the user to work with computer-aided design software, spreadsheet software, database development software, etc. In other cases, the test or scenario may include audio and/or video files to be played via speakers and/or display screens within the physical testing environment 700, such as instructional videos or audio/visual test questions.
The test or scenario retrieved in step 803 also may be retrieved based on the identity of the particular user who will be completing the test or skills evaluation scenario. In some embodiments, the server of the physical testing environment 700 may be configured to select the appropriate test, scenario, and/or simulation (e.g., a particular software scenario, skill level, etc.) based on the user's current set of badges or digital credentials, the user's skill level, and/or the user's performance history on previous tests or scenarios within the testing environment 700. Additionally, in some cases, the server may vary scenarios/test questions so that a particular user does not receive the same test questions, scenarios, or other testing content that they have already completed (or completed within a particular recent time window).
In step 804, the server may determine and apply a set of environmental conditions within the physical testing environment 700 for the execution of the test or scenario. As noted above, the physical testing environment 700 in some embodiments may be capable of setting various environment conditions such as lighting (e.g., to simulate different day or night, and/or different real-world working environments), temperature and weather conditions (e.g., to simulate outdoor scenarios, different seasons and locations), noise (e.g., to provide background noise, traffic noise, distractions, etc.) and other various environment conditions. The server may select and apply environmental conditions as part of the test or scenario selected in step 803, or as a separate determination which is performed based on random chance or selected by a test administrator, etc. For instance, for certain types of badges and other certifications, separate day and night testing of certain tasks may be required. In other cases, the environmental conditions may be selected randomly and changed for each testing session. In still other cases, user may select and/or save their preferred environmental conditions for different types of testing. Further, in some embodiments, the physical testing environment 700 may track and analyze the user's various testing or scenario performance metrics (e.g., accuracy, efficiency, safety, compliance, biometrics, etc.) under different environmental conditions, in order to determine the optimal environmental conditions for the particular user. In such cases, user's may receive different badges or certifications (or may have different badge assigned characteristics or endorsements) based on their test or scenario performance in different environmental conditions.
In step 805, the computer server(s) associated with the physical testing environment 700 may execute the test or simulation scenario, during which the user's performance and any/all user reactions or responses may be monitored. As noted above, even for certain tests that are entirely manual in nature, the physical testing environment 700 may use cameras and any other sensors to monitor the user's actions. Such monitoring may include various aspects of the user's performance, such as answers to test questions selected via a testing computer terminal, or the user's interactions with physical objects (and/or other people) within the physical testing environment 700. The user's answers and actions may be recorded by cameras and computer input devices, and additional user data may be collected using various other sensors such as microphones, biometric sensors, etc.
In step 806, the results for the test and/or simulation scenario completed by the user may be analyzed. In some embodiments, the such analyses may be performed based not only on the user's responses to particular test questions or scenarios. Additionally or alternatively, the analysis in step 806 may include an evaluation of the user's other reactions or responses, such as speed and confidence of action (e.g., as determined by user comments, speed of response, facial expression analysis, body movement analysis, biometric data, etc.), efficiency, safety, decision making, and user biometrics. One or more of these separate analyses may be performed in steps 807-810, and each may be performed independently of the others, or may be combined into a single analysis. For instance, in some cases the goal of the simulation might be only to measure the user's biometric data, and the user's actual responses to the questions/scenarios may be irrelevant and need not be evaluated in step 807. In other tests or simulation scenarios, the opposite analysis may be applied, where only the accuracy of the user's responses or behaviors are measured and analyzed in step 807, and the user's biometric data is irrelevant and thus the analysis in step 810 is not performed. As another example, in a certain simulation of driving, machine operation, use of force training, etc., the only relevant analysis to be performed may be a safety/decision making analysis in step 809, while the efficiency analysis in step 808 need not be performed. In other similar tests/situations, the server may apply both a safety/decision making analysis in step 809 and an efficiency analysis in step 808 (e.g., to confirm that a driving maneuver or route was completed both safely and efficiently, to confirm that a suspect was subdued safely and quickly, to assure that a manufacturing assembly task was performed safely and efficiently, etc.).
For example, in certain embodiments and implementations of the concepts discussed above in reference to FIGS. 8-9, various techniques (e.g., systems, methods, computer-program products tangibly embodied in a non-transitory machine-readable storage medium, etc.) may include evaluating a physical simulation by a digital credential generator system (e.g., 630 and/or 610). Such techniques may include monitoring, by a digital credential generator system, a physical simulation area using a plurality of sensors, during a physical simulation. During the monitoring the digital credential generator system may detect, using the plurality of sensors, various physical actions performed by a user during the physical simulation. The digital credential generator system may analyze data corresponding to the plurality of physical actions performed by the user during the physical simulation, and determine that the user corresponds to a particular credential receiver. The digital credential generator system then may determine whether the first credential receiver is eligible to receive a digital credential, by comparing the data corresponding to the analysis of the physical actions performed by the user during the physical simulation, to one or more digital credential requirements. Finally, the digital credential generator system may generate a first digital credential associated with the first credential receiver and with the digital credential requirements, in response to determining that the first credential receiver is eligible to receive the first digital credential.
In some embodiments, outputting a physical simulation may include outputting audio and/or video simulation components within the physical simulation area, manipulating physical objects (e.g., motorized objects) during a live-action simulation within the physical simulation area, and/or outputting virtual reality simulations via a virtual reality headset. Additionally, certain embodiments may include generating physical simulation environments within a physical simulation area, including, for example, simulating ambient light conditions within the physical simulation area, outputting one or more background noise conditions within the physical simulation area, monitoring and controlling the physical temperature using a heating and cooling system installed at the physical simulation area, outputting smells to the physical simulation area using a smell output device, and/or outputting vibratory effect within the physical simulation area, using a vibration system.
As noted above, the monitoring of a test/simulation may include monitoring physical actions/activities performed by the user using video recording devices and/or motions. Additionally or alternatively, the monitoring may be of computer-based tasks, using additional software-based sensors such as mouse movement trackers, keystroke loggers, and user eye-tracking software, etc.
In accordance with certain aspects described herein, the processes used for testing/evaluating a user and determining that a user is eligible for a particular digital credential (or badge) need not include a specific test, designated evaluation, or scored scenario training. Rather, the testing and badging determinations may be performed automatically during the user's normal course of on-the-job performance of tasks. In such embodiments, the testing and credentialing of users may be based on observation of workers during their normal work activities. Cameras and other sensors may be installed and used to detect the completion of tasks and/or certain competencies of the users, and the data from these sensors may be evaluated to automatically determine when the user is eligible for a digital credential. Thus, on the job testing and badging may be performed entirely transparently to the worker performance of their job duties, and need not require any delay or distraction from job performance, or any designated time or location needed to perform formal testing.
In order to perform automatic and on-the-job testing and credentialing of workers or other users (e.g., students, athletes, etc.), the “work” environment of the user may be monitored with cameras and/or sensors capable of tracking the user's activities and performance. As discussed above with respect to the implementation of physical testing environments (e.g., 700), different types of digital credentials relate to different activities that may be performed in a variety of different work environments. Referring briefly to FIG. 9A, an example work environment 900 a is shown for a user completing computer-based tasks. In this example, the work environment 900 a may include a basic workstation, server, modem, printer, monitor, keyboard, etc., as well as desk and chair to allow the user to complete normal computer-based work activities. In this example, the user may be data entry specialist, computer programmer or design engineer, call center customer support operator, or may be performing any other computer-based job. In such examples, sensors 905 and 910 may include cameras, network monitoring devices, keystroke loggers, mouse movement monitors, biometric devices and sensors, etc. Such software tools may operate as background processes on a computer terminal being monitored. Additional monitoring devices may be built into specific software programs with which the user is interacting, and may be able to determine the correctness, quality, and efficiency of the user's interaction with the particular software. For example, if a user is interacting with a spreadsheet software application or computer-aided design application to perform a work task, then monitoring features within the software application may be used to determine how quickly the user performed the task, how many attempts it took the user, how correct/accurate was the finished product, etc. In other examples, the monitoring of the user's interaction with a particular software program need not involve any monitoring features within the software itself; but instead may include monitoring at the operating system or hardware layers, or monitoring that is entirely external to the workstation. For example, external cameras 905 and other sensors may capture and analyze the user's interactions with the software application, and thus need not affect the operation of the software at all.
Another example work environment is shown in FIG. 9B. In this example work environment 900 b, the entire layout of workplace floor is shown and monitored by a series of cameras 905 and/or other sensors. The monitoring in this example may apply to works who do not perform only computer-based tasks, but whose work requires them to interact with physical objects within their workspace, and/or to move around the work environment 900 b to other workspaces. For instance, maintenance works, office mail delivery works, construction workers, electricians, plumbers, machine assembly or manufacturing works, etc., may be monitored with such systems. When monitoring a larger area for the performance of non-computer-based work tasks, in addition to cameras 905, the work environment 900 b may include motion sensors, microphones and noise sensors, as wells as movement sensors and/or tracking devices that may be placed on specific physical objects within the environment. By way of example, work environment 900 b may correspond to a shop floor, mechanic's garage, or manufacturing assembly plant, and the cameras 905 and other sensors may be used to confirm that workers are complying with safety requirements and/or health codes with respect with their work with machinery or hazardous materials, etc. As another example, work environment 900 b may be an office environment, and the cameras 905 and other sensors may be used to confirm that individual workers are working efficiently, in their assigned areas, etc., and that workers without assigned areas (e.g., cleaning, mail delivery, maintenance workers, etc.) are working efficiently and not skipping any portion of the floor 900 b.
Referring now to FIG. 10, a flow diagram is shown illustrating an example process of automatically monitoring work activities and issuing digital credentials via “on-the-job” testing. As described below, the steps in this process may be performed by monitoring and credentialing computing devices operating within various types of work environments 900, such as those described above. For example, each of steps 1001-1006 may be performed by a computer server operating automatically and unassisted (or at the direction of an administrator) within a work environment 900. In other examples, work environments 900 might be configured only to monitor work activities and performance, and then to transmit the results and related observation data regarding various worker to a separate server (e.g., a digital credential platform server 610) for scoring, analysis, and the issuance of digital credentials.
In step 1001, a computer server controlling the on-the-job badging system may activate the cameras, sensors, monitoring software, etc., within the workstation and/or work environment. As discussed above, this activation may include specific monitoring software to detect computer-based tasks, and/or location monitoring devices such as cameras, sensors, biometrics, etc., depending on the type of workers and work environments 900 being monitored. In some cases, an on-the-job testing and credentialing system may be implemented as an “always on” system, in which the workstation/workplace monitoring is constantly recording and analyzing worker activities. Thus, step 1001 may be optional in such embodiments. However, in other cases, workstation/workplace monitoring might only be activated at certain times and not others, for example, only during normal work hours, only on certain specific work days designated for work evaluation, etc. In some embodiments, a system administrator and/or individual workers may activate or de-activate the workstation/workplace monitoring systems within their work environment at any time. Thus, such systems need not be an invasion of privacy for any worker that does not choose for their work to be monitored and evaluated, but workers may choose to turn the monitoring systems on in order to be eligible for evaluation and earning of additional work related digital credentials and credentials.
In step 1002, the workstation/workplace monitoring systems may capture the user's work-related activities and behaviors, including performing various computer-based tasks and non-computer-based tasks as discussed above. In step 1003, the user's working data as collected by the workstation/workplace monitoring systems and sensors may be analyzed by the server, in order to determine in step 1004 whether or not the user is eligible for one or more digital credentials or other credentials (e.g., professional certifications, etc.) based on their on-the job work activities. Certain digital credentials or credentials may be made available to users in response to detecting that the user has successful completed one or more specialized work tasks, thus demonstrating that the user has obtained the particular skill associated with the digital credential. In some cases, the server and/or the monitoring systems and sensors may also be configured to detect a certain level of efficiency by the user in performing the tasks, and/or may require that the user perform a certain task N number of times before the user is eligible for the digital credential or credential.
In step 1004, if the system determines that the user is eligible for one or more particular digital credentials (1004: Yes), then in step 1005 the system may either issue the digital credential directly (e.g., if the workplace server is permitted to be digital credential issuer), and/or may initiate a communication session with a badging platform 610 and/or digital credential issuer 630 to request that a new digital credential is issued for the worker. In such examples, the workplace server may provide the information identifying the worker (e.g., name, employee ID, digital credential system profile ID, etc.) to a digital credential platform 610 or issuer 630, along with verification that the worker has completed the requirements to earn a particular digital credential. In some embodiments, the servers operating at the workplace may be configured to capture evidence (e.g., video evidence, screen captures, facial/identity verification, etc.) and transmit the evidence to the digital credential-issuing authority, before the digital credential may be issued.
In step 1006, the worker may be notified that they have received a digital credential based on their normal on-the-job activities. In some embodiments, the worker may indicate interest in obtaining one or more particular digital credentials, and the workstation/workplace monitoring system may be configured to evaluate the worker with respect to the particular digital credentials or credentials that the worker has expressed interest in. However, in other examples, it may be possible for a worker to receive an issued digital digital credential without expressing any interest in the digital credential (or even being aware of such a digital credential), but solely based on the determination that the worker has achieved the level of skills mastery required for the digital credential/credential, based on the automated monitoring of the worker within the workplace. In certain cases, a user may be informed that they are eligible for receiving a digital credential prior to the issuance of the digital credential in step 1005, and the user may be allowed to accept or reject the digital credential. Additionally, in some cases, the user may receive status reports (e.g., daily, weekly, etc.) identifying which digital credentials the user is being monitored for, and the user's progress with respect to earning those digital credentials. This data may include indications to the worker that he/she may earn a particular digital credential after performing a task another N times, or performing the task N amount faster, or performing the task without making any errors or backtracking, etc.
For example, in certain embodiments and implementations of the concepts discussed above in reference to FIGS. 10-11, various techniques (e.g., systems, methods, computer-program products tangibly embodied in a non-transitory machine-readable storage medium, etc.) may include generating digital credentials for particular credential receivers, based on monitoring of a physical environment using a plurality of sensors. A digital credential issuer (or generator) 630 and/or 610 may detect or receiver sensor data from a number of sensors corresponding to the user actions performed by a user within the physical environment. The digital credential generator may determine the operations that were/were not performed within the physical environment, based on the user actions detected. The digital credential issuer then may retrieve data from a credential receiver data store associated with the user/credential receiver, and determine one or more digital credential templates, based on the retrieved data associated with the first credential receiver. For each of the digital credential templates, the digital credential issuer/generator may retrieve criteria associated with the digital credential template, compare the operations performed by the user within the physical environment to the criteria associated with the digital credential template, and then determine whether or not a credential receiver is eligible to receive a digital credential based on the digital credential template. If the first credential receiver is eligible to receive a digital credential based on the digital credential template, the digital credential generator may generate a digital credential based on the digital credential template and user data associated with the first credential receiver.
In such cases, the generated (or issued digital credential) may be embedded with additional data such as the evaluation/simulation time, location, or the sensor system/physical environment within which the evaluation/simulation was performed. Additionally, in some cases, facial recognition data and/or biometric data may be collected from the user (credential receiver), and may be used to validate or authenticate the digital credential by verifying the user's identity. As in the above examples, the monitoring may be done using physical movement tracking sensors such as video recorders and/or motion detectors, or may use software-based sensors such as network monitoring devices, keystroke loggers, mouse movement monitors, touch screen monitors. Such software-based tools may operate as background processes on a computer terminal being monitored, and/or may be built into specific software programs with which the user is interacting.
Additional aspects related to the automated tracking of user or worker activities, after the user/worker has been issued a badge (or digital credential), in order to determine how often the user/worker is “using” their digital credential. Depending on type of digital credential or credential, post-credentialing monitoring of the user may involve analysis of user's physical work product (e.g., documents produced, parts/items created, etc.), or may be involve observations of the user (e.g., via a workstation/workplace monitoring system). In order to evaluate how often a user is using a particular digital credential, a data store of digital credentials may be linked to particular skills, work-related, or activities. The user/worker may then be tracked to determine the number of such tasks performed, and/or the quality, efficiency, and/or competence of the user's performing those tasks, in order to determine to what extend the user/worker is “using” the digital credential.
Referring now to FIG. 11, an example computing environment 1100 is shown, including a digital credential platform server 1110, one or more workstation/workplace monitoring systems 1120, and a credential-to-activity mapping data store 1130. In some examples, the digital credential platform server 1110 may be a badging server similar or identical to the server 610 discussed above. Thus, server 1110 may be configured as a digital credential repository and credentialing system, acting as a clearinghouse for digital credential owners, issuers, earners, endorsers, etc. Server 1110 may include a digital credential (or digital credential) data store configured to store badging information such as the details of the particular digital credentials earned by particular users. As noted above, such details may include the date on which a digital credential was issued to a user, and for certain digital credentials, an expiration date associated with the digital credential.
In this example, system 1100 also includes a credential-to-activity mapping data store 1120, which may be implemented as a separate external data store and/or may be integrated into the digital credential data store of server 1100. The credential-to-activity mapping data store 1130 may include mappings of one or more tasks or activities associated with each digital credential type that a user may potentially earn. For example, a digital credential relating to automotive maintenance for a particular make of car may have associated activities and tasks that include particular maintenance tasks (e.g., tune-ups, part replacements, etc.) for different model cars having the make. As another example, an operating system administrator-related digital credential may list, within data store 1130, various system administrator tasks and that a user may perform on the particular operating system. In some cases, the activities or tasks associated with a particular digital credential may correspond to the same set of activities or tasks that a user is required to perform to earn the particular digital credential, and as discussed below, these activities or tasks may serve as a metric to evaluate how much the user is “using” the digital credential.
One or more workstation and/or workplace monitoring systems 1120 may provide user monitoring data to the server 1110, to allow the sever 1110 to analyze the user's activities and determine to what extent the user is using the activities and abilities associated with their digital credentials. In some embodiments, the workstation and/or workplace monitoring systems 1120 may be similar or identical to any of the workstation/workplace monitoring systems and sensors discussed above. For example, workplace monitoring systems 1120 may collect records detailing the user's physical work product (e.g., documents produced, modified or accessed by the user, inventory or work order records indicating tasks performed by the user, etc.). Additionally, workplace monitoring systems 1120 may include observation systems (e.g., workplace monitoring systems) including cameras and other sensors to track the user's activities and determine which specific tasks have been performed by the user.
In some embodiments, the monitoring and tracking of post-credentialing activities by the user may be used to analyze and provide digital credential or credential feedback data to various entities. For example, referring now to FIG. 12, a flow diagram is shown illustrating an example process that may be used to determine whether a user has or has not used the activities associated with a particular digital credential that they have obtained, and then to aggregate and report that digital credential usage data to the relevant parties. In step 1201, a particular digital credential is issued to a user based on the user's successful completion of the badging requirements. As in the various examples discussed above, the digital credential may be associated with a computer-based activity, non-computer-based activity, or any other set of digital credential requirements determined by a digital credential owner or issuer. Additionally, the digital credential issuance in step 1201 may be the result of formal testing and/or certification processes, or may be based on on-the-job or other observational data collected for the user.
In step 1202, the digital credential server 1110 and/or monitoring systems 1120 may monitor and track the activities of the credentialed user, including, for example, the workplace tasks performed by the user based on analyses of the various monitoring systems/sensor data installed at the user's workstation and/or workplace environment. As described above, determining what activities and tasks the credentialed user has performed, and when, may be performed using a variety of techniques. In some cases, determining what work-related tasks a user has performed, and what other activities they have been engaged in, may be done by analyses of written and electronic documents associated with the user or workplace. For instance, documents such as maintenance requests, work orders, customer tickets, purchase receipts, and the like may be analyzed to determine what activities or tasks the user has completed and when. For instance, a maintenance record listing the user as the assigned technician may be used in determination that the user has performed the specified task/activity at the time listed on the record. In other examples, the user's electronic mail and other electronic documents may be searched and analyzed (e.g., using a keyword analysis and/or trained artificial intelligence) to determine what tasks the user has performed and/or what activities the user has demonstrated during the relevant time periods. In some embodiments, there may be particular advantages in implementing a post-credentialing usage analysis and/or digital credential valuation process for certain digital credentials/tasks that are more discrete and detectable, for instance, a number of transmissions changed after earning a vehicle transmission certification, a number of particular medical procedures done following a digital credential credential for the procedure, a number of IT tickets resolved successfully following receiving an advanced IT computer services and computer repair digital credential, etc. In contrast, for other tasks and activities for which a user may receive a digital credential, such as leadership, communication skills, advanced C software programming, jujitsu skill levels, and the like, it may be more difficult to quantify if when, and how often a user is using the particular skill or task associated with the digital credential.
In step 1203, a set of tasks and/or activities associated with the digital credentials obtained by the specific user may be retrieved using the credential-activity mapping data store 1130, and in step 1204 the retrieved tasks and/or activities may be compared to the tasks and activities that have been performed by the user subsequent to the digital credentials being earned (as determined in step 1202). As an example, the comparison in step 1204 may determine that in the six month since the user was issued a professional certification to perform a particular technical task, the user has performed that task on a weekly basis. Alternatively, for a different digital credential issued to the user directed to expertise in a particular software program, the comparison in step 1204 may determine that the user has used that software program only once since receiving the digital credential two years ago. In this case, the system may conclude that the professional certification issued six months ago to the user has been of greater usefulness than the software digital credential issued two years ago (allowing for the possibility of career changes, prestige-driven digital credentials rather than functional digital credentials, etc.).
In step 1205, data from the comparison of step 1204, i.e., data indicating the post-credentialing usage by the user of the digital credential-associated activities or tasks, may be aggregated and analyzed, and then transmitted to one or more of the relevant system components. In various embodiments, any of several different components and roles associated with the credentialing platform 1110 may request and receive this information for their associated digital credentials and/or associated users. For instance, digital credential owners and/or digital credential issuers may request and receive from the platform server 1110 data regarding the post-issuance usage of the digital credentials they own or have issued. In other cases, digital credential endorsers may request and receive from the platform server 1110 data regarding the post-issuance usage of the digital credentials they have endorsed. Digital credential earners, the users themselves also may request reports from the platform server 1110 quantifying the post-credentialing usage (which may be expressed in terms of time, value, and/or dollar amounts) associated with their previously earned digital credentials. Employers and other organizations also may request such reports for their employees or organization members, in order to determine which digital credentials have been the most used and most useful to the organization.
Referring now to FIG. 13, another flow diagram is shown illustrating an related process involving determining whether a user has or has not used the activities associated with a particular digital credential that they have obtained, and then adjusting an expiration or re-certification date associated with the digital credential based on the user's usage of the digital credential skills. The steps in this example may be similar or identical to the corresponding steps in FIG. 12, and in some embodiments, the analyses and transmission of the post-credentialing usage described in step 1205 may be performed in conjunction with the setting of an expiration or re-certification date for the digital credential as discussed below.
Steps 1301-1304 may correspond to steps 1201-1204 in some cases, and may be performed using similar or identical techniques to those discussed above. For example, in step 1301 a platform server 1110 and/or digital credential issuer may issue a digital credential associated with one or more activities or tasks to a particular user, recording the digital credential issuance data within the digital credential data store. In step 1302, the post-issuance activities of the particular user may be monitored, including monitoring of the user's work-related activities and tasks performed/completed, in order to determine the particular tasks and activities with which the user has been engaged following issuance of the digital credential. In step 1303, the skills, activities, and tasks associated with the user's digital credential(s) are retrieved, and in step 1304 are compared to the post-issuance user tasks and activities determined for the user in step 1302. Finally, in step 1305, based on the comparison in step 1304, the platform server 1110 may determine that an expiration date and/or recertification date associated with the user's digital credential should be adjusted based on the user's post-issuance activities. As an example, if the system determines in step 1305 that a user who received a digital credential corresponding to a forklift operator's license or commercial truck driving license three years ago, but has infrequently (or not at all) driven a forklift or a commercial truck since receiving their digital credential, then the system may determine that the user's license should expire at the earliest possible time (e.g., the expiration time as of when the digital credential was first issued). In contrast, if the system determines in step 1305 that the same user has frequently and consistently driven a forklift or a commercial truck ever since receiving their digital credential, and also that the user has a high-safety rating and/or high safety compliance scores, then the system may determine that the user's license may be extended. In such cases, the platform server 1110 may determine a new extended expiration or recertification time for the digital credential, update the user's digital credential record within the digital credential data store, and transmit notifications to the affected entities (e.g., the user, employer, digital credential issuer, digital credential owner, etc.) providing the new expiration date. In other examples, rather than changing the expiration date or recertification date of a digital credential (or eliminating the expiration altogether), the platform server 1110 may in other examples determine a new recertification course or procedure for the user, such as simple refresher course to allow the user to recertify quick than the longer complete recertification course used by other users with less post-credentialing digital credential usage.
Additional aspects described herein relate to capturing and using “evidence” data in connection with user testing and credentialing systems, on-the-job evaluation and badging systems, and/or post-credential monitoring systems. For example, within any automated badging/certification/verification system, evidence of the user's performance may be extracted and saved, for example, in a digital credential server along with an associated issued digital credential, or as part of a separate user portfolio of evidence. Evidence data may include, for example, audio and video of the user during a live simulation, or during a virtual reality or augment reality simulation, audio and keystroke data from the user during the testing processing, the user's reaction time and/or decision-making data during a split-second simulated scenario or relevant real-life event (e.g., a workplace accident, etc.), and/or any other sensor or biometric data collected during testing, credentialing, and/or monitoring. As discussed below, evidence data associated with a user may be saved with the user's digital credential and/or into a separate portfolio of evidence, which may be available to the user for review, and also may be provided upon request to potential employers for review during a review or hiring process. Such evidence data also may be applied to updated digital credential credentialing requirements, so that in some cases a user may simply resubmit their evidence portfolio instead of being required to recertify their digital credential when the test or credentialing standards are updated.
Referring now to FIG. 14, an example computing environment is shown including a digital credential platform server 1410 in communication with a plurality of testing, credentialing, and/or monitoring systems 1421-1423, and one or more external client devices 1460. In some examples, the digital credential platform server 1410 may be a digital credentialing server similar or identical to the server 610 discussed above. Thus, server 1410 may be configured as a digital credential repository and credentialing system, acting as a clearinghouse for digital credential owners, issuers, earners, endorsers, etc. Server 1410 may include a digital credential (or digital credential) data store configured to store digital credential information such as the details of the particular digital credentials earned by particular users. As noted above, such details may identify the digital credential issuer and/or other testing/credential authorities responsible for administering testing or simulation scenarios as part of the digital credentialing process, and/or for pre-digital credential or post-digital credential monitoring of workstations/workplaces to detect and analyze user tasks performance and user skills/abilities
In this example, the platform server 1410 may receive data from three testing/credentialing systems 1421-1423. Similar to the above examples, the simulation lab system 1421 may correspond to a simulation lab or other physical testing environment, an on-the-job credentialing systems 1422 may include workstation/workplace monitoring systems and sensors to record and analyze the user's on-the-job performance, and may issue digital credentials in some cases without the need for any separate formal testing procedure; and post-credential monitoring systems 1423 may be configured to monitor users following the issuance of a digital credential, including tracking task performance data, skills usage, and the like, and comparing the data to the skills/tasks associated with the user's digital credentials.
In some embodiments, one or more systems 1421-1423 which perform user testing, credentialing, and/or monitoring, such as those systems discussed above, may capture and transmit “evidence data” of the user during a test, simulation, or during an on-the-job monitoring process. Evidence data may include, for example, video and/or audio of the user during a test, simulation (e.g., live, VR, or AR), collected by the sensors of a physical testing environment 700. Additional evidence data may include user reaction time data, decision-making data, facial expression and body language data, keystroke and mouse movement data, and/or user biometric data. The evidence data may correspond to a time period just before, during, and just after a test, simulation, or a task or activity performed during on-the-job monitoring.
As shown in this example, the various evidence data collected by systems 1421-1423 may be transmitted to the platform server 1410 and stored in an evidence portfolio data store. The evidence data collected by the testing, credentialing, and/or user monitoring systems may be associated with a particular user (or users) and with a particular digital credential (or digital credentials) that the user is in the process of earning or using (e.g., for post-credentialing monitoring). Thus, the evidence data may provide documented proof that the user actually completed the digital credential requirements, along with additional contextual evidence showing how the user performed during the testing, simulation, or monitoring.
Referring now to FIG. 15, a flow diagram is shown illustrating an example process by which a testing system, simulator, credentialing systems, workstation/workplace monitoring system, and the like, may collect and preserve evidence data related to a user and a digital credential. In step 1501, a testing, credentialing, and/or monitoring system such as those described above may execute a test, simulation, or user monitoring process for a particular user in connection with a digital credential that the user is seeking or has already obtained. The particular types of tests may include, for example, live simulations and/or virtual or augmented reality simulations executed within a physical testing environment 700. In other examples, the testing in step 1501 may correspond to an on-the-job credentialing system that monitors and evaluates a user's workplace tasks and activities, or to a post-credentialing user monitoring system configured to determine whether the user is using their previously issued digital credentials. In step 1502, during any of these testing, simulation, or monitoring processes, the system 1421-1423 may capture evidence data relating to the user. As noted above, evidence data may include audio or video of the user, user reaction time data, decision-making data, facial expression data, body language data, the user's keystrokes and mouse movement data, particular software interaction data, and/or the user's biometric data. In step 1503, the evidence data may be encapsulated and transmitted to the platform server 1410 for storage within the user's evidence portfolio, and in step 1504 the platform server 1410 may store the evidence data files with data records associated with the user and the particular digital credential(s) to which the evidence applies. In other embodiments, certain systems 1421-1423 may retain and store user evidence data locally, rather than the evidence data being stored in a central repository. Additionally, when the evidence data is transmitted, it may be compressed and edited as needed, and/or encrypted in order to assure data security and user privacy.
In some cases, the platform server 1410 may determine a subset of the user activities matching digital credential requirements associated with the digital credential, wherein other user activities might bare no relevance to the requirements of the digital credential. In such cases, the platform server may store only the corresponding subset of the evidence/sensor data for the user activities matching digital credential requirements, and might not store other evidence/sensor data corresponding to irrelevant user activities upon which digital credentials do not depend.
Referring now to FIGS. 16A and 16B, two additional flow diagrams are shown illustrating example processes by which evidence data may be retrieved and/or accessed from a platform server 1410 or other data repository. As noted above, individual evidence data files stored by the platform server 1410 may be associated with a particular user and/or with a particular digital credential or credential earned (or in process of earning) by the user. Thus, in some embodiments, evidence data may be stored and made available to certain authorized entities. For instance, in step 1601 of FIG. 16A, the platform server 1410 may receive a request for some or all of the user's evidence portfolio. In step 1602, the platform server 1410 may perform authorization/authentication on the request to determine (1) whether the requestor is authorized to access the user's evidence data, and/or (2) whether the requested evidence is current and valid. One or both of these determinations may require explicit authorization from the user himself or herself, in order to (1) prevent any unwanted parties from accessing the user's evidence data, and (2) to prevent any old and obsolete from being accessed, even by authorized parties. Thus, step 1602 may include verifying the requestor's identity or role and comparing to an access control list or other permissions data associated with the evidence. In some cases, step 1602 may include a real-time request sent by the platform server 1410 to a client device associated with the user, to allow the user the option to allow or reject the request. Additionally, the request in step 1601 may specify one or more particular users and/or one or more particular digital credentials for which the associated evidence is to be retrieved, and thus authorization in step 1602 may be granted or denied for evidence relating to each possible combination of users and digital credentials. In step 1603, assuming that the requestor has been granted access to the requested evidence data, the corresponding evidence data files may be retrieved and forwarded to the requestor.
In some examples, the request in step 1601 may be from the user himself/herself, who wants to review and study the evidence from his/her previous tests, simulations, and monitoring data. In other examples, the request in step 1601 may be from a current or potential employer, who has been authorized by the user to retrieve and view the user's evidence data associated with all work-relevant digital credentials, as part of a hiring process or review process. The user's evidence data may verify to the employer or potential employer that the user actually completed the digital credential requirements, and also may allow the employer or potential employer to observe the user's behaviors, responses, reactions first-hand, thus allowing them to evaluate the user's reaction time, efficiency, mental state, decision-making, etc., and other difficult to quantify characteristics. In still other examples, the user may authorize a digital credential issuer or digital credential owner to view the user's evidence files related to the digital credentials issued and owned by those entities. Finally, users may make some or all of their evidence data publicly available (e.g., on a file-by-file basis) and/or may actively post their evidence data as a multimedia file or data records within a digital credential profile page of the user that is maintained and published by the platform server 1410.
In some embodiments, in addition to (or instead of) providing evidence data in response to requests, the platform server 1410 may provide the functionality to receive updated tests, digital credential requirements, credentialing data, etc., and to apply a user's previously stored evidence to the new testing or credentialing requirements. For instance, in step 1604 of FIG. 16B, the platform server 1410 may receive a request to apply previously stored evidence data within a user's portfolio to an updated testing/credentialing process. For example, testing or credentialing authorities (e.g., a digital credential owners or issuers, employers, etc.) may periodically update digital credentialing requirements in order to improve the quality of the digital credential testing, to comply with new best industry practices, to make a digital credential more restrictive by increasing the required scores or efficiency, etc. Additionally, certain testing or credentialing authorities may implement multiple different levels of the same digital credential, in which users are subjected to the same test, same simulation, same monitoring processes, etc., but different scoring ranges may equate to different levels of the digital credential that may be earned by the user. In these scenarios, whenever digital credential requirements are updated, or if a new digital credential level is made available, it may be possible to apply the user's previously collected evidence data to the new digital credential requirements or digital credential level, rather than requiring the user to retake the test, simulation, or monitoring process. As an example, a set of new requirements for particular digital credential may be similar to the previous set of requirement, with the addition of a newly imposed time limit by which the test or simulated scenario must be completed. In other example, new digital credential requirements or digital credential levels may raise the minimum performance level during a test or simulation to a higher level, and/or may require additional steps or procedures during the test or simulation that were not required in the previous version of the digital credential requirements. In these cases, rather than require the user to retest/recertify to earn the updated digital credential, the platform server 1410 may provide the service of receiving the updated digital credential requirements or new digital credential levels, and automatically evaluating the new digital credential requirements/levels using the user's evidence data that was collected with earning the previous version of the digital credential. Thus, in step 1605, the requestor may be authenticated and the requested data may be validated, and in step 1605 the user's evidence data may be applied the updated testing/credentialing process. Referring to these same digital credential requirements changes discussed above, the evaluation in step 1606 may include automated analysis of the user's evidence data to determine whether the user complied with the newly imposed time limit, the new minimum performance level, and/or performed the additional new steps or procedures during the user's previous digital credential testing. If so, the digital credential authority may allow the user to upgrade their digital credential automatically without having to retake the test or simulation, etc. If not, the user may be informed that they are required to retake the test or simulation (or in some cases they may receive a lower digital credential level). Either way, in step 1607, the results of the evidence analysis and application to the new credentialing requirements may be output to the requestor. Another potential advantage in certain embodiments may include the protection of the user's evidence data itself. For instance, in the above example, the platform server 1410 might perform the analysis and application of the user's previously stored evidence data to the new testing requirement, without ever allowing any other entity access to the evidence data. In other examples, the platform server 1410 may perform the analysis and/or may provide the actual evidence data files to the requestor device, with the sufficient authorization from the user.
In various embodiments, the updated testing/credentialing process in step 1604 may correspond to a re-issuance of a digital credential, with the same or updated requirements, or may corresponding to a different digital credential having similar and/or overlapping issuance requirements. For instance, the platform server 1410 may receive an updated set of requirements for a digital credential previously issued to a credential receiver, may retrieve the stored set of sensor data corresponding to the relevant activities performed by the credential receiver in connection with the issuance, may compare the retrieved set of sensor data/activities to the updated set of digital credential requirements, and then may generate/issue an updated digital credential to the credential receiver, based on the comparison of the retrieved set of sensor/activity data to the updated set of digital credential requirements. Similar techniques may be performed to generate and issue digital credentials to receivers for entirely different digital credentials, rather than updated credentials, such as similar credentials and/or credentials having overlapping eligibility requirements.
Additional aspects described herein relate to capturing and using user biometric data, physical user cues, and the like, in connection with user testing and credentialing systems, on-the-job evaluation and badging systems, and/or post-credential monitoring systems. For example, within any automated badging/certification/verification system, data identifying particular physical user cues and/or user biometric data may be collected during testing/simulation/monitoring processes and saved, for example, in a digital credential platform server along with an associated issued digital credential and/or the associated user. Physical user cues may include, for example, facial expressions, user reactions and/or noises made by the user during testing/simulations, user body language, eye movement, and any other user behavior or reaction detectable via cameras and external sensors. Additionally or alternatively, various types of user biometric data also may be collected during the testing simulation, and/or monitoring processes performed on the user. Such biometric data may include, for instance, the user's temperature, heartrate, blood pressure, respiration, skin conductivity, and brainwave activity, and/or any known types of biometric data that may collected during testing, credentialing, and/or monitoring processes.
As discussed in more detail below, the user's physical cues and/or biometric data may be collected and saved within a digital credential platform server, and associated with the user, one or more particular digital credentials, and/or with the particular testing/simulation/monitoring processes during which the data was originally detected. Once collected, the data may be used to authenticate the testing, simulation, and/or monitoring processes, to confirm the user's identity and to prevent errors or fraudulent activities by users. The data may be saved with the user's digital credential and/or into a separate portfolio of evidence, which may be available to the user for review, and also may be provided upon request to potential employers for review during a review or hiring process. Such evidence data also may be applied to updated credentialing requirements, so that in some cases a user may simply resubmit their evidence portfolio instead of being required to recertify their digital credential when the test or credentialing standards are updated. In certain embodiments, the user's physical cues and/or biometric data also may be analyzed to determine the user's emotional states and reactions during the testing, simulation, and/or monitoring. Additionally or alternatively, the physical cues and biometric data may be detected for several users and analyzed collectively to provide feedback regarding the digital credential testing processes, simulations, monitoring, physical testing environments, etc.
Referring now to FIGS. 17A-17B, examples are shown illustrating facial recognition and analysis functionality that may be performed in connection with a user testing/credentialing process (live or simulation), or with user on-the-job credentialing or monitoring processes. In this example, one or more cameras may be configured to capture the user's facial features and expressions at different points during the testing/credentialing/monitoring processes. For tests performed within a simulation lab-type physical testing environment, a number of designated cameras may capture not only the user's face but also the user's body from several different angles. Thus, certain physical testing environments may be capable not only of capturing facial images of the user, but also detecting detailed facial expressions at different times during the test/simulation, and potentially eye movement patterns, gestures, body language, and any other non-verbal and non-written user expression data.
In other embodiments, such as for certain on-the-job credentialing or monitoring systems, or for formal testing/credentialing when sophisticated high-tech physical testing environments are not used, the physical cue data and/or biometrics data collected may be limited by the cameras and sensors available. In some cases, a laptop camera or webcam installed at the user's workstation may be use to capture facial images and/or to recognize facial expressions at different times during the testing/monitoring. However, such cameras may or may not have the resolution and image capture capabilities to perform advanced facial expression monitoring, eye movement, and/or body language detection. In other examples, such as on-the-job credentialing and monitoring scenarios, facial images might only be detectable using lower-quality security cameras or the like that are configured to monitor an entire floor or workspace. In such examples, the facial images may be still be useful for certain purposes (e.g., confirmation of user identification), but potential may be unsuitable for facial expression analysis, eye movement analysis, and the like.
Additionally or alternatively, physical testing environments (e.g., simulation labs) and/or workstation or workplace monitoring systems may include various biometric sensors configured to detect biometric data of the user at different times during the test/simulation. As noted above, such biometric data may include the user's temperature, heartrate, blood pressure, respiration, skin conductivity, and brainwave activity, and/or any known types of biometric data. Thus, the biometric metric may be detected and captured via a combination of external sensors, wearable sensors, and/or implanted sensors in some cases. For on-the-job credentialing and monitoring, mobile wearable sensors such as heartrate monitors, step trackers, and the like, may be used when more advanced wearable sensors (e.g., blood pressure, respiration, skin conductivity, brainwave activity, etc.) are not practical.
Referring now to FIG. 18, a flow diagram is shown illustrating an express process of collecting physical cue data and/or biometric data for a user during a user testing credentialing, or monitoring processes, and using the physical cue and biometrics to authenticate the user's identity and the associated data. The process shown in this example may be implemented within any of the testing/credentialing systems, simulators, workstation or workplace monitoring systems, and the like described herein. In step 1801, a testing, credentialing, and/or monitoring system such as those described above may execute a test, simulation, or user monitoring process for a particular user in connection with a digital credential that the user is seeking or has already obtained. The particular types of tests may include, for example, live simulations and/or virtual or augmented reality simulations executed within a physical testing environment 700. In other examples, the testing in step 1801 may correspond to an on-the-job credentialing system that monitors and evaluates a user's workplace tasks and activities, or to a post-credentialing user monitoring system configured to determine whether the user is using their previously issued digital credentials. In step 1802, during any of these testing, simulation, or monitoring processes, one or more of the user monitoring devices described above, including cameras, microphones, motion sensors, tracking devices, and/or user biometrics sensors, may capture physical cues from the user and/or biometric data of the user during the testing, simulation, or monitoring processes. Such physical cues may include particular facial expressions, user reactions and/or noises made by the user during testing/simulations/monitoring, as well as user body language and eye movements. In step 1803, the physical cue and user biometric data may be encapsulated and transmitted to the transmitted to the platform server 1410. In other embodiments, certain systems (e.g., 1421-1423) may retain and store user's physical cues and biometrics data locally, rather than the evidence data being stored in a central repository. Regardless of storage location, the physical cues and biometrics data of the user may be associated with particular test questions and/or particular time stamps during a testing or simulation. Additionally, when the data is transmitted, it may be compressed and edited as needed, and/or encrypted in order to assure data security and user privacy.
In some embodiments, the platform server 1410 may use the physical cues and/or biometrics data collected for the user as part of an authentication process in step 1804. For example, during any testing/credentialing process (e.g., written testing, computer-based testing, simulation lab testing, etc.) the user's facial images, physical cues, and/or biometrics may be compared against previously stored corresponding data (e.g., user images, physical cue patterns, biometrics, etc.) in order to verify that the correct user is taking the test/simulation. Additionally, the user's physical cues and biometrics may provide an additional level of authentication, by comparing the observed physical cues and biometrics at particular times during the test or simulation to expected physical cues and biometrics, based on what is happening during the test or simulation at that particular time. For instance, a simulation may be designed to present a challenging and stressful situation to the user at a particular timestamp or within a sequence of tasks the user is performed. In step 1804, the server may compare the user's observed physical cues and biometrics to the physical cues and biometrics that would be expected for the challenging and stressful situation, in order to confirm that the data is valid and/or that the user did not expect this situation in advance (e.g., indicating cheating). In step 1805, the platform serving 1410 having validated the user's identity and the authenticity of the user's physical cues and biometrics, may store the testing, credentialing, monitoring data in the digital credential data store as valid data. In some embodiments, the image data, facial cues, and/or biometrics data also may be retained and stored by the platform server for future analysis.
In some embodiments, the data relating to the user's physical cues and biometrics collected during a test, simulation, or during on-the-job monitoring, may be further evaluated to identify the user's emotional states at different times. For instance, certain simulations may be specifically designed to invoke certain emotional states (e.g., anger, boredom, frustration, surprise, etc.), and the user's level of performance while experiencing those emotional states may be particularly important for certain testing/credentialing processes. In these examples and other cases, either exhibiting or not exhibiting particular emotion states may be an eligibility requirement for a credential receiver to obtain certain types of digital credentials. Thus, the data collected during the test, simulation, or monitoring in step 1801 may be used not only for user identification/authentication, but also may be analyzed to (1) determine the user's emotional state at different times during the test, simulation, or monitoring, (2) compare that emotional state to an expected emotional state based on what the user is experiencing, and (3) evaluate the user's reactions, levels of skills performance during different emotional states.
Additionally, in some embodiments, the physical cues, biometrics data, and/or emotional states detected for multiple users may be aggregated for the same tests, simulations, monitoring environments, etc. The aggregated data for tests may be used to revise current tests and simulations, design new tests and simulations, and for training users how to respond to particular scenarios and situations (e.g., workplace accidents).
Further aspects described herein relate to identifying top performers for a particular field (e.g., job, occupation, or employer), and determining top performer profiles for the particular job, occupation, or employer based on the digital credential portfolios of the identified top performers as well as any other skills, attributes, or traits of the top performers. For instance, the top performers in a particular occupation or field may be identified, and the skills profiles of these top performers may be analyzed to determine a model profile associated with top performers for the particular field (e.g., occupation). Thus, if an employer wanted to hire N new employees for a particular position, that employer may use the top performer model profile empirically determined from the existing workforce, including the skills/traits of those top performers, to make hiring decisions for the new positions. The identification of top performers may be done by direct on-the-job observation (e.g., on-the-job monitoring systems with cameras and/or sensors), or by performance output from the employer's systems to measure productivity (e.g., number of products sold, number of maintenance tickets closed, efficiency rate, etc.), or by subjective evaluations (e.g., reviews from supervisors and/or peers), and the like. Then, once the top performers are identified, a top performer model profile tool within a digital credential platform system (or other external tools) can analyze the skills and/or traits of the top performers, including digital credentials (e.g., both skills-based, personality/temperament trait digital credentials, health/DNA based digital credentials, etc.), to create the model profile of top performers. Since the characteristics of top performers may be different from occupation to occupation, occupation to occupation, and employer to employer, the top performing model profile may be difficult to predict in advance, and may be the result of unique sets of factors in different cases. For example, aside from the particular job skills and personality traits required to be top performer, additional factors such as company culture, location/region, etc., may affect which workers are the top performers and which digital credentials/traits are identified within a top performer model profile.
Referring now to FIG. 42, an example computing environment 4200 is shown, including a digital credential platform server 4210, in communication with one or more employer performance systems 4265 and employer administrator client devices 4260. In some examples, the digital credential platform server 4210 may be a digital credential server similar or identical to the digital credential server 610 discussed above. Thus, server 4210 may be configured as a digital credential repository and credentialing system, acting as a clearinghouse for digital credential owners, issuers, earners, endorsers, etc. As shown in this example, server 4210 may include a top performance model profile tool, implemented via specialized hardware and/or software configured to retrieve and analyze employee performance data from systems 4265, and to determine top performer model profiles (e.g., a top performer digital credential portfolio) associated with a particular field, occupation, and/or employer. Employer performance system 4265 may include systems from one employer or several, and may include many different types of performance systems (e.g., formal skills testing systems, simulation testing systems, on-the-job monitoring systems, employee review/evaluation systems, and employee output or productivity systems). Additionally, performance systems 4265 may include systems from other entities, such as supplier systems, customer systems, governmental systems, and the like, from which particular employee performance (e.g., outputs or quality of the employee's work output) can be determined. In contrast, an employer administrator client 4260 may be operated by an individual representative of the employer (e.g., an owner, supervisor, internal recruiter, etc.) used to access the top performer model profile tool in order to retrieve a top performer model profile for a particular job opening position, or for the employer's workforce as a whole.
Referring now to FIG. 43, a flow diagram is shown illustrating an example process of determining and providing a top performer profile (e.g., top performer model profile) for a particular field, occupation, or employer. Thus, in some embodiments, this process may be performed by digital credential platform server 4210, using a top performer model profile tool 4215 to retrieve and analyze employee performance data, and correlate that data with the digital credential portfolios of top performers. In step 4301, the top performer model profile tool 4215 may retrieve employee performance data from one or more employer performance systems 4265. As noted above, such data may include employee evaluation data (e.g., performance scores, raises, promotions, etc.) employee scores on various work-related testing (e.g., professional certification scores, simulation scores, etc.), employee output (e.g., data metrics regarding employee efficiency, amount of work completed, quality of work completed, etc.), and/or on-the-job monitoring data. In step 4302, based on any combination of the received employee performance data, the top performer model profile tool 4215 may identify a number of the top performing employees within the company's current (and/or past workforce). In some cases, the top performing employees may be selected from within a particular role at the company (e.g., performing a particular occupation, at a particular seniority level, at a particular location/region/office, having a salary less than a salary threshold, etc.), in order to match the criteria of new employees being sought by the employer. In different embodiments, different numbers of top performing employees may be selected in step 4302, such as the top 100 performing employees, the top 10% of performing employees, the top 10 most profitable employees from the past N years, etc. In various examples, the performer model profile tool 4215 may perform the analysis in step 4302 using one or more mathematical model, regression analyses, and/or trained AI machine learning algorithms, to determination correlations between particular characteristics/capabilities of top performers. Further, it should be understood that similar or identical techniques may be used to determine a model profile for a not top performer.
In step 4303, for each of the employees identified in step 4302, the top performer model profile tool 4215 may retrieve the digital credential portfolio and/or any other available user data. The digital credential portfolio (or other user data) retrieved in step 4303 may include an aggregated skills profile for the user (e.g., based on the skills associated with each of the user's digital credentials), including personality-based digital credentials (e.g., emotion-related digital credentials, temperament-based digital credentials, etc.), digital credentials for abilities/traits, DNA-based or health-related digital credentials, and/or any other user characteristics determinable from the user's digital credential portfolio or other user data. Finally, in step 4304, the top performer model profile tool 4215 may determine a top performers profile for the particular field, occupation, or employer, based on the digital credential portfolios and other user data retrieved in step 4304. In some examples, the top performer model profile tool 4215 may identify a set of the most-commonly earned digital credentials among the identified top performing users. Additionally or alternatively, the top performer model profile tool 4215 may identify the most-common skills among the top performing users, the most-common personality traits, and/or any other common abilities, traits, and/or characteristics shared by some or all of the top performing users, and/or which are particularly strong among the top performing users. Thus, the top performer profile for the particular field, occupation, or employer may then be provided to an employer administrator 4260 and/or other client device (e.g., candidate seeking a job, recruiter seeking to fill an open position, etc.).
Certain aspects described herein relate to determining current and expected market values for particular digital credential offerings (e.g., individual digital credentials or groups of digital credentials) for particular digital credential earners. For example, within a digital credential server platform and network, different digital credential owners and issuers may charge various amounts for their different digital credentials. Costs may include course/training costs, testing and simulation costs, administrative costs and recertification costs. Digital credential earners, especially those who may be new to the digital credential system and/or new the job market, may want to know the objective value of a digital credential offering to decide whether or not it's worth the user's effort (in both time and money) to obtain that digital credential. Accurate and current calculation of digital credential values may be difficult because the value may be driven by market factors (e.g., current employment data, current job listing data, etc.), and also may be different for different digital credential earners (e.g., based on the earner's other similar or complementary digital credentials, based on the earner's current skills profile and other qualifications, etc.). Accordingly, a digital credential valuation tool may be implemented as a user-facing tool that provides current valuations of each digital credential for a particular earner (e.g., including both the earner's current digital credentials and potential digital credentials that the earner might decide to obtain). Such tools also may recommend digital credentials to the particular digital credential earner, based on the current value of the digital credential, or may recommend substitute digital credential offering to a potential digital credential (e.g., a suggestion to get these two digital credentials which are quicker and cheaper, rather than one longer and more expensive digital credential). Such data also could be provided to digital credential owners/issuers, to allow them to change the price or availability of their digital credential offerings, etc.
Referring now to FIG. 44, a flow diagram is shown illustrating an example process of valuating a digital credential offering for a particular user within a digital credential platform system. In some embodiments, this process may be performed by a digital credential platform server (e.g., 610) using a specialize digital credential offering valuation tool configured to retrieve and analyze both digital credential portfolio/skills data, and job market data, as described in various examples above. In step 4401, the digital credential platform server may receive a request from a user via a client device to determine a value for a particular digital credential or digital credential offering (e.g., digital credential grouping or package) that the user is considering. The digital credentials or digital credential offerings identified in the request may correspond to new digital credentials that the user is considering obtaining, or to the user's existing digital credentials that the user is considering recertifying (or not recertifying). Additionally, as described below. the request in the step 4401 may be associated with a particular digital credential earner having an existing digital credential portfolio and/or user profile data within the system, and thus, the valuation of the digital credential may be determined with respect to the particular digital credential user based on his/her digital credential portfolio and other user data. However, in other examples, it may be possible to determine a value for a digital credential or digital credential offering that is not tied to any particular user.
In step 4402, the digital credential platform server may retrieve the digital credential portfolio and/or any other available user data (e.g., current employment data, educational qualifications, location data, other skills/abilities data, etc.) for the user that initiated the request in step 4401. Based on the retrieved data, the digital credential platform server may determine a current skills profile for the user by aggregating the level of the user's skills in different skill areas based on the digital credentials the user has earned and/or other available user data. In step 4403, the digital credential platform server may determine a current market value associated with the user's current skills profile. The current market value may be based on an analysis of data from multiple different data sources, including data from multiple employer systems within the same technical field (e.g., average skills profiles/skills levels of current employees in different positions, salaries of employees in those positions), current job posting data (e.g., number of type of jobs/positions being advertised by employers, and the number of current candidates with compatible skill sets for those jobs, etc.). In step 4404, the skills profile determined for the user in step 4402 may be updated based on an assumption that the user has obtained the digital credential (or digital credentials) identified in the request, or taken whatever other prospective action was identified in the request (e.g., learning a new skill, moving cities, obtaining an additional degrees, letting a digital credential lapse, etc.). In step 4405, the updated skills profile for the user, which may include additional skills, increased levels of existing skills, and/or the reduction or elimination of other skills, may be used to determine an updated market value associated with the updated skills profile. Thus, the process in the step 4405 may be similar or identical to the determination of the market value for the user's current skills profile in step 4403. In step 4406, the change in the market value of the user's skills profile, between the user's current skills profile and the user's updated prospective skills profile may be determined and output to the requesting user.
In some examples, it may be found that the prospective digital credential offering may greatly increase the market value of the user's skills profile, while in other cases the prospective digital credential offering might increase the market value of the user's skills profile very little or not at all. The changes may be based on an objective value of the digital credential offering itself (e.g., the skills offered, the endorsements and determined quality of the digital credential testing, etc.), as well as the current job market/hiring/employment data, and also based on the user's particular skill set. For instance, if the skills associated with the digital credential offering are redundant to the user's current skill set, or are not complementary to the user's current skill set, then there may be little or no increase in value for the user to obtain the digital credentials. However, if the skills associated with the digital credential offering are lacking within the user's current skill set, and/or would be complementary to the user's current skill set, then there may be a significant increase in value for the user to obtain the digital credentials.
In some embodiments, results may be displayed graphically via a user interface, and a variety of different user-facing functionality may be offered based on prospective digital credential valuation. For instance, the digital credential platform server may provide tools to allow users to browse and estimate the value to that user of different digital credential offerings. Related tools may allow employers or recruiters to recommend digital credentials to existing employees or job candidates, and/or may allow digital credential owners and issuers to evaluate the demand for their own digital credential offerings. Referring briefly now to FIG. 45, an example user interface screen 4500 is shown displaying the results of a prospective digital credential search for a particular user (“User ABC”). In this example, a number of possible digital credentials that the user may obtain is shown in response to the user's request, including for each possible digital credential data identifier the digital credential issuer, the estimated time commitment that will be required for the user to obtain the digital credential (e.g., as provided by the issuer), the cost for the user to obtain the digital credential (e.g., as provided by the issuer), and the estimated change in the market value of the user's skill set that would result from the user obtaining the digital credential.
A number of variations and modifications of the disclosed embodiments can also be used. Specific details are given in the above description to provide a thorough understanding of the embodiments. However, it is understood that the embodiments may be practiced without these specific details. For example, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
Implementation of the techniques, blocks, steps and means described above may be done in various ways. For example, these techniques, blocks, steps and means may be implemented in hardware, software, or a combination thereof. For a hardware implementation, the processing units may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described above, and/or a combination thereof.
Also, it is noted that the embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a swim diagram, a data flow diagram, a structure diagram, or a block diagram. Although a depiction may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed, but could have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination corresponds to a return of the function to the calling function or the main function.
Furthermore, embodiments may be implemented by hardware, software, scripting languages, firmware, middleware, microcode, hardware description languages, and/or any combination thereof. When implemented in software, firmware, middleware, scripting language, and/or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine readable medium such as a storage medium. A code segment or machine-executable instruction may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a script, a class, or any combination of instructions, data structures, and/or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, and/or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc.
For a firmware and/or software implementation, the methodologies may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. Any machine-readable medium tangibly embodying instructions may be used in implementing the methodologies described herein. For example, software codes may be stored in a memory. Memory may be implemented within the processor or external to the processor. As used herein the term “memory” refers to any type of long team, short team, volatile, nonvolatile, or other storage medium and is not to be limited to any particular type of memory or number of memories, or type of media upon which memory is stored.
Moreover, as disclosed herein, the term “storage medium” may represent one or more memories for storing data, including read only memory (ROM), random access memory (RAM), magnetic RAM, core memory, magnetic disk storage mediums, optical storage mediums, flash memory devices and/or other machine readable mediums for storing information. The term “machine-readable medium” includes, but is not limited to portable or fixed storage devices, optical storage devices, and/or various other storage mediums capable of storing that contain or carry instruction(s) and/or data.
While the principles of the disclosure have been described above in connection with specific apparatuses and methods, it is to be clearly understood that this description is made only by way of example and not as limitation on the scope of the disclosure.

Claims

1. A digital credential platform server configured to generate a model credential profile for particular field data objects, the digital credential platform server comprising:

a processing unit comprising one or more processors;

one or more network interfaces; and

memory coupled with and readable by the processing unit and storing therein a set of instructions which, when executed by the processing unit, causes the digital credential platform server to:

receive data identifying a first subset of credential receivers, out of a plurality of credential receivers associated with a field data object;

for each particular credential receiver in the first subset of credential receivers, determine a set of capabilities associated with the particular credential receiver, wherein determining set of capabilities comprises:

(a) retrieving one or more digital credentials issued to the particular credential receiver;

(b) retrieving data identifying one or more capabilities associated with each of the digital credentials issued to the particular credential receiver; and

(c) aggregating the capabilities associated with each of the digital credentials issued to the particular credential receiver;

perform an analysis on the determined sets of capabilities associated with each of the credential receivers in the first subset; and

generate a model capabilities profile for the field data object, based on the analysis of the determined sets of capabilities associated with each of the credential receivers in the first subset.

2. The digital credential platform server of claim 1, wherein performing the analysis on the determined sets of capabilities associated with the credential receivers in the first subset comprises performing at least one of a regression analysis or a machine learning algorithm on the aggregated capabilities data, to determine one or more correlations between the capabilities of the first subset of credential receivers.

3. The digital credential platform server of claim 1, wherein determining the set of capabilities associated with each particular credential receiver comprises determining, for each particular credential receiver in the first subset of credential receivers:

a plurality of capabilities associated with the particular credential receiver; and

a magnitude value for each of the plurality of capabilities associated with the particular credential receiver.

4. The digital credential platform server of claim 1, wherein determining the set of capabilities associated with each particular credential receiver further comprises, for each particular credential receiver in the first subset of credential receivers:

monitoring a physical environment associated with the particular credential receiver using a plurality of sensors, and detecting a plurality of user activities of the particular credential receiver within the physical environment; and

determining a plurality of capabilities associated with the particular credential receiver, based on the detected user activities of the particular credential receiver within the physical environment.

5. The digital credential platform server of claim 1, the memory storing additional instructions which, when executed by the processing unit, causes the digital credential platform server to:

for each particular credential receiver in the first subset of credential receivers, determine a set of traits associated with the particular credential receiver; and

perform an additional analysis of the determined sets of traits associated with each of the credential receivers in the first subset,

wherein generating the model capabilities profile for the field data object includes determining a model set of traits, based on the additional analysis of the determined sets of traits associated with each of the credential receivers in the first subset.

6. The digital credential platform server of claim 1, the memory storing additional instructions which, when executed by the processing unit, causes the digital credential platform server to:

for each particular credential receiver in the first subset of credential receivers, determine a set of physical condition traits associated with the particular credential receiver; and

perform an additional analysis of the determined sets of physical condition traits associated with each of the credential receivers in the first subset,

wherein generating the model capabilities profile for the field data object includes determining a model set of physical condition traits, based on the additional analysis of the determined sets of physical condition traits associated with each of the credential receivers in the first subset.

7. The digital credential platform server of claim 1, the memory storing additional instructions which, when executed by the processing unit, causes the digital credential platform server to:

prior to determining the sets of capabilities associated with each credential receiver in the first subset of credential receivers, determine the first subset of credential receivers out of the plurality of credential receivers by:

receiving performance data for each of the plurality of credential receivers, corresponding to the performance of the credential receivers with respect to a field identified in the field data object; and

selecting the first subset of credential receivers out of the plurality of credential receivers, based on the performance data.

8. A method of generating a model credential profile for particular field data objects, comprising:

receiving, by a digital credential platform server, data identifying a first subset of credential receivers, out of a plurality of credential receivers associated with a field data object;

for each particular credential receiver in the first subset of credential receivers, determining a set of capabilities associated with the particular credential receiver, wherein determining set of capabilities comprises:

(a) retrieving, by the digital credential platform server, one or more digital credentials issued to the particular credential receiver;

(b) retrieving, by the digital credential platform server, data identifying one or more capabilities associated with each of the digital credentials issued to the particular credential receiver; and

(c) aggregating, by the digital credential platform server, the capabilities associated with each of the digital credentials issued to the particular credential receiver;

performing, by the digital credential platform server, an analysis on the determined sets of capabilities associated with each of the credential receivers in the first subset; and

generating, by the digital credential platform server, a model capabilities profile for the field data object, based on the analysis of the determined sets of capabilities associated with each of the credential receivers in the first subset.

9. The method of claim 8, wherein performing the analysis on the determined sets of capabilities associated with the credential receivers in the first subset comprises performing at least one of a regression analysis or a machine learning algorithm on the aggregated capabilities data, to determine one or more correlations between the capabilities of the first subset of credential receivers.

10. The method of claim 8, wherein determining the set of capabilities associated with each particular credential receiver comprises determining, for each particular credential receiver in the first subset of credential receivers:

11. The method of claim 8, wherein determining the set of capabilities associated with each particular credential receiver further comprises, for each particular credential receiver in the first subset of credential receivers:

12. The method of claim 8, further comprising:

for each particular credential receiver in the first subset of credential receivers, determining a set of traits associated with the particular credential receiver; and

performing an additional analysis of the determined sets of traits associated with each of the credential receivers in the first subset,

13. The method of claim 8, further comprising:

for each particular credential receiver in the first subset of credential receivers, determining a set of physical condition traits associated with the particular credential receiver; and

performing an additional analysis of the determined sets of physical condition traits associated with each of the credential receivers in the first subset,

14. The method of claim 8, further comprising:

prior to determining the sets of capabilities associated with each credential receiver in the first subset of credential receivers, determining the first subset of credential receivers out of the plurality of credential receivers by:

15. A non-transitory computer-readable medium, having instructions stored therein, which when executed by a computing device cause the computing device to perform a set of operations comprising:

receiving data identifying a first subset of credential receivers, out of a plurality of credential receivers associated with a field data object;

performing an analysis on the determined sets of capabilities associated with each of the credential receivers in the first subset; and

generating a model capabilities profile for the field data object, based on the analysis of the determined sets of capabilities associated with each of the credential receivers in the first subset.

16. The non-transitory computer-readable medium of claim 15, wherein performing the analysis on the determined sets of capabilities associated with the credential receivers in the first subset comprises performing at least one of a regression analysis or a machine learning algorithm on the aggregated capabilities data, to determine one or more correlations between the capabilities of the first subset of credential receivers.

17. The non-transitory computer-readable medium of claim 15, wherein determining the set of capabilities associated with each particular credential receiver comprises determining, for each particular credential receiver in the first subset of credential receivers:

18. The non-transitory computer-readable medium of claim 15, wherein determining the set of capabilities associated with each particular credential receiver further comprises, for each particular credential receiver in the first subset of credential receivers:

19. The non-transitory computer-readable medium of claim 15, wherein the instructions further cause the computing device to perform operations comprising:

20. The non-transitory computer-readable medium of claim 15, wherein the instructions further cause the computing device to perform operations comprising: