SYSTEMS AND METHODS FOR AUTOMATED CUSTOMIZED MICROSERVICE CREATION
A global architecture (GLP), as disclosed herein, is based on the thin server architectural pattern; it delivers all its services in the form of web services and there are no user interface components executed on the GLP. Each web service exposed by the GLP is stateless, which allows the GLP to be highly scalable. The GLP is further decomposed into components. Each component is a microservice, making the overall architecture fully decoupled. Each microservice has fail-over nodes and can scale up on demand. This means the GLP has no single point of failure, making the platform both highly scalable and available. The GLP architecture provides the capability to build and deploy a microservice instance for each course-recipient-user combination. Because each student interacts with their own microservice, this makes the GLP scale up to the limit of cloud resources available—i.e. near infinity.
1 . A system for automated personalized microservice generation, the system comprising:
a memory comprising:
a custom microservice database comprising data identifying generated customized microservices and associated users; and
an asset database comprising a plurality of package-data assets; and
at least one server comprising an engagement delivery microservice, the engagement delivery microservice comprising a microservices builder module, wherein the at least one server is configured to:
receive a content request identifying requested content from a user device;
determine prior creation of a custom user microservice corresponding to the content request;
retrieve package-data asset information relevant to the content request;
create a new microservice when a custom user microservice was not previously created, wherein the new microservice corresponds to the received content request; and
deliver the requested content to the user device via the new microservice.
2 . The system of claim 1 , wherein the content request identifies a user, requested content, and a content group.
3 . The system of claim 2 , wherein the at least one server is further configured to identify capability requirements associated with the content request.
4 . The system of claim 3 , wherein the capability requirements correspond to a content type of the requested content.
5 . The system of claim 4 , wherein delivering the requested content to the user device comprises rendering the requested content with the new microservice.
6 . The system of claim 5 , wherein the at least one server is further configured to direct downloading of the new microservice to the user device.
7 . The system of claim 6 , wherein the downloading of the new microservice to the user device comprises creation of a copy of the new microservice on the user device.
8 . The system of claim 7 , wherein the new microservice comprises an independent recommendation engine.
9 . The system of claim 8 , wherein the independent recommendation engine comprises a machine-learning algorithm trained to identify next content based on an attribute of the next content and of the user.
10 . The system of claim 9 , wherein the new microservice is specific to the user and to the content group.
11 . A method of automated personali 7 Pd microservice generation, the method comprising:
receiving at least one server a content request identifying requested content from a user device;
determining prior creation of a custom user microservice corresponding to the content request;
retrieving package-data asset information relevant to the content request;
creating with the at least one server a new microservice when a custom user microservice was not previously created, wherein the new microservice corresponds to the received content request; and
delivering the requested content to the user device via the new microservice.
12 . The method of claim 11 , wherein the content request identifies a user, requested content, and a content group.
13 . The method of claim 12 , further comprising identifying capability requirements associated with the content request.
14 . The method of claim 13 , wherein the capability requirements correspond to a content type of the requested content.
15 . The method of claim 14 , wherein delivering the requested content to the user device comprises rendering the requested content with the new microservice.
16 . The method of claim 15 , further comprising directing downloading of the new microservice to the user device.
17 . The method of claim 16 , wherein the downloading of the new microservice to the user device comprises creation of a copy of the new microservice on the user device.
18 . The method of claim 17 , wherein the new microservice comprises an independent recommendation engine.
19 . The method of claim 18 , wherein the independent recommendation engine comprises a machine-learning algorithm trained to identify next content based on an attribute of the next content and of the user.
20 . The method of claim 19 , wherein the new microservice is specific to the user and to the content group.