Methods and systems for synchronous communication in content provisioning

• US 10,528,876 B1
• Filed: 07/23/2018
• Issued: 01/07/2020
• Est. Priority Date: 04/08/2016
• Status: Active Grant

• Current Assignee: Pearson Education Incorporated (Pearson plc)
• Original Assignee: Pearson Education Incorporated (Pearson plc)
• Inventors: McFall, Greg
• Primary Examiner(s): Meky, Moustafa M
• Application Number:
  US16/042,844
• Time in Patent Office: 533 Days
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/322 Trees
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 16/337 Profile generation, learning or modification
• CPC Class Codes: G06F 16/338 Presentation of query results
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 40/123 Storage facilities
• CPC Class Codes: G06F 40/131 Fragmentation of text files, e.g. creating reusable text-blocks; Linking to fragments, e.g. using XInclude; Namespaces
• CPC Class Codes: G06F 40/211 Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
• CPC Class Codes: G06F 40/216 using statistical methods
• CPC Class Codes: G06F 40/226 Validation
• CPC Class Codes: G06F 40/289 Phrasal analysis, e.g. finite state techniques or chunking
• CPC Class Codes: G06F 40/35 Discourse or dialogue representation
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/02 Neural networks
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/022 : Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 5/04 : Inference or reasoning models
• CPC Class Codes: G06N 7/01 : Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 30/02 : Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0224 : based on user history
• CPC Class Codes: G06Q 30/0269 : based on user profile or attribute
• CPC Class Codes: G06Q 50/20 : Education
• CPC Class Codes: G09B 5/00 : Electrically-operated educational appliances working with questions and answers G09B7/00; simulators G09B9/00; advertising or displaying in general G09F
• CPC Class Codes: G09B 7/02 : of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
• CPC Class Codes: H04L 12/407 : with decentralised control
• CPC Class Codes: H04L 12/4641 : Virtual LANs, VLANs, e.g. virtual private networks [VPN] LAN interconnection over a bridge based backbone H04L12/462; encapsulation techniques H04L12/4633; routing of packets H04L45/00; packet switches H04L49/00; virtual private networks for security H04L63/0272
• CPC Class Codes: H04L 41/0806 : for initial configuration or provisioning, e.g. plug-and-play
• CPC Class Codes: H04L 41/142 : using statistical or mathematical methods
• CPC Class Codes: H04L 41/145 : involving simulating, designing, planning or modelling of a network
• CPC Class Codes: H04L 41/5051 : Service on demand, e.g. definition and deployment of services in real time
• CPC Class Codes: H04L 43/16 : Threshold monitoring
• CPC Class Codes: H04L 45/74591 : using content-addressable memories [CAM]
• CPC Class Codes: H04L 47/10 : Flow control; Congestion control
• CPC Class Codes: H04L 65/1069 : Session establishment or de-establishment
• CPC Class Codes: H04L 67/02 : based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/06 : specially adapted for file transfer, e.g. file transfer protocol [FTP]
• CPC Class Codes: H04L 67/10 : in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/1095 : Replication or mirroring of data, e.g. scheduling or transport for data synchronisation between network nodes
• CPC Class Codes: H04L 67/1097 : for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
• CPC Class Codes: H04L 67/146 : Markers for unambiguous identification of a particular session, e.g. session cookie or URL-encoding
• CPC Class Codes: H04L 67/306 : User profiles
• CPC Class Codes: H04L 67/52 : specially adapted for the location of the user terminal
• CPC Class Codes: H04L 67/535 : Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 67/567 : Integrating service provisioning from a plurality of service providers
• CPC Class Codes: H04L 67/61 : taking into account QoS or priority requirements
• CPC Class Codes: H04W 88/02 : Terminal devices

Abstract

Systems and methods for automated content selection and/or distribution are disclosed herein. The system can include a packet selection system including a recommendation engine. The recommendation engine can select a next data packet that can include content for delivery to a user device. The system can include a presentation system including a presenter module. The presenter module can receive an indication of the selected next data packet and send the content for delivery to the user device via an electrical communication. The system can include a response system including a response processor. The response processor can receive a response from the user device, and the response processor can determine whether the received response is a desired response. The system can include a summary model system including model engine, and a messaging bus.

Claims

1. A system for event driven content provisioning, the system comprising:

a memory comprising;

a plurality of predictive models;

information relating to at least one attribute of a student; and

content for providing to the student; and

one or more servers comprising a plurality of modules linked by a messaging bus, the messaging bus configured to generate a data stream comprising indicators of completion of actions, the one or more severs configured to;

receive a response;

indicate receipt of the response in the data stream;

evaluate the response upon identification of receipt of the response in the data stream;

indicate completion of evaluating the response in the data stream;

update a model based on the evaluated response upon identification of completion of evaluation in the data stream;

indicate completion of updating the model in the data stream;

select next content based on the updated model upon identification of updating of the model in the data stream;

indicate completion of selecting next content in the data stream; and

provide the selected next content to a user device upon identification of selection of the next content in the data stream.

2. The system of claim 1, wherein the at least some of the plurality of modules of the one or more servers comprises a plurality of subscriber modules, and wherein the data stream is delivered to the plurality of subscriber modules.

3. The system of claim 2, wherein the plurality of subscriber modules comprise a plurality of hardware modules in the one or more servers.

4. The system of claim 2, wherein the subscriber modules of the one or more servers are configured to communicate via the messaging bus.

5. The system of claim 3, wherein the subscriber modules are configured to:

receive the data stream;

parse the data stream; and

identify indicators in the data stream, wherein identification of an indicator triggers an action by the subscriber module identifying the indicator.

6. The system of claim 1, wherein the response is received from a user device associated with the student.

7. The system of claim 6, wherein evaluating the received response comprises generating an observable, wherein the observable identifies the response as either correct or incorrect.

8. The system of claim 7, wherein updating the model comprises identifying a model relevant to the received response;

and updating the model relevant to the received response based on the observable.

9. The system of claim 8, wherein the data stream comprises a plurality of data streams, each of which plurality of data streams is received by at least one subscriber.

10. The system of claim 8, wherein the at least one updated model is retrieved from a model database in the memory, the model database comprising the plurality of predictive models.

11. The system of claim 10, wherein providing the selected next content comprises retrieving the next content from a content library database in the memory.

12. The system of claim 1, wherein the output of the messaging bus is delivered according to a push model.

13. The system of claim 1, wherein the output of the messaging bus is delivered according to a pull model.

14. A method for event driven content provisioning comprising:

generating a data stream with a messaging bus, the data stream comprising indicators of completion of actions;

receiving a response to a question;

indicating the receipt of the response in the data stream;

evaluating the response upon identification of receipt of the response via the data stream;

indicating completion of evaluating the response in the data stream;

updating a model based on the evaluated response upon identification of completion of evaluation via the data stream;

indicating completion of updating the model in the data stream;

selecting next content based on the updated model upon identification of completion of updating the model via the data stream;

indicating completion of selecting next content in the data stream; and

providing the next content to a user device upon identification of selecting of the next content in the data stream.

15. The method of claim 14, wherein the data stream is delivered to a plurality of subscriber modules.

16. The method of claim 15, wherein the plurality of subscriber modules comprise:

a presenter module;

a response processor;

a model engine;

or a recommendation engine.

17. The method of claim 16, wherein the subscriber modules are located within one or more servers.

18. The method of claim 17, wherein the subscriber modules communicate only via the messaging bus.

19. The method of claim 18, wherein evaluating the received response comprises generating an observable identifying the response as either correct or incorrect, and wherein updating the model comprises:

identifying a model relevant to the received response; and

updating the model relevant to the received response based on the observable.

20. The method of claim 19, wherein the next content is selected based on the updated model.