Methods and systems for hybrid synchronous- asynchronous communication in content provisioning

• US 10,397,323 B2
• Filed: 12/28/2018
• Issued: 08/27/2019
• 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:
  US16/236,030
• Publication Number:
  US 20190141126A1
• Time in Patent Office: 242 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 method for redundant synchronous and asynchronous communication in content provisioning comprising:

generating an evaluation of the response at a response processor, wherein the response is received at the response processor from a presenter module;

generating an output with a messaging bus from data received from the presenter module indicating completion of the evaluation of the response, wherein the output of the messaging comprises an asynchronous communication;

sending an indicator of the completion of the evaluation of the response to a model engine via a synchronous communication and via the asynchronous communication, wherein the synchronous communication comprises a direct communication sent from the presenter module to the model engine;

updating at least one predictive model with the model engine in response to receipt of the indicator of the completion of the evaluation of the response;

selecting a next data packet based on the updated at least one predictive model; and

providing the selected next data packet to a user device with the presenter module.

2. The method of claim 1, further comprising receiving a response at the presenter module.

3. The method of claim 2, further comprising receiving the synchronous communication and the asynchronous communication at the model engine.

4. The method of claim 3, wherein the at least one predictive model is updated in response to the receipt of a first communication, wherein the first communication is whichever of the synchronous communication and the asynchronous communication to arrive first at the model engine.

5. The method of claim 4, wherein a second communication comprises a redundant communication.

6. The method of claim 5, wherein the second communication comprises whichever of the synchronous communication and the asynchronous communication to arrive last at the model engine.

7. The method of claim 6, wherein the next data packet is selected from one of a plurality of data packets in a content library database.

8. The method of claim 7, further comprising:

identifying at least one predictive model relevant to the received response; and

updating the at least one predictive model relevant to the received response based on the observable.

9. The method of claim 8, wherein the at least one predictive model is identified from a plurality of predictive models stored in a model database.

10. The method of claim 9, wherein the output of the messaging bus comprises a plurality of data streams.

11. The method of claim 10, wherein the messaging bus is communicatingly coupled with a queue subscription information database.

12. The method of claim 11, wherein the queue subscription information database identifies a plurality of subscribers.

13. The method of claim 12, wherein the queue subscription information database identifies at least one of the plurality of data stream associated with each of the plurality of subscribers.

14. The method of claim 13, further comprising:

identifying a private variable in the received data; and

filtering the private variable from the output of the messaging bus.