System and method for decay-based content provisioning

• US 10,419,559 B1
• Filed: 11/03/2016
• Issued: 09/17/2019
• Est. Priority Date: 04/08/2016
• Status: Active Grant

• Current Assignee: Pearson Education Incorporated (Pearson plc)
• Original Assignee: Pearson Education Incorporated (Pearson plc)
• Inventors: York, Sean, Stewart, Tim, Strong, David, Moriarty, Brian, Potter, Mark, McAllister, Angela
• Primary Examiner(s): Mackes, Kris E
• Application Number:
  US15/342,920
• Time in Patent Office: 1,048 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 content aggregation creation are disclosed herein. The system can include memory having a content database and an aggregation database. The system can include a user device having a first network interface and a first I/O subsystem. The system can include a server that can: provide content to the user device via a first electrical signal; receive a selection of a portion of the provided content from the user device via a second electrical signal; automatically extract sentences from the selected portion of the provided content via a natural language processor; automatically generate a parse tree for one of the automatically extracted sentences; identify noun phrases from the part of speech tags within the parse tree; place content associated with one of the noun phrase in a content aggregation; and output the content aggregation to the user device.

Claims

1. A system for content aggregation network evaluation comprising:

memory comprising;

a content library database, wherein the content library database is configured to store a plurality of content aggregations;

a user device comprising;

a first network interface configured to exchange data via a communication network; and

a first I/O subsystem configured to convert electrical signals to user interpretable outputs via a user interface; and

one or more servers, wherein the one or more servers are configured to;

identify a user;

retrieve task data from a content library database, wherein the task data identifies one or several tasks, and wherein the task data identifies one or several skill levels for completion of the one or several tasks;

retrieve a user mastery level, wherein the user mastery level identifies one or several user skill levels, the user skill levels determined based on the user'"'"'s responses to a plurality of questions;

determine a decay profile customized for the user, wherein the decay profile comprises a model based on a plurality of attributes of the user stored in a user profile of a deterioration of the skill levels of the user;

determine a decay modified mastery level based on the retrieved user mastery level and the determined decay profile, the decay modified mastery level indicating lower mastery than the retrieved user mastery level;

identify a plurality of content aggregations for delivery to the user based on the decay modified mastery level; and

provide the plurality of content aggregations to the user via the user device.

2. The system of claim 1, wherein the server is further configured to receive a user identifier from the user device.

3. The system of claim 2, wherein the one or more servers are further configured to determine an amount of time passed since determination of the user mastery level.

4. The system of claim 3, wherein the amount of time passed is based on one or several time stamps.

5. The system of claim 4, wherein the model of the deterioration of the user skill levels comprises a statistical model.

6. The system of claim 5, wherein the one or more servers are further configured to identify a task, wherein the task comprises at least one objective.

7. The system of claim 6, wherein the one or more servers are further configured to predict an amount of time for the user to reach the at least one objective.

8. The system of claim 7, wherein the predicted amount of time for the user to reach the at least one objective is based on the decay modified mastery level.

9. The system of claim 8, wherein the model of the deterioration of the user skill levels comprises a rate of decay of the user skill levels.

10. The system of claim 9, wherein the plurality of content aggregations are provided to the user via the user device as one or several alerts.

11. The system of 10, wherein the one or several alerts comprise code to direct the user device to provide an indicator of a received alert.

12. The system of claim 11, wherein the indicator of the received alert comprises at least one of:

an aural indicator;

a tactile indicator;

or visual indicator.

13. A method for decay based content provisioning comprising:

identifying a user;

retrieving task data from a content library database, wherein the task data identifies one or several tasks, and wherein the task data identifies one or several skill levels for completion of the one or several tasks;

retrieving a user mastery level, wherein the user mastery level identifies one or several user skill levels, the user skill levels determined based on the user'"'"'s responses to a plurality of questions;

determining a decay profile customized for the user, wherein the decay profile comprises a model based on a plurality of attributes of the user stored in a user profile of a deterioration of the skill levels of the user;

determining a decay modified mastery level based on the retrieved user mastery level and the determined decay profile, the decay modified mastery level indicating lower mastery than the retrieved user mastery level;

identifying a plurality of content aggregations for delivery to the user based on the decay modified mastery level; and

providing the plurality of content aggregations to the user via a user device.

14. The method of claim 13, further comprising determining an amount of time passed since determination of the user mastery level.

15. The method of claim 14, wherein the amount of time passed is based on one or several time stamps.

16. The method of claim 15, further comprising:

identifying a task, wherein the task comprises at least one objective; and

predicting an amount of time for the user to reach the at least one objective.

17. The method of claim 16, wherein the predicted amount of time for the user to reach the at least one objective is based on the decay modified mastery level.

18. The method of claim 17, wherein the model of the deterioration of the user skill levels comprises a rate of decay of the user skill levels.

19. The method of claim 18, wherein the plurality of content aggregations are provided to the user via the user device as one or several alerts.

20. The method of claim 19, wherein the one or several alerts comprise code to direct the user device to provide an indicator of a received alert, and wherein the indicator of the received alert comprises at least one of:

an aural indicator;

a tactile indicator;

or visual indicator.