System and method for automatic content aggregation evaluation

• US 10,380,126 B1
• Filed: 12/13/2016
• Issued: 08/13/2019
• 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, Hellman, Scott, Murray, William
• Primary Examiner(s): Mackes, Kris E
• Application Number:
  US15/377,863
• Time in Patent Office: 973 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 creation and multi-level evaluation of flashcards, the system comprising:

memory comprising;

a content library database, wherein the content library database is configured to store one or several flash cards;

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;

create a flash card;

retrieve the flash card from the content library database, wherein the flash card comprises a presentation portion and an extraction portion comprising a correct response, wherein the extraction portion is designated for removal from the flash card before presentation;

generate a flash card score according to one or several identified attributes of the flash card;

select to keep the flash card based on the flash card score;

extract content features from the flash card, wherein the content features comprise at least one count of at least one word type;

generate a plurality of content parameters based on the extracted content features;

retrieve a statistical model from a model database;

generate an evaluation result by inputting the content parameters into the statistical model, wherein the evaluation result is an output of the statistical model; and

determine to store the flash card based on the evaluation result.

2. The system of claim 1, wherein generating the flash card score comprises identifying a feature of the retrieved flash card and determining an attribute of the identified feature.

3. The system of claim 2, wherein selecting to keep the flash card comprises retrieving a score threshold and comparing the flash card score to the score threshold.

4. The system of claim 3, wherein the one or more servers are further configured to parse the retrieved flash card.

5. The system of claim 4, wherein the retrieved flash card is parsed according to a Natural Language Processing technique.

6. The system of claim 4, wherein the one or several content parameters comprise at least one of:

a ratio;

or an average.

7. The system of claim 6, wherein the average comprises an average vector space representation of a portion of the flash card.

8. The system of claim 7, wherein the one or more servers are further configured to retrieve an evaluation threshold, wherein the evaluation threshold comprises a value delineating between acceptable and unacceptable flash cards based on the evaluation result.

9. The system of claim 8, wherein the one or more servers are further configured to compare the evaluation result to the evaluation threshold and store the retrieved flash card when the comparison of the evaluation result to the evaluation threshold indicates that the flash card is acceptable.

10. A method for creation and multi-level evaluation of flash cards comprising:

creating a flash card;

retrieving the flash card from a content library database, wherein the flash card comprises a presentation portion and an extraction portion comprising a correct response, wherein the extraction portion is designated for removal from the flash card before presentation;

generating a flash card score according to one or several identified attributes of the flash card;

selecting to keep the flash card based on the flash card score;

extracting content features from the flash card, wherein the content features comprise at least one count of at least one word type;

generating a plurality of content parameters based on the extracted content features;

retrieving a statistical model from a model database;

generating an evaluation result by inputting the content parameters into the statistical model, wherein the evaluation result is an output of the statistical model; and

determine to store the flash card based on the evaluation result.

11. The method of claim 10, wherein generating the flash card score comprises identifying a feature of the retrieved flash card and determining an attribute of the identified feature.

12. The method of claim 11, wherein selecting to keep the flash card comprises retrieving a score threshold and comparing the flash card score to the score threshold.

13. The method of claim 12, further comprising parsing the retrieved flash card.

14. The method of claim 13, wherein the one or several content parameters comprise at least one of:

a ratio;

or an average.

15. The method of claim 14, wherein the average comprises an average vector space representation of a portion of the flash card.

16. The method of claim 15, further comprising retrieving an evaluation threshold comprising a value delineating between acceptable and unacceptable flash cards based on the evaluation result.

17. The method of claim 16, further comprising comparing the evaluation result to the evaluation threshold and storing the retrieved flash card when the comparison of the evaluation result to the evaluation threshold indicates that the flash card is acceptable.