SYSTEM AND METHOD FOR AUTOMATIC CONTENT AGGREGATION GENERATION

• US 20170293675A1
• Filed: 08/12/2016
• Published: 10/12/2017
• 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
• Granted Patent: US 10,325,215 B2
• Time in Patent Office: 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 creation, the system comprising:

memory comprising;

a content database comprising content relevant to a common subject;

an aggregation database, wherein the aggregation database is configured to store one or several 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

a server, wherein the server is configured to;

provide content to the user device via a first electrical signal communicated via the communication network;

receive a selection of a portion of the provided content from the user device via a second electrical signal communicated via the communication network;

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, wherein a plurality of branches of the parse tree are each associated with a part of speech tag;

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.

2. The system of claim 1, wherein the server is further configured to identify a subset of the noun phrases, wherein the subset of the noun phrases comprise rational phrases.

3. The system of claim 2, wherein the rational phrases are identified based on at least on one of:

size;

or the presence of a trigger attribute.

4. The system of claim 3, wherein identifying the rational phrases based on size comprises:

automatically determining the number of words in the noun phrase;

retrieving a size threshold;

comparing the number of words in the noun phrase to the size threshold; and

associating a first Boolean value with the noun phrase when the number of words is less than the size threshold and associating a second Boolean value with the noun phrase when the number of words is greater than the noun threshold.

5. The system of claim 3, wherein the trigger attribute can comprise a number.

6. The system of claim 3, wherein the processor is further configured to store the content aggregation in the aggregation database.

7. The system of claim 6, wherein content is placed in a content aggregation for each of the rational phrases.

8. The system of claim 1, wherein identifying noun phrases from the part of speech tags within the parse tree comprises:

(a) identifying the root of the parse tree;

(b) progressing from the root to a one of the plurality of branches of the parse tree;

(c) retrieving the part of speech tag associated with the one of the plurality of branches of the parse tree, and (d) identifying the part of speech tag associated with the one of the plurality of branches of the parse tree.

9. The system of claim 8, wherein identifying noun phrases from the part of speech tags within the parse tree further comprises repeating steps (a)-(d) for each of the plurality of branches of the parse tree.

10. The system of claim 1, wherein placing content associated with one of the noun phrase in a content aggregation comprises:

designating at least one word of the noun phrase as an extraction;

generating a presentation portion of the noun phrase by removing the extraction from the noun phrase;

storing the presentation portion of the noun phrase in the aggregation database; and

storing the extraction in the aggregation database, wherein the extraction is associated with the presentation portion.

11. The system of claim 10, wherein the at least one word is designated as the extraction via a random number generator.

12. The system of claim 10, wherein designating the at least one of the noun phrase as an extraction comprises:

identifying a part of speech for each of the words in the noun phrase;

retrieving an extraction parameter, wherein the extraction parameter identifies at least one part of speech for potential identification as the extraction;

identifying the words in the noun phrase for potential identification as the extraction according to the extraction parameter; and

designating the extraction from the words in the noun phrase identified for potential identification as the extraction according to the extraction parameter.

13. The system of claim 1, wherein the processor can be further configured to associate a tag characterizing the content aggregation with a portion of the content aggregation.

14. A method for creation of a content aggregation comprising:

providing content to a user device via a first electrical signal communicated via a communication network;

receiving a selection of a portion of the provided content from the user device via a second electrical signal communicated via the communication network;

automatically extracting sentences from the selected portion of the provided content via Natural Language Processing;

automatically generating a parse tree for one of the automatically extracted sentences, wherein each of a plurality of branches of the parse tree are associated with a part of speech tag;

identifying noun phrases from the part of speech tags within the parse tree;

placing content associated with one of the noun phrases in a content aggregation; and

outputting the content aggregation to the user device via a third electrical signal communicated via the communication network.

15. The method of claim 14, wherein the content is provided to the user device from a content database comprising content relevant to a common subject.

16. The method of claim 14, wherein the user device comprises:

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.

17. The method of claim 14, further comprising identifying a subset of the noun phrases, wherein the subset of the noun phrases comprises rational phrases.

18. The method of claim 17, wherein identifying the rational phrases based on size comprises:

automatically determining the number of words in the noun phrase;

retrieving a size threshold;

comparing the number of words in the noun phrase to the size threshold; and

associating a first Boolean value with the noun phrase when the number of words is less than the size threshold and associating a second Boolean value with the noun phrase when the number of words is greater than the noun threshold.

19. The method of claim 18, wherein identifying noun phrases from the part of speech tags within the parse tree comprises:

(a) identifying the root of the parse tree;

(b) progressing from the root to a one of the plurality of branches of the parse tree;

(c) retrieving the part of speech tag associated with the one of the plurality of branches of the parse tree, and

identifying the part of speech tag associated with the one of the plurality of branches of the parse tree.

20. The method of claim 19, wherein placing content associated with one of the noun phrases in a content aggregation comprises:

designating at least one word of the noun phrase as an extraction;

generating a presentation portion of the noun phrase by removing the extraction from the noun phrase;

storing the presentation portion of the noun phrase in the aggregation database; and

storing the extraction in the aggregation database, wherein the extraction is associated with the presentation portion.