Systems and methods for content navigation with automated curation

• US 10,733,255 B1
• Filed: 08/30/2016
• Issued: 08/04/2020
• Est. Priority Date: 06/30/2016
• Status: Active Grant

• Current Assignee: Snap, Inc.
• Original Assignee: Snap, Inc.
• Inventors: Yang, Jianchao, Zhu, Yuke, Xu, Ning, Tang, Kevin Dechau, Li, Jia
• Primary Examiner(s): Robinson, Greta L
• Application Number:
  US15/251,976
• Time in Patent Office: 1,435 Days
• Field of Search: 707705, 707708, 707723, 707726, 707738, 707748, 382155, 382156, 705319
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/51 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/55 Clustering; Classification
• CPC Class Codes: G06F 16/5854 using shape and object relationship
• 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 16/954 Navigation, e.g. using categorised browsing
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06T 2207/20084 Artificial neural networks [ANN]
• CPC Class Codes: G06T 2207/30168 Image quality inspection
• CPC Class Codes: G06T 7/0002 Inspection of images, e.g. flaw detection
• CPC Class Codes: G06V 20/35 Categorising the entire scene, e.g. birthday party or wedding scene
• CPC Class Codes: G06V 20/41 Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items segmenting video sequences G06V20/49
• CPC Class Codes: H04L 51/10 Multimedia information
• CPC Class Codes: H04L 51/222 using geographical location information, e.g. messages transmitted or received in proximity of a certain spot or area
• CPC Class Codes: H04L 51/52 for supporting social networking services
• CPC Class Codes: H04L 67/01 : Protocols
• CPC Class Codes: H04L 67/55 : Push-based network services
• CPC Class Codes: H04W 4/18 : Information format or content conversion, e.g. adaptation by the network of the transmitted or received information for the purpose of wireless delivery to users or terminals

Abstract

Systems, devices, methods, media, and instructions for automated image processing and content curation are described. In one embodiment a server computer system communicates at least a portion of a first content collection to a first client device, and receives a first selection communication in response, the first selection communication identifying a first piece of content of the first plurality of pieces of content. The server analyzes analyzing the first piece of content to identify a set of context values for the first piece of content, and accesses accessing a second content collection comprising pieces of content sharing at least a portion of the set of context values of the first piece of content. In various embodiments, different content values, image processing operations, and content selection operations are used to curate the content collections.

Claims

1. A method comprising:

communicating, by a server system, at least a portion of a first content collection to a first client device, wherein the first content collection comprises a first plurality of pieces of content;

receiving, at the server system from the first client device, a first selection communication, the first selection communication identifying a first piece of content of the first plurality of pieces of content;

analyzing the first piece of content to identify a set of context values and one or more quality values for the first piece of content;

accessing a second content collection comprising pieces of content sharing at least a portion of the set of context values of the first piece of content, wherein the second content collection is selected in response to the first selection communication based on the portion of the set of context values of the first piece of content;

communicating at least a portion of the second content collection to the first client device; and

deleting a content message from a database based on a deletion trigger associated with the content message;

wherein the one or more quality values comprises an interestingness value based on predicting an expected user interest in the content message, where the interestingness value is generated using a neural network generated using a training set of content messages identified as interesting within the server system;

wherein the interestingness value is further generated based on rating inputs received at a curation tool integrated with the server system to provide operator selected training sets for the neural network; and

wherein the content message as received at the server system is associated with the deletion trigger.

2. The method of claim 1 further comprising:

receiving, at a server computer system, a content message from a first content source of a plurality of content sources, the content message comprising the first piece of content;

analyzing, by the server computer system, the content message to determine the set of context values for the first piece of content, the set of context values comprising one or more quality scores and one or more content values associated with the content message;

storing the content message in a database of the server computer system along with the one or more quality scores and the one or more context values;

analyzing, by the server computer system, the content message with a plurality of content collections of the database to identify a match between at least one of the one or more context values and a topic associated with at least a first content collection of the plurality of content collections; and

automatically adding, by the server computer system, the first piece of content to the first content collection based at least in part on the match.

3. The method of claim 2 further comprising:

receiving, at the server computer system, one or more content messages from each content source of the plurality of content sources, each content message comprising media content;

analyzing, by the server computer system, each of the one or more content messages from each content source to determine corresponding one or more quality scores and one or more content values for each content message;

storing the one or more content messages from each content source in the database of the server computer system along with the one or more quality scores and the one or more content values;

analyzing, by the server computer system, the database to identify one or more content values shared by a first set of content messages in the database;

automatically generating the first content collection based on the identification of the one or more content values shared by the first set of content messages; and

automatically transmitting, by the server computer system, a notification associated with the first content collection in response to generation of the first content collection.

4. The method of claim 2 further comprising:

receiving, at the server system from the first client device, a second selection communication associated with a piece of content of the second content collection;

accessing a third content collection comprising pieces of content sharing at least a portion of a set of content characteristics of the piece of content of the second content collection, wherein the third content collection is different than the second content collection;

communicating at least a portion of the third content collection to the first client device;

receiving, at the server system from the first client device, a return selection communication associated with the first content collection; and

retransmitting the portion of the first content collection to the first client device in response to receipt of the return selection communication.

5. The method of claim 1 wherein the neural network further comprises a convolutional neural network with a feature map including a set of content features and a set of quality features.

6. The method of claim 5 wherein the interestingness value is further generated based on feedback training data comprising feedback messages from users rating selected content messages of the training set of content messages based on previous content collections communicated to the users.

7. The method of claim 1 wherein the second content collection is generated in response to receipt of the selection communication at the server system.

8. The method of claim 1 wherein the second content collection is generated and associated with the second piece of content based on content characteristics of the second piece of content prior to receipt of the selection communication.

9. The method of claim 1 further comprising:

receiving, at the server system from the first client device, a second selection communication associated with a first piece of content of the first content collection;

accessing a third content collection comprising pieces of content sharing at least a portion of a set of content characteristics of the second piece of content, wherein the third content collection is different than the second content collection; and

communicating at least a portion of the third content collection to the first client device.

10. A server computer system comprising:

a memory; and

one or more processors coupled to the memory and configured to;

communicate at least a portion of a first content collection to a first client device, wherein the first content collection comprises a first plurality of pieces of content;

receive from the first client device, a first selection communication, the first selection communication identifying a first piece of content of the first plurality of pieces of content;

analyze the first piece of content to identify a set of context values and one or more quality values for the first piece of content;

access a second content collection comprising pieces of content sharing at least a portion of the set of context values of the first piece of content, wherein the second content collection is selected in response to the first selection communication based on the portion of the set of context values of the first piece of content;

communicate at least a portion of the second content collection to the first client device; and

delete a content message from a database based on a deletion trigger associated with the content message;

wherein the one or more quality values comprises an interestingness value based on predicting an expected user interest in the content message, where the interestingness value is generated using a neural network generated using a training set of content messages identified as interesting within the server computer system;

wherein the interestingness value is further generated based on rating inputs received at a curation tool integrated with the server computer system to provide operator selected training sets for the neural network; and

wherein the content message as received at the server computer system is associated with the deletion trigger.

11. The server computer system of claim 10 wherein the one or more processors are further configured to:

receive a content message from a first content source of a plurality of content sources, the content message comprising the first piece of content;

analyze the content message to determine the set of context values for the first piece of content, the set of context values comprising one or more quality scores and one or more content values associated with the content message;

store the content message in a database of the server computer system along with the one or more quality scores and the one or more context values;

analyze, by the server computer system, the content message with a plurality of content collections of the database to identify a match between at least one of the one or more context values and a topic associated with at least a first content collection of the plurality of content collections; and

automatically add, by the server computer system, the first piece of content to the first content collection based at least in part on the match.

12. The server computer system of claim 10 wherein the second content collection is generated in response to receipt of the selection communication at the server system.

13. The server computer system of claim 10 wherein the second content collection is generated and associated with the second piece of content based on content characteristics of the second piece of content prior to receipt of the selection communication.

14. The server computer system of claim 10 wherein the one or more processors are further configured to:

receive, at the server system from the first client device, a second selection communication associated with a first piece of content of the first content collection;

access a third content collection comprising pieces of content sharing at least a portion of a set of content characteristics of the second piece of content, wherein the third content collection is different than the second content collection; and

communicate at least a portion of the third content collection to the first client device.

15. A non-transitory computer readable medium comprising instructions that, when executed by one or more processors, cause a server computer system to perform a method comprising:

communicating at least a portion of a first content collection to a first client device, wherein the first content collection comprises a first plurality of pieces of content;

receiving from the first client device, a first selection communication, the first selection communication identifying a first piece of content of the first plurality of pieces of content;

analyzing the first piece of content to identify a set of context values and one or more quality values for the first piece of content;

accessing a second content collection comprising pieces of content sharing at least a portion of the set of context values of the first piece of content, wherein the second content collection is selected in response to the first selection communication based on the portion of the set of context values of the first piece of content;

communicating at least a portion of the second content collection to the first client device; and

deleting a content message from a database based on a deletion trigger associated with the content message;

wherein the one or more quality values comprises an interestingness value based on predicting an expected user interest in the content message, where the interestingness value is generated using a neural network generated using a training set of content messages identified as interesting within the server computer system;

wherein the interestingness value is further generated based on rating inputs received at a curation tool integrated with the server computer system to provide operator selected training sets for the neural network; and

wherein the content message as received at the server computer system is associated with the deletion trigger.

16. The non-transitory computer readable medium of claim 15 wherein the instructions further cause the server computer to perform operations comprising:

receiving, at a server computer system, a content message from a first content source of a plurality of content sources, the content message comprising the first piece of content;

analyzing, by the server computer system, the content message to determine the set of context values for the first piece of content, the set of context values comprising one or more quality scores and one or more content values associated with the content message;

storing the content message in a database of the server computer system along with the one or more quality scores and the one or more context values;

analyzing, by the server computer system, the content message with a plurality of content collections of the database to identify a match between at least one of the one or more context values and a topic associated with at least a first content collection of the plurality of content collections;

automatically adding, by the server computer system, the first piece of content to the first content collection based at least in part on the match;

receiving, at the server computer system, one or more content messages from each content source of the plurality of content sources, each content message comprising media content;

analyzing, by the server computer system, each of the one or more content messages from each content source to determine corresponding one or more quality scores and one or more content values for each content message;

storing the one or more content messages from each content source in the database of the server computer system along with the one or more quality scores and the one or more content values;

analyzing, by the server computer system, the database to identify one or more content values shared by a first set of content messages in the database;

automatically generating the first content collection based on the identification of the one or more content values shared by the first set of content messages; and

automatically transmitting, by the server computer system, a notification associated with the first content collection in response to generation of the first content collection.

17. The non-transitory computer readable medium of claim 15 wherein the neural network further comprises a convolutional neural network with a feature map including a set of content features and a set of quality features;

and

wherein the interestingness value is further generated based on feedback training data comprising feedback messages from users rating selected content messages of the training set of content messages based on previous content collections communicated to the users.

18. The non-transitory computer readable medium of claim 15 wherein the second content collection is generated in response to receipt of the selection communication at the server system.

19. The non-transitory computer readable medium of claim 15 wherein the second content collection is generated and associated with the second piece of content based on content characteristics of the second piece of content prior to receipt of the selection communication.

20. The non-transitory computer readable medium of claim 15 wherein the instructions further cause the server computer system to perform operations comprising:

receiving, from the first client device, a second selection communication associated with a first piece of content of the first content collection;

accessing a third content collection comprising pieces of content sharing at least a portion of a set of content characteristics of the second piece of content, wherein the third content collection is different than the second content collection; and

communicating at least a portion of the third content collection to the first client device.