Systems and methods for identifying and suggesting emoticons

• US 10,311,139 B2
• Filed: 06/02/2017
• Issued: 06/04/2019
• Est. Priority Date: 07/07/2014
• Status: Active Grant

• Current Assignee: Mz Ip Holdings, LLC (AppLovin Corporation)
• Original Assignee: Mz Ip Holdings, LLC (AppLovin Corporation)
• Inventors: Leydon, Gabriel, Bojja, Nikhil
• Primary Examiner(s): Opsasnick, Michael N
• Application Number:
  US15/612,162
• Publication Number:
  US 20170270087A1
• Time in Patent Office: 732 Days
• Field of Search: 704 4, 704 5
• CPC Class Codes: G06F 3/0219 Special purpose keyboards
• CPC Class Codes: G06F 3/0238 Programmable keyboards key guide holders G06F3/0224
• CPC Class Codes: G06F 3/04842 Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06F 40/166 Editing, e.g. inserting or deleting
• CPC Class Codes: G06F 40/169 Annotation, e.g. comment data or footnotes
• CPC Class Codes: G06F 40/20 Natural language analysis semantic analysis of natural language G06F40/30
• CPC Class Codes: G06F 40/226 Validation
• CPC Class Codes: G06F 40/253 Grammatical analysis; Style critique
• CPC Class Codes: G06F 40/289 Phrasal analysis, e.g. finite state techniques or chunking
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06Q 30/0209 Incentive being awarded or redeemed in connection with the playing of a video game
• CPC Class Codes: G07F 17/3244 Payment aspects of a gaming system, e.g. payment schemes, setting payout ratio, bonus or consolation prizes
• CPC Class Codes: H04L 51/04 Real-time or near real-time messaging, e.g. instant messaging [IM]
• CPC Class Codes: H04M 1/72436 for text messaging, e.g. short messaging services [SMS] or e-mails
• 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

Computer-implemented systems and methods are provided for suggesting emoticons for insertion into text based on an analysis of sentiment in the text. An example method includes: determining a first sentiment of text in a text field; selecting first text from the text field in proximity to a current position of an input cursor in the text field; identifying one or more candidate emoticons wherein each candidate emoticon is associated with a respective score indicating relevance to the first text and the first sentiment based on, at least, historical user selections of emoticons for insertion in proximity to respective second text having a respective second sentiment; providing one or more candidate emoticons having respective highest scores for user selection; and receiving user selection of one or more of the provided emoticons and inserting the selected emoticons into the text field at the current position of the input cursor.

Claims

1. A computer-implemented method, comprising:

receiving text entered by a user;

determining a first sentiment of the text;

searching emoticons in a corpus of emoticons,

wherein each emoticon comprises a visual representation of the emoticon and is associated with a respective second sentiment;

identifying one of the emoticons as a candidate for insertion into the text based on a score indicating relevance between the first sentiment of the text and the respective second sentiment of the candidate emoticon;

providing the candidate emoticon for selection by the user;

receiving the selection of the candidate emoticon by the user; and

inserting the selected emoticon into the text.

2. The method of claim 1, wherein the selected emoticon is inserted at a current position of an input cursor in the text.

3. The method of claim 1, wherein providing the candidate emoticon for selection by the user comprises:

presenting the candidate emoticon for selection by the user at or near a current position of an input cursor in the text.

4. The method of claim 1, comprising:

determining a respective popularity of the emoticons through crowd sourcing.

5. The method of claim 4, wherein the score is based at least in part on the popularity of the candidate emoticon.

6. The method of claim 1, comprising:

determining that the text is associated with a brand, a product, or a service; and

, based thereon,

identifying the candidate emoticon for the brand, the product, or the service.

7. The method of claim 1, comprising:

performing sentiment analysis to determine the first sentiment of the text.

8. The method of claim 7, wherein performing sentiment analysis to determine the first sentiment of the text comprises:

analyzing the text in real-time as the text is being entered by the user.

9. The method of claim 1, wherein the first sentiment is associated with an attitude of the user with respect to a topic described in the text.

10. A system, comprising:

a searchable corpus of emoticons; and

one or more computer processors programmed to perform operations comprising;

receiving text entered by a user;

determining a first sentiment of the text;

searching emoticons in the corpus,

wherein each emoticon comprises a visual representation of the emoticon and is associated with a respective second sentiment;

identifying one of the emoticons as a candidate for insertion into the text based on a score indicating relevance between the first sentiment of the text and the respective second sentiment of the candidate emoticon;

providing the candidate emoticon for selection by the user;

receiving the selection of the candidate emoticon by the user; and

inserting the selected emoticon into the text.

11. The system of claim 10, wherein the selected emoticon is inserted at a current position of an input cursor in the text.

12. The system of claim 10, wherein the operation of providing the candidate emoticon for selection by the user comprises:

presenting the candidate emoticon for selection by the user at or near a current position of an input cursor in the text.

13. The system of claim 10, wherein the operations further comprise:

determining a respective popularity of the emoticons through crowd sourcing.

14. The system of claim 13, wherein the score is based at least in part on the popularity of the candidate emoticon.

15. The system of claim 10, wherein the operations further comprise:

determining that the text is associated with a brand, a product, or a service; and

, based thereon,

identifying the candidate emoticon for the brand, the product, or the service.

16. The system of claim 10, wherein the operations further comprise:

performing sentiment analysis to determine the first sentiment of the text.

17. The system of claim 16, wherein the operation of performing sentiment analysis to determine the first sentiment of the text comprises:

analyzing the text in real-time as the text is being entered by the user.

18. The system of claim 10, wherein the first sentiment is associated with an attitude of the user with respect to a topic described in the text.

19. The system of claim 10, wherein the searchable corpus of emoticons comprises one or more user-defined emoticons, and wherein each user-defined emoticon comprises a visual representation of the user-defined emoticon and is associated with a respective natural language and respective text.

20. An article, comprising:

a non-transitory computer-readable medium having instructions stored thereon that when executed by one or more computer processors cause the computer processors to perform operations comprising;

receiving text entered by a user;

determining a first sentiment of the text;

searching emoticons in a corpus of emoticons,

wherein each emoticon comprises a visual representation of the emoticon and is associated with a respective second sentiment;

identifying one of the emoticons as a candidate for insertion into the text based on a score indicating relevance between the first sentiment of the text and the respective second sentiment of the candidate emoticon;

providing the candidate emoticon for selection by the user;

receiving the selection of the candidate emoticon by the user; and

inserting the selected emoticon into the text.