Systems and methods for identifying and inserting emoticons

• US 10,579,717 B2
• Filed: 04/25/2019
• Issued: 03/03/2020
• 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): Opsanick, Michael N
• Application Number:
  US16/394,366
• Publication Number:
  US 20190251152A1
• Time in Patent Office: 313 Days
• 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 method, comprising:

determining a first sentiment of a text received from a user;

calculating sentiment similarity scores based on a similarity between the first sentiment and a respective second sentiment of each of a plurality of candidate emoticons;

identifying one or more of the candidate emoticons having a closest similarity to the first sentiment based on the sentiment similarity scores;

providing the one or more candidate emoticons for selection by the user;

receiving the selection of one of the candidate emoticons by the user; and

inserting the selected candidate emoticon into the text.

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

3. The method of claim 1, wherein providing the one or more candidate emoticons 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 candidate emoticons.

5. The method of claim 4, wherein the sentiment similarity 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. The method of claim 1, wherein identifying one or more of the candidate emoticons having a closet sentiment similarity score to the first sentiment is further based on at least one of a preference of the user, user-related information, or recipient-related information.

11. A system, comprising:

one or more computer processors programmed to perform operations to;

determine a first sentiment of a text received from a user;

calculate sentiment similarity scores based on a similarity between the first sentiment and a respective second sentiment of each of a plurality of candidate emoticons;

identify one or more of the candidate emoticons having a closest similarity to the first sentiment based on the sentiment similarity scores;

provide the one or more candidate emoticons for selection by the user;

receive the selection of one of the candidate emoticons by the user; and

insert the selected candidate emoticon into the text.

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

13. The system of claim 11, wherein to provide the one or more candidate emoticons for selection by the user the one or more computer processors are further to:

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

14. The system of claim 11, wherein the operations are further to:

determine a respective popularity of the candidate emoticons.

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

16. The system of claim 11, wherein the operations are further to:

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

, based thereon,

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

17. The system of claim 11, wherein the operations are further to:

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

18. The system of claim 17, wherein to perform sentiment analysis to determine the first sentiment of the text the one or more computer processors are further to:

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

19. The system of claim 11, wherein the first sentiment is associated with an attitude of the user with respect to a topic described in the text.

20. A non-transitory computer-readable medium having instructions stored thereon that, when executed by one or more computer processors, cause the one or more computer processors to:

determine a first sentiment of a text received from a user;

calculate sentiment similarity scores based on a similarity between the first sentiment and a respective second sentiment of each of a plurality of candidate emoticons;

identify one or more of the candidate emoticons having a closest similarity to the first sentiment based on the sentiment similarity scores;

provide the one or more candidate emoticons for selection by the user;

receive the selection of one of the candidate emoticons by the user; and

insert the selected candidate emoticon into the text.