# US 11,115,356 B2 · Emoji recommendation system and method > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,115,356 B2 - Document ID: US11115356B2 - Status: Granted - Kind: B2 - Application number: 16/684,049 - Filed: 2019-11-14 - Published: 2021-05-20 - Granted: 2021-09-07 - Earliest claimed: 2019-11-14 - Assignees: Woofy, Inc. - Applicants: Woofy, Inc. - Inventors: Arjun Rai - Web page: https://patentgrep.com/patent/US11115356B2 ## CPC - H04L 51/046: Interoperability with other network applications or services - H04L 51/10: Multimedia information - G06F 40/205: Parsing - G06F 40/30: Semantic analysis - H04L 51/52: for supporting social networking services - H04L 51/18: Commands or executable codes - H04L 51/216: Handling conversation history, e.g. grouping of messages in sessions or threads - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A system includes a memory and at least one processor to receive text from a client computing device, the text received one character at a time, as each character of the text is received, determine a recommendation in real-time to be added to the text based on at least one of a list of rules, word embedding, an n-gram model, and a co-occurrence model, the recommendation comprising at least one of a word, a list of hashtags, a quotation, and a list of emojis, and send the recommendation to the client computing device. ## Claims ### Claim 1 A system comprising: a memory; and at least one processor to: receive text from a client computing device, the text received one character at a time as part of a payload having a particular format by a web application programming interface (API) provided by a predictive linguistics engine, the payload having the text, a fanout level, and a depth value; as each character of the text is received, determine, by the predictive linguistics engine, a recommendation in real-time to be added to the text having a similarity score above a particular threshold based on at least one of a list of rules, word embedding, an n-gram model, and a co-occurrence model, the recommendation comprising at least one of a word, a list of hashtags, a quotation, and a list of emojis; determine that the text includes a uniform resource location (URL), parse content available at the URL, and determine the recommendation based on the content available at the URL, the recommendation comprising at least one of the quotation and the list of hashtags; and send, by the predictive linguistics engine, the recommendation to the client computing device. ### Claim 2 (depends on 1) The system of claim 1, the at least one processor further to determine the list of emojis using the list of rules, each rule in the list of rules having a recommended emoji for a list of words, and at least one word in the list of words in the text. ### Claim 3 (depends on 1) The system of claim 1, wherein the recommendation is a first recommendation, the at least one processor further to receive a selection of one emoji in the list of emojis and add the emoji to the text, determine a second recommendation in real-time to be added to the text, the second recommendation comprising at least one of a word, a list of hashtags, a quotation, and a list of emojis, and send the second recommendation to the client computing device. ### Claim 4 (depends on 1) The system of claim 1, the at least one processor further to send a graphical user interface (GUI) to the client computing device and display the GUI on a display of the client computing device, the GUI showing the text and the recommendation. ### Claim 5 (depends on 4) The system of claim 4, the at least one processor further to display the text in a text box user interface element, display the word in the text box user interface element after the text, display the list of hashtags in a hashtag user interface element, display the quotation in a quotation user interface element, and display the list of emojis in an emoji user interface element. ### Claim 6 (depends on 4) The system of claim 4, the at least one processor further to receive a selection of an add to library user interface element on the GUI and add the text to a post to be published using at least one social media platform. ### Claim 7 A method comprising: receiving, by at least one processor, text from a client computing device, the text received one character at a time as part of a payload having a particular format by a web application programming interface (API) provided by a predictive linguistics engine, the payload having the text, a fanout level, and a depth value; as each character of the text is received, determining, by the predictive linguistics engine by the at least one processor, a recommendation in real-time to be added to the text having a similarity score above a particular threshold based on at least one of a list of rules, word embedding, an n-gram model, and a co-occurrence model, the recommendation comprising at least one of a word, a list of hashtags, a quotation, and a list of emojis; determining, by the at least one processor, that the text includes a uniform resource location (URL), parsing content available at the URL, and determining the recommendation based on the content available at the URL, the recommendation comprising at least one of the quotation and the list of hashtags; and sending, by the predictive linguistics engine by the at least one processor, the recommendation to the client computing device. ### Claim 8 (depends on 7) The method of claim 7, further comprising determining the list of emojis using the list of rules, each rule in the list of rules having a recommended emoji for a list of words, and at least one word in the list of words in the text. ### Claim 9 (depends on 7) The method of claim 7, wherein the recommendation is a first recommendation, the method further comprising receiving a selection of one emoji in the list of emojis and adding the emoji to the text, determining a second recommendation in real-time to be added to the text, the second recommendation comprising at least one of a word, a list of hashtags, a quotation, and a list of emojis, and sending the second recommendation to the client computing device. ### Claim 10 (depends on 7) The method of claim 7, further comprising sending a graphical user interface (GUI) to the client computing device and displaying the GUI on a display of the client computing device, the GUI showing the text and the recommendation. ### Claim 11 (depends on 10) The method of claim 10, further comprising displaying the text in a text box user interface element, displaying the word in the text box user interface element after the text, displaying the list of hashtags in a hashtag user interface element, displaying the quotation in a quotation user interface element, and displaying the list of emojis in an emoji user interface element. ### Claim 12 (depends on 10) The method of claim 10, further comprising receiving a selection of an add to library user interface element on the GUI and adding the text to a post to be published using at least one social media platform. ### Claim 13 A non-transitory computer-readable storage medium, having instructions stored thereon that, when executed by a computing device cause the computing device to perform operations, the operations comprising: receiving text from a client computing device, the text received one character at a time as part of a payload having a particular format by a web application programming interface (API) provided by a predictive linguistics engine, the payload having the text, a fanout level, and a depth value; as each character of the text is received, determining, by the predictive linguistics engine, a recommendation in real-time to be added to the text having a similarity score above a particular threshold based on at least one of a list of rules, word embedding, an n-gram model, and a co-occurrence model, the recommendation comprising at least one of a word, a list of hashtags, a quotation, and a list of emojis; determining that the text includes a uniform resource location (URL), parsing content available at the URL, and determining the recommendation based on the content available at the URL, the recommendation comprising at least one of the quotation and the list of hashtags; and sending, by the predictive linguistics engine, the recommendation to the client computing device. ### Claim 14 (depends on 13) The non-transitory computer-readable storage medium of claim 13, the operations further comprising determining the list of emojis using the list of rules, each rule in the list of rules having a recommended emoji for a list of words, and at least one word in the list of words in the text. ### Claim 15 (depends on 13) The non-transitory computer-readable storage medium of claim 13, wherein the recommendation is a first recommendation, the method further comprising receiving a selection of one emoji in the list of emojis and adding the emoji to the text, determining a second recommendation in real-time to be added to the text, the second recommendation comprising at least one of a word, a list of hashtags, a quotation, and a list of emojis, and sending the second recommendation to the client computing device. ### Claim 16 (depends on 13) The non-transitory computer-readable storage medium of claim 13, the operations further comprising sending a graphical user interface (GUI) to the client computing device and displaying the GUI on a display of the client computing device, the GUI showing the text and the recommendation. ### Claim 17 (depends on 13) The non-transitory computer-readable storage medium of claim 13, the operations further comprising receiving a selection of an add to library user interface element on the GUI and adding the text to a post to be published using at least one social media platform.