Demographic specific language interchange system

Abstract

Apparatus and associated methods relate to a group specific language application (GSLA). In an illustrative example, a GSLA may, for example, include a slang dictionary configured to store informal expressions (IBs) with metadata such as origin, usage context, and demographic relevance. For example, the GSLA may include a viral threshold engine that evaluates engagement metrics — likes, shares, usage frequency — to determine whether an IE qualifies for inclusion. The GSLA may further include a pronunciation engine that generates voice renderings using selectable voice profiles, and a promotion module that distributes validated IBs across social media platforms based on user selection and demographic targeting. Various embodiments may advantageously enable crowd-validated slang creation and dissemination tailored to particular cultural or generational groups.

Description

TPL Docket No.: 1014-02-WODEMOGRAPHIC SPECIFIC LANGUAGE INTERCHANGE SYSTEMCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Application Serial No. 63 / 693,095, titled “DEMOGRAPHIC SPECIFIC LANGUAGE INTERCHANGE SYSTEM,” filed by Jamie Grotty on September 10, 2024.

[0002] This application incorporates the entire contents of the foregoing application(s) herein by reference.TECHNICAL FIELD

[0003] Various embodiments relate generally to language translation and informal expression management systems.SUMMARY

[0004] Apparatus and associated methods relate to a group specific language application (GSLA). In an illustrative example, a GSLA may, for example, include a slang dictionary configured to store informal expressions (IES) with metadata such as origin, usage context, and demographic relevance. For example, the GSLA may include a viral threshold engine that evaluates engagement metrics — likes, shares, usage frequency — to determine whether an IE qualifies for inclusion. The GSLA may further include a pronunciation engine that generates voice renderings using selectable voice profiles, and a promotion module that distributes validated IEs across social media platforms based on user selection and demographic targeting. Various embodiments may advantageously enable crowd-validated slang creation and dissemination tailored to particular cultural or generational groups.

[0005] Various embodiments may achieve one or more advantages. For example, some embodiments may advantageously enable inclusive communication across generational, regional, and cultural boundaries. In some implementations the GSLA may, for example, advantageously improve the accuracy and relevance of slang adoption. In some embodiments the GSLA may, for example, advantageously personalize user experience. In some implementations the GSLA may, for example, advantageously increase visibility and adoption of user-created expressions through targeted social media promotion. In some embodiments the GSLA may, for example, advantageously preserve attribution and context for informal expressions, supporting transparent and community-driven language evolution.

[0006] The details of various embodiments are set forth in the accompanying drawings and the description below. Other features and advantages will be apparent from the description and drawings, and from the claims.TPL Docket No.: 1014-02-WOBACKGROUND

[0007] Language is a dynamic and evolving form of communication that incorporates various elements (e.g., regional dialects, technical jargon, informal expressions). For example, informal expressions (e.g., slang, other colloquial terms and / or idiomatic expressions) may hold a unique place in everyday communication. These expressions, for example, may reflect cultural, societal, and / or temporal influences shaping a conversation or exchange. Informal expressions may vary widely in meaning, use, and context, for example. Informal expression, for example, may be a resource for communication and / or a potential challenge for understanding among different audiences (e.g., demographic groups, cultural groups).

[0008] For example, a use of informal expressions may sometimes implicitly or explicitly divide speakers into distinct cultural groups. For example, these groups may be categorized by geographical location, profession, age, and / or other demographic factors. For example, expressions used by a specific age group in one region may be entirely unfamiliar to older generations and / or individuals from different geographic regions. For example, professional or occupational jargon may serve as a barrier to those outside the field. For example, the informal expressions may form a kind of linguistic boundary.

[0009] Sometimes, people may learn informal expressions by asking others in their community. For example, informal learning through conversations or interactions may constitute a dominant approach in picking up new terms (e.g., by observing or asking native speakers in a given demographic group).BRIEF DESCRIPTION OF THE DRAWINGS

[0010] FIG. 1 depicts an exemplary group specific language interchange (GSLI) employed in an illustrative use-case scenario.

[0011] FIG. 2 is a block diagram depicting an exemplary group specific language interchange server (GSLIS).

[0012] FIG. 3 is a flowchart illustrating an exemplary GSLI user configuration method.

[0013] FIG. 4A, FIG. 4B, and FIG. 4C depict exemplary GSLI lexicon feed operation method.

[0014] FIG. 5 is a schematic diagram showing an exemplary home interface of a GSLI client application.

[0015] FIG. 6 is a schematic diagram showing an exemplary search interface of the GSLI client application described with reference to FIG. 5.

[0016] FIG. 7A, FIG. 7B, and FIG. 7C are schematic diagrams showing an exemplary user interface sequence for creating a new lexicon using the GSLI client application described with reference to FIG. 5.TPL Docket No.: 1014-02-WO

[0017] FIG. 8 is a schematic diagram showing an exemplary translation interface of the GSLI client application described with reference to FIG. 5.

[0018] FIG. 9 is a schematic diagram showing an exemplary user profile of the GSLI client application described with reference to FIG. 5.

[0019] Like reference symbols in the various drawings indicate like elements.DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

[0020] FIG. 1 depicts an exemplary group specific language interchange (GSLI) employed in an illustrative use-case scenario. In the depicted example, a GSLI 100 includes a user device 105. For example, the user device 105 may be a smartphone. In some examples, the user device 105 may include a personal computer. In some examples, the user device 105 may include any computing device that may be connected to the Internet.

[0021] For example, the GSLI 100 may allow a user to retrieve information of and / or create informal expressions (e.g., slang, other group specific language). For example, the user may share a created informal expression (IE) through the GSLI 100 to other users (e.g., globally, regionally, towards a target (age, professional, cultural) group).

[0022] As shown, the user device 105 includes a group specific language application (GSLA 110). For example, the GSLA 110 may include a mobile application executed by a processor of the user device 105. In some implementations, the GSLA 110 may create and share slang languages. In some embodiments, the GSLA 110 may be a social media application configured to promote interaction of IES across demographic groups.

[0023] The GSLA 110, in this example, is operably connected to a GSLIS 115. For example, the GSLA 110 and the GSLIS 115 may be connected through a communication network (e.g., the Internet).

[0024] The GSLA 110 includes a user interface engine (UTE 120). For example, a user of the user device 105 may interact (e.g., input commands, transmit and / or receive information) through a user interface generated by the UIE 120. In this example, the GSLA 110 includes a search engine 125. For example, the user may search for information (e.g., meaning, pronunciation, history, usage) of an IE. In some implementations, the search engine 125 may include a natural language search.

[0025] The GSLA 110 includes a pronunciation engine 130. For example, after receiving information of an IE, the GSLA 110 may be controlled to generate a pronunciation of the IE. In some implementations, the pronunciation engine 130 may generate the pronunciation based on a specified demographic group (e.g., age, time, profession, geographical region). For example, the pronunciation engine 130 may generate a first pronunciation based on a first demographic groupTPL Docket No.: 1014-02-WO (e.g., an 18 year-old in the 1980s located in Texas, USA). For example, the pronunciation engine 130 may generate a second pronunciation based on a first demographic group (e.g., a 12 year-old in the 2010s located in Singapore).

[0026] In some implementations, the specified demographic group may be input by the user using the user interface generated by the UIE 120. In some implementations, the specified demographic group may be determined by the GSLA 110 based on a detected location (e.g., by a global positioning system (GPS), provided by the user device 105, through a user profile) of the user. In some implementations, the specified demographic group may be determined by the GSLA 110 based on, for example, the IE. For example, the IE may only be prominent within a certain cultural group. For example, in that case, the pronunciation engine 130 may generate the pronunciation based on the certain cultural group. In some implementations, the specified demographic group may be determined by the GSLA 110 based on one or more of the above factors and other factors. In some embodiments, the slang pronunciation engine may apply an artificial intelligence model to the voice database to generate a pronunciation of a slang based on a specific person.

[0027] The GSLA 110 includes a translation engine 135. For example, the translation engine 135 may receive a user input including a word and / or a sentence via the user interface. The input may, in some implementations, include filtering parameters (e.g., a location, a time, and / or a situational characterization). Based on the input, the translation engine 135 may generate a suitable slang related to the word or sentences and the filtering parameters.

[0028] The GSLA 110, as shown, includes a GSL creation engine (GCE 140) and a GSL promotion engine (GPE 145). For example, the GCE 140 may receive a new slang (e.g., a new IE) created by the user. For example, the user may input a meaning, a spelling, and a pronunciation of the slang into the GSLA 110. For example, the GCE 140 may save the newly created slang to a dictionary.

[0029] In this example, the GSLIS 115 includes a GSL management engine (GME 150) and a GSL dictionary 155). For example, the GME 150 may be configured to process requests and signals from each of the GSLA 110. For example, the GSL dictionary 155 may store a list of IES. For example, the list of IEs may include associations of each IE with different generations (e.g., by decades, by time periods), professions, and / or locations (e.g., by the states, by country). For example, a same IE may include a different meaning in various time and / or locations. In some implementations, the GSL dictionary 155 may include a history (e.g., who first used, who first created, relevant evolution) of the IE.

[0030] In some implementations, the GME 150 may add a new IE to the GSL dictionary 155 based on a viral threshold 160. For example, the viral threshold 160 may include a (e.g., predetermined, dynamic) threshold of interactions received from other users using the GSLA 110. In someTPL Docket No.: 1014-02-WO implementations, the viral threshold 160 may include a set of interaction and / or reactions requirements for adding an IE to the GSL dictionary 155. Accordingly, for example, the GME 150 may advantageously ensure accuracy and / or acceptability of the list of IES stored in the GSL dictionary 155.

[0031] In this example, the GSLIS 115 includes a predetermined voices database 165. For example, the predetermined voices database 165 may include speaking voice of various people (e.g., celebrities, cartoon figures). In some examples, the GME 150 may use the predetermined voices database 165 to generate a pronunciation of an IE when a pronunciation request is received from the pronunciation engine 130. In some embodiments, the pronunciation engine 130 may generate the pronunciation request with a voice selected by the user of the GSLA 110.

[0032] The GPE 145, for example, may allow a user to select to promote a user-created IE. For example, the user may use the GPE 145 to promote the user created IE in targeted region(s) and / or demographic(s). As an illustrative example without limitation, a user may promote a user-created slang to facilitate addition to the GSL dictionary 155. For example, the user may create shareable content (e.g., posts, videos, short videos, images) using the user-created slang. For example, the user may promote it through the GPE 145. In some examples, the GPE 145 may advantageously increase reach of the user-created slang through various social media platforms (e.g., TikTok®, Facebook®, Instagram®). TikTok® is a registered trademark of TikTok Limited headquartered in the Cayman Islands. Facebook® is a registered trademark of Meta Platforms, INC. headquartered in Delaware, USA. Instagram® is a registered trademark of Instagram, LLC. headquartered in Delaware, USA. For example, with the promotion, the user may obtain the viral threshold 160 for adding the user-created slang into the predetermined voices database 165 faster. Various embodiments may advantageously promote understanding of IEs across generations and geographical locations.

[0033] In some examples, without the viral threshold 160, a user may create an IE by directly adding the IE to a slang dictionary. For example, the slang dictionary may be filled with uncommon lexicons and / or expressions. For example, the slang dictionary may include conflicting meanings, pronunciations, history, rendering the slang dictionary unusable. In various implementations, the viral threshold 160 may advantageously create a streamlined approach in creating and / or expanding the GSL dictionary 155. For example, the GSL dictionary 155 may be updated only when a new IE has reached a (predetermined) approval criteria (e.g., the viral threshold 160).

[0034] FIG. 2 is a block diagram depicting an exemplary group specific language interchange server (GSLIS 115). The GSLIS 115 includes a processor 205. The processor 205 may, for example, include one or more processing units. The processor 205 is operably coupled to a communication module 210. The communication module 210 may, for example, include wiredTPL Docket No.: 1014-02-WO communication. The communication module 210 may, for example, include wireless communication. In the depicted example, the communication module 210 is operably coupled to the user device 105. For example, the user device 105 may be connected to the communication module 210 through a communication network (e.g., the Internet, a satellite network, a cellular network). As shown, the communication module 210 is also operably connected to a cloud 215. For example, the GSLIS 115 may access information (e.g., geographical information, information updates, other social media platforms) through the cloud 215.

[0035] The processor 205 is operably coupled to a memory module 220. The memory module 220 may, for example, include one or more memory modules (e.g., random-access memory (RAM)). The processor 205 includes a storage module 225. The storage module 225 may, for example, include one or more storage modules (e.g., non-volatile memory). In the depicted example, the storage module 225 includes the GME 150, a interaction inducing engine (HE 230), and a GSL evaluation engine (GSLEE 235). For example, the HE 230 may be configured to generate newsfeed for each user of the GSL A 110. For example, the newsfeed may include user created content of an IE. For example, the newsfeed may be generated based on a current interaction level of the user- created content. In some implementations, the HE 230 may generate the newsfeed based on a signal received from the GPE 145. For example, a user may use the GPE 145 to promote a content to targeted demographic groups. For example, the HE 230 may generate the newsfeed to the targeted demographic groups based on the signal received from the GPE 145.

[0036] The GSLEE 235, for example, may be configured to evaluate interactions associated with each IE within the content presented at the GSL A 110. For example, the GSLEE 235 may update the GSL dictionary 155 based on interactions (e.g., likes, shares, comments) related to an IE stored in the GSL dictionary 155. In some examples, the GSLEE 235 may determine to add a new IE to the GSL dictionary 155 based on its interaction being above the viral threshold 160.

[0037] The processor 205 is further operably coupled to a data store 240. The data store 240 includes the GSL dictionary 155, the viral threshold 160, the predetermined voices database 165, and user profiles 245. For example, a new user of the GSLA 110 may be registered to create a user profile. For example, the HE 230 may generate newsfeed for the new user based on the user profile associated with the new user. In some implementations, the GSLEE 235 may update the user profiles 245 associated with the users based on their creation history of IES and / or addition of the IES to the GSL dictionary 155.

[0038] FIG. 3 is a flowchart illustrating an exemplary GSLI user configuration method. For example, the GSLI user configuration method may be performed by the GSLIS 115 when a new user installed (e.g., register) the GSLA 110. In this example, a method 300 begins when userTPL Docket No.: 1014-02-WO credentials are received in step 305. For example, the GME 150 may receive the user credentials via the UIE 120, which is displayed on the user device 105.

[0039] In step 310, the user’s age is received. For example, the GME 150 may receive this information as part of the registration process. For example, the user may input the age (e.g., the exact age, a range of age, a birthday) through a user interface generated by the UIE 120. Next, in step 315, the user's profession is received. For example, the user may input their profession through the same interface.

[0040] The user's geolocation is received in step 320. For example, the GME 150 may automatically detect the geolocation through a GPS module on the user device 105. In some examples, the user may input a location manually. At a decision point 325, it is determined whether additional information is required or provided. For example, the GME 150 may prompt the user for optional information through the UIE 120. If additional information is required or offered, the method proceeds to step 330.

[0041] In step 330, the additional information (e.g., family information) is received. For example, the GME 150 may receive this additional data to further personalize the user profile and services offered by the system. If no additional information is needed at the decision point 325, or after the step 330, in step 335, received information is stored to a user profile. For example, the GME 150 may save the user’s credentials, age, profession, geolocation, and additional information to user profiles 245.

[0042] FIG. 4A, FIG. 4B, and FIG. 4C depict exemplary GSLI lexicon feed operation method. As shown in FIG. 4 A, a method 400 may be performed by the GSLA 110 in connection with the GSLIS 115 when a user searches for an IE. In this example, the method 400 begins when a search signal is received in step 405. For example, the UIE 120 of the GSLA 110 may detect the user's search input for a specific IE through the user interface of the user device 105.

[0043] In step 410, information of the input IE is retrieved from a dictionary. For example, the GME 150 may access the GSL dictionary 155 via the GSLIS 115 to retrieve relevant information about the searched IE (e.g., definition, usage, history).

[0044] At a decision point 415, it is determined whether the IE is found in the dictionary. For example, the GME 150 may check whether the input IE exists within the GSL dictionary 155. If the IE is not found, in step 420, an IE creation interface is generated to prompt the user to create the IE, and the method 400 ends. For example, the UIE 120 may generate a user prompt on the user device 105, allowing the user to define and input a new IE.

[0045] If the IE is found, in step 425, information associated with the IE is generated for the user. For example, the UIE 120 may display the retrieved details of the IE on the user interface (e.g., the meaning, origin, pronunciation) based on a demographic group or region targeted by the user.TPL Docket No.: 1014-02-WO

[0046] At a decision point 430, it is determined whether a pronunciation signal has been received. For example, the GSLA 110 may detect if the user has requested an audio pronunciation of the IE via the user interface. If the pronunciation signal is received, in step 435, a voice clip is generated according to the information based on the user-selected voice, and the method 400 ends. For example, the pronunciation engine 130 may generate the pronunciation of the IE using the selected voice profile from the predetermined voices database 165 and play it back to the user via the user device 105. If no pronunciation signal is received, the method 400 ends.

[0047] As shown in FIG. 4B, a method 440 may be performed by the GSLIS 115 when a content of an IE receives an interaction from a user of the GSLI 100. In this example, the method 440 begins when an interaction signal is received in step 445. For example, the interaction signal may be received by the GSLEE 235, which monitors user activities such as likes, shares, or comments on content associated with an IE through the GSLA 110.

[0048] In step 450, one or more new IES related to the interaction signal are determined. For example, the GSLEE 235 may analyze the interaction data and determine if new IEs are being used in user-generated content or comments.

[0049] At a decision point 455, it is determined whether any IE exceeds a viral threshold. For example, the GSLEE 235 may compare the interactions associated with each new IE to the viral threshold 160, which tracks a predetermined level of interaction or engagement.

[0050] If an IE exceeds the viral threshold, in step 460, the IE is added to the GSL dictionary 155, and the method 440 ends. For example, the GME 150 may update the GSL dictionary 155 by including the new IE and its associated data (e.g., meaning, usage, history). If no IE exceeds the viral threshold, the method 440 ends without adding new entries to the dictionary.

[0051] As shown in FIG. 4C, a method 465 may be performed by the GSLA 110 when a user requests a translation for an IE. In this example, the method 465 begins in step 470 when a translation signal of an input phrase is received. For example, the UIE 120 may receive the user’s input phrase via the user interface and pass it to the translation engine 135.

[0052] In step 475, target translation demographics are received. For example, the translation engine 135 may receive or determine the target demographic group (e.g., based on user input, geolocation, or user profile) for translating the input phrase into a suitable IE for that group.

[0053] At a decision point 480, it is determined whether the input phrase exists. For example, the translation engine 135 may check the GSL dictionary 155 to determine if a translation exists for the input phrase based on the target demographics.

[0054] If the phrase does not exist, in step 485, an error message is returned to the user, and the method 465 ends. For example, the UIE 120 may prompt the user to create the phrase or provide additional information.TPL Docket No.: 1014-02-WO

[0055] If the phrase exists, in step 490, a translated IE is determined based on the target translation demographics, and the method 465 ends. For example, the translation engine 135 may generate and present the translated IE to the user based on the target group's specific language preferences or context.

[0056] FIG. 5 is a schematic diagram showing an exemplary home interface 500 of a GSLI client application. For example, the home interface 500 may be configured to be activated automatically when a user opens the GSLA 110 on the user device 105. The home interface 500 may include, for example, a default screen including newsfeed content 505a, 505b of some, or all users followed by the user. For example, the newsfeed content 505a, 505b may also include top trending IE (e.g., determined by the IIE 230) words. In some implementations, the IIE 230 may generate the newsfeed content 505a, 505b to include suggested public accounts associated with the user (e.g., based on the user’s searches, posts and / or reels shared, content liked).

[0057] As shown, the home interface 500 includes a heart icon 510. For example, the heart icon 510 may display a (e.g., real-time) notification showing new interactions obtained by the user (e.g., the user’s content). For example, the interactions may include likes, shares, and / or comments on their created content. The home interface 500 includes a setting icon 515. For example, the user may select the setting icon 515 to update users account settings. For example, the user account settings may include; login / logout, billing information for charges, saved posts, historic activities (e.g., the posts the user liked, shared, tagged in, commented on), account preferences, privacy settings (e.g., public or private, blocked users), changing username, credentials, and / or other personal details. In some examples, the setting icon 515 may include a selection for a preferred voice for the pronunciation engine 130. In some examples, the setting icon 515 may include adding another account.

[0058] A house icon 520 may be used to take the user back to a home screen (e.g., the default feed). A magnifying glass 525 may bring the user to the search screen (e.g., for searching a meaning or other information of an IE). A plus sign 530 may take the user to an IE create page. A talking bubble 535 may take the user to a translate screen. A stick figure icon 540 may take the user to the user’s profile.

[0059] FIG. 6 is a schematic diagram showing an exemplary search interface of the GSLI client application described with reference to FIG. 5. In this example, a search interface 600 includes a header 605 and a footer 610 from the home interface 500. For example, the UTE 120 may generate the search interface 600. Below the header 605, the search interface 600 includes a search bar 615. Three horizontal lines 620 may, for example, be used to filter search results 625. In some implementations, the three horizontal lines 620 may include a way to sort the search results 625TPL Docket No.: 1014-02-WO by time (e.g., most recent , decades, generations), location (e.g., state, country), and / or trending indication.

[0060] FIG. 7A, FIG. 7B, and FIG. 7C are schematic diagrams showing an exemplary user interface sequence for creating a new lexicon using the GSLI client application described with reference to FIG. 5. In this example, an IE creation interface 700 may be generated by the UIE 120. For example, the IE creation interface 700 may include the header 605 and the footer 610 of the home interface 500. Below the header 605, a plus sign 705 and an input box 710 are shown. For example, the user may use the input box 710 to input a new IE.

[0061] In the depicted example shown in FIG. 7 A, a main creation interface 715 includes a textbox for inputting a meaning and / or definition of the new IE. The main creation interface 715 may receive an example of the new IE in use. For example, the user may include a written description of the new IE. In some examples, the user may include a spoken description of the new IE.

[0062] In some implementations, the user may generate a written post with the new IE showing the meaning of it being used. For example, the user may make the post to include user-selected styles including font, stickers, graphics, colors, background music, or a combination thereof.

[0063] In some implementations, the user may compose a film (e.g., a reel, a short video) showing an illustrative usage of the new IE to promote the new IE using, for example, music, text overlay with the user’s voice or other sound overlay, or a combination thereof.

[0064] After completing a creatin of the new IE, as shown in FIG. 7A, the user may select an arrow icon 720. Selecting the arrow icon 720 may generate a second creating page 725 as shown in FIG. 7B. The user may review, for example, spelling, meaning, and / or their created post. Once ready, for example, the user may select a create button 730 to generate the new IE in the GSLI 100.

[0065] As shown in FIG. 7C, a congrats page 740 may be generated after the create button 730 is selected. As shown, the congrats page 740 may include a large “CONGRATS” message. In this example, the congrats page 740 includes three promotion buttons 745a, 745b, 745c for the user to promote the new IE.

[0066] A friends and followers promotion button 745a may be selected when the user promotes the new IE on the UIE 120 to connected friends (e.g., based on the user profiles 245). For example, a share other platforms button 745b may be configured to share the new IE to a user’s friend on other social media platforms connected to a user profile of the user. For example, the user profiles 245 may include connected social media accounts of each user of the GSLI 100.

[0067] A promote button 745c may be configured to allow the user to promote the new IE on all social media sites, connected or not connected in the user profile. In some implementations, when the three promotion buttons 745c is selected, the UIE 120 may generate options for the user toTPL Docket No.: 1014-02-WO promote the new IE at targeted locations. For example, the targeted locations may include statewide, nationwide, and / or worldwide promotions. In some implementations, each targeted geographical location may include a predetermined radius and / or boundary. In some implementations, the GPE 145 may be configured to allow other targeted promotions based on other demographic groupings (e.g., age, marriage status, professions).

[0068] FIG. 8 is a schematic diagram showing an exemplary translation interface of the GSLI client application described with reference to FIG. 5. In this example, a translation interface 800 includes an input box 805 configured to receive words or phrases for translation. In some implementations, a talk bubble 810 may be configured to allow the user to translate a word and / or phrase by speaking (e.g., into the user device 105). Once the translation is completed, for example, the translation engine 135 may generate an option for the user to listen to a voice to pronounce the translation by selecting a megaphone icon 815, activating the GPE 145.

[0069] FIG. 9 is a schematic diagram showing an exemplary user profile of the GSLI client application described with reference to FIG. 5. In this example, a profile page 900 includes a display area 905. The display area 905, for example, may show information related to the user (e.g., based on the user profiles 245). For example, the display area 905 may include a profile image. For example, the display area 905 may include a display of information about the user’s geographical area, social media links, and / or websites. The display area 905 may, for example, show an amount of followers of the user. In some implementations, the display area 905 may display a number of posts the user has. In some embodiments, the display area 905 may include various views for displaying and / or sorting their posts / profile page (e.g., in a grid view, a “one at a time” view, to be sorted by most viewed, to be sort by most likes).

[0070] Although various embodiments have been described with reference to the figures, other embodiments are possible.

[0071] Although an exemplary system has been described with reference to the figures, other implementations may be deployed in other industrial, scientific, medical, commercial, and / or residential applications.

[0072] In various embodiments, some bypass circuits implementations may be controlled in response to signals from analog or digital components, which may be discrete, integrated, or a combination of each. Some embodiments may include programmed, programmable devices, or some combination thereof (e.g., PL As, PLDs, ASICs, microcontroller, microprocessor), and may include one or more data stores (e.g., cell, register, block, page) that provide single or multi-level digital data storage capability, and which may be volatile, non-volatile, or some combination thereof. Some control functions may be implemented in hardware, software, firmware, or a combination of any of them.TPL Docket No.: 1014-02-WO

[0073] Computer program products may contain a set of instructions that, when executed by a processor device, cause the processor to perform prescribed functions. These functions may be performed in conjunction with controlled devices in operable communication with the processor. Computer program products, which may include software, may be stored in a data store tangibly embedded on a storage medium, such as an electronic, magnetic, or rotating storage device, and may be fixed or removable (e.g., hard disk, floppy disk, thumb drive, CD, DVD).

[0074] Although an example of a system, which may be portable, has been described with reference to the above figures, other implementations may be deployed in other processing applications, such as desktop and networked environments.

[0075] Temporary auxiliary energy inputs may be received, for example, from chargeable or single use batteries, which may enable use in portable or remote applications. Some embodiments may operate with other DC voltage sources, such as (nominal) batteries, for example. Alternating current (AC) inputs, which may be provided, for example from a 50 / 60 Hz power port, or from a portable electric generator, may be received via a rectifier and appropriate scaling. Provision for AC (e.g., sine wave, square wave, triangular wave) inputs may include a line frequency transformer to provide voltage step-up, voltage step-down, and / or isolation.

[0076] Although particular features of an architecture have been described, other features may be incorporated to improve performance. For example, caching (e.g., LI, L2, . ..) techniques may be used. Random access memory may be included, for example, to provide scratch pad memory and or to load executable code or parameter information stored for use during runtime operations. Other hardware and software may be provided to perform operations, such as network or other communications using one or more protocols, wireless (e.g., infrared) communications, stored operational energy and power supplies (e.g., batteries), switching and / or linear power supply circuits, software maintenance (e.g., self-test, upgrades), and the like. One or more communication interfaces may be provided in support of data storage and related operations.

[0077] Some systems may be implemented as a computer system that can be used with various implementations. For example, various implementations may include digital circuitry, analog circuitry, computer hardware, firmware, software, or combinations thereof. Apparatus can be implemented in a computer program product tangibly embodied in an information carrier, e.g., in a machine-readable storage device, for execution by a programmable processor; and methods can be performed by a programmable processor executing a program of instructions to perform functions of various embodiments by operating on input data and generating an output. Various embodiments can be implemented advantageously in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system,TPL Docket No.: 1014-02-WO at least one input device, and / or at least one output device. A computer program is a set of instructions that can be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result. A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0078] Suitable processors for the execution of a program of instructions include, by way of example, both general and special purpose microprocessors, which may include a single processor or one of multiple processors of any kind of computer. Generally, a processor will receive instructions and data from a read-only memory or a random-access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memories for storing instructions and data. Generally, a computer will also include, or be operatively coupled to communicate with, one or more mass storage devices for storing data files; such devices include magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and optical disks. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including, by way of example, semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, ASICs (applicationspecific integrated circuits).

[0079] In some implementations, each system may be programmed with the same or similar information and / or initialized with substantially identical information stored in volatile and / or nonvolatile memory. For example, one data interface may be configured to perform auto configuration, auto download, and / or auto update functions when coupled to an appropriate host device, such as a desktop computer or a server.

[0080] In some implementations, one or more user-interface features may be custom configured to perform specific functions. Various embodiments may be implemented in a computer system that includes a graphical user interface and / or an Internet browser. To provide for interaction with a user, some implementations may be implemented on a computer having a display device. The display device may, for example, include an LED (light-emitting diode) display. In some implementations, a display device may, for example, include a CRT (cathode ray tube). In some implementations, a display device may include, for example, an LCD (liquid crystal display). A display device (e.g., monitor) may, for example, be used for displaying information to the user. Some implementations may, for example, include a keyboard and / or pointing device (e.g., mouse, trackpad, trackball joystick), such as by which the user can provide input to the computer.TPL Docket No.: 1014-02-WO

[0081] In various implementations, the system may communicate using suitable communication methods, equipment, and techniques. For example, the system may communicate with compatible devices (e.g., devices capable of transferring data to and / or from the system) using point-to-point communication in which a message is transported directly from the source to the receiver over a dedicated physical link (e.g., fiber optic link, point-to-point wiring, daisy-chain). The components of the system may exchange information by any form or medium of analog or digital data communication, including packet-based messages on a communication network. Examples of communication networks include, e.g., a LAN (local area network), a WAN (wide area network), MAN (metropolitan area network), wireless and / or optical networks, the computers and networks forming the Internet, or some combination thereof. Other implementations may transport messages by broadcasting to all or substantially all devices that are coupled together by a communication network, for example, by using omni-directional radio frequency (RF) signals. Still other implementations may transport messages characterized by high directivity, such as RF signals transmitted using directional (i.e., narrow beam) antennas or infrared signals that may optionally be used with focusing optics. Still other implementations are possible using appropriate interfaces and protocols such as, by way of example and not intended to be limiting, USB 2.0, Firewire, ATA / IDE, RS-232, RS-422, RS-485, 802.11 a / b / g, Wi-Fi, Ethernet, IrDA, FDDI (fiber distributed data interface), token-ring networks, multiplexing techniques based on frequency, time, or code division, or some combination thereof. Some implementations may optionally incorporate features such as error checking and correction (ECC) for data integrity, or security measures, such as encryption (e.g., WEP) and password protection.

[0082] In various embodiments, the computer system may include Internet of Things (loT) devices. loT devices may include objects embedded with electronics, software, sensors, actuators, and network connectivity which enable these objects to collect and exchange data. loT devices may be in-use with wired or wireless devices by sending data through an interface to another device. loT devices may collect useful data and then autonomously flow the data between other devices.

[0083] Various examples of modules may be implemented using circuitry, including various electronic hardware. By way of example and not limitation, the hardware may include transistors, resistors, capacitors, switches, integrated circuits, other modules, or some combination thereof. In various examples, the modules may include analog logic, digital logic, discrete components, traces and / or memory circuits fabricated on a silicon substrate including various integrated circuits (e.g., FPGAs, ASICs), or some combination thereof. In some embodiments, the module(s) may involve execution of preprogrammed instructions, software executed by a processor, or some combination thereof. For example, various modules may involve both hardware and software.TPL Docket No.: 1014-02-WO

[0084] In some aspects, the techniques described herein relate to a system including: a data store including a program of instructions; and, a processor operably coupled to the data store such that, when the processor executes the program of instructions, the processor causes operations to be performed to manage informal expressions across demographic groups, the operations including: receive a user input including an informal expression and a target demographic group; retrieve metadata associated with the informal expression from a slang dictionary stored in the data store, wherein the metadata includes origin, usage, and demographic relevance of the informal expression; evaluate engagement metrics associated with the informal expression, wherein the engagement metrics include likes, shares, and usage frequency; determine whether the engagement metrics meet a viral threshold stored in the data store, wherein the viral threshold includes a predetermined level of interaction prerequisite to include in the slang dictionary; generate an update to the slang dictionary to include the informal expression when the engagement metrics meet the viral threshold, wherein the slang dictionary is configured to store the informal expression with the associated metadata; generate a voice rendering of the informal expression using a voice profile selected by a user; analyze the informal expression and filter parameters including location, time, and situational characterization to produce a corresponding informal expression to the target demographic group; transmit shareable content including the informal expression to one or more social media platforms selected by the user to deliver the shareable content to one or more targeted demographic groups; such that the informal expression is incorporated into the slang dictionary upon satisfying the viral threshold, thereby enabling crowd-validated slang creation and dissemination tailored to specific demographic communities.

[0085] In some aspects, the techniques described herein relate to a system, wherein the operations further include retrieve a history from the slang dictionary, the history including when and where the informal expression was first used, who created the informal expression, and an evolution of the informal expression.

[0086] In some aspects, the techniques described herein relate to a system, wherein the operations further include determine the viral threshold dynamically based on aggregate interaction data across multiple demographic groups.

[0087] In some aspects, the techniques described herein relate to a system, wherein the operations further include generate the voice rendering using a voice profile selected from a predetermined voices database that includes celebrity and fictional character voices.

[0088] In some aspects, the techniques described herein relate to a system, wherein the operations further include analyze geolocation data automatically detected from a user device.TPL Docket No.: 1014-02-WO

[0089] In some aspects, the techniques described herein relate to a system, wherein the operations further include evaluate feedback received from the one or more social media platforms to update the engagement metrics of the informal expression.

[0090] In some aspects, the techniques described herein relate to a system, wherein the operations further include receive user profile information including age and location and store the user profile information in association with the informal expression.

[0091] In some aspects, the techniques described herein relate to a computer-implemented method performed by at least one processor to manage informal expressions across demographic groups, the method including: receive a user input including an informal expression and a target demographic group; retrieve metadata associated with the informal expression from a slang dictionary stored in a data store, wherein the metadata includes origin, usage, and demographic relevance of the informal expression; evaluate engagement metrics associated with the informal expression, wherein the engagement metrics include likes, shares, and usage frequency; determine whether the engagement metrics meet a viral threshold stored in the data store, wherein the viral threshold includes a predetermined level of interaction prerequisite to include in the slang dictionary; generate an update to the slang dictionary to include the informal expression when the engagement metrics meet the viral threshold, wherein the slang dictionary is configured to store the informal expression with the associated metadata; generate a voice rendering of the informal expression using a voice profile selected by a user; analyze the informal expression and filter parameters including location, time, and situational characterization to produce a corresponding informal expression to the target demographic group; transmit shareable content including the informal expression to one or more social media platforms selected by the user to deliver the shareable content to one or more targeted demographic groups; such that the informal expression is incorporated into the slang dictionary upon satisfying the viral threshold, thereby enabling crowd-validated slang creation and dissemination tailored to specific demographic communities.

[0092] In some aspects, the techniques described herein relate to a method further including retrieve a history from the slang dictionary, the history including when and where the informal expression was first used, who created the informal expression, and an evolution of the informal expression.

[0093] In some aspects, the techniques described herein relate to a method further including determine the viral threshold dynamically based on aggregate interaction data across multiple demographic groups.

[0094] In some aspects, the techniques described herein relate to a method further including generate the voice rendering using a voice profile selected from a predetermined voices database that includes celebrity and fictional character voices.TPL Docket No.: 1014-02-WO

[0095] In some aspects, the techniques described herein relate to a method further including analyze geolocation data automatically detected from a user device.

[0096] In some aspects, the techniques described herein relate to a method further including evaluate feedback received from the one or more social media platforms to update the engagement metrics of the informal expression.

[0097] In some aspects, the techniques described herein relate to a method further including receive user profile information including age and location and store the user profile information in association with the informal expression.

[0098] In some aspects, the techniques described herein relate to a computer program product (CPP) including a program of instructions tangibly embodied on a non-transitory computer readable medium wherein, when the instructions are executed on a processor, the processor causes operations to be performed to manage informal expressions across demographic groups, the operations including: receive a user input including an informal expression and a target demographic group; retrieve metadata associated with the informal expression from a slang dictionary stored in a data store, wherein the metadata includes origin, usage, and demographic relevance of the informal expression; evaluate engagement metrics associated with the informal expression, wherein the engagement metrics include likes, shares, and usage frequency; determine whether the engagement metrics meet a viral threshold stored in the data store, wherein the viral threshold includes a predetermined level of interaction prerequisite to include in the slang dictionary; generate an update to the slang dictionary to include the informal expression when the engagement metrics meet the viral threshold, wherein the slang dictionary is configured to store the informal expression with the associated metadata; generate a voice rendering of the informal expression using a voice profile selected by a user; analyze the informal expression and filter parameters including location, time, and situational characterization to produce a corresponding informal expression to the target demographic group; transmit shareable content including the informal expression to one or more social media platforms selected by the user to deliver the shareable content to one or more targeted demographic groups; such that the informal expression is incorporated into the slang dictionary upon satisfying the viral threshold, thereby enabling crowd-validated slang creation and dissemination tailored to specific demographic communities.

[0099] In some aspects, the techniques described herein relate to a CPP, wherein the operations further include retrieve a history from the slang dictionary, the history including when and where the informal expression was first used, who created the informal expression, and an evolution of the informal expression.TPL Docket No.: 1014-02-WO

[0100] In some aspects, the techniques described herein relate to a CPP, wherein the operations further include determine the viral threshold dynamically based on aggregate interaction data across multiple demographic groups.

[0101] In some aspects, the techniques described herein relate to a CPP, wherein the operations further include generate the voice rendering using a voice profile selected from a predetermined voices database that includes celebrity and fictional character voices.

[0102] In some aspects, the techniques described herein relate to a CPP, wherein the operations further include evaluate feedback received from the one or more social media platforms to update the engagement metrics of the informal expression.

[0103] In some aspects, the techniques described herein relate to a CPP, wherein the operations further include receive user profile information including age and location and store the user profile information in association with the informal expression.

[0104] A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made. For example, advantageous results may be achieved if the steps of the disclosed techniques were performed in a different sequence, or if components of the disclosed systems were combined in a different manner, or if the components were supplemented with other components. Accordingly, other implementations are contemplated within the scope of the following claims.

Claims

TPL Docket No.: 1014-02-WOCLAIMSWhat is claimed is:

1. A system comprising: a data store comprising a program of instructions; and, a processor operably coupled to the data store such that, when the processor executes the program of instructions, the processor causes operations to be performed to manage informal expressions across demographic groups, the operations comprising: receive a user input comprising an informal expression and a target demographic group; retrieve metadata associated with the informal expression from a slang dictionary stored in the data store, wherein the metadata comprises origin, usage, and demographic relevance of the informal expression; evaluate engagement metrics associated with the informal expression, wherein the engagement metrics comprise likes, shares, and usage frequency; determine whether the engagement metrics meet a viral threshold stored in the data store, wherein the viral threshold comprises a predetermined level of interaction prerequisite to include in the slang dictionary; generate an update to the slang dictionary to include the informal expression when the engagement metrics meet the viral threshold, wherein the slang dictionary is configured to store the informal expression with the associated metadata; generate a voice rendering of the informal expression using a voice profile selected by a user; transmit shareable content comprising the informal expression to one or more social media platforms selected by the user to deliver the shareable content to one or more targeted demographic groups; such that the informal expression is incorporated into the slang dictionary upon satisfying the viral threshold, thereby enabling crowd-validated slang creation and dissemination tailored to specific demographic communities.

2. The system of claim 1, wherein the operations further comprise retrieve a history from the slang dictionary, the history comprising when and where the informal expression was first used, who created the informal expression, and an evolution of the informal expression.TPL Docket No.: 1014-02-WO3. The system of claim 1, wherein the operations further comprise determine the viral threshold dynamically based on aggregate interaction data across multiple demographic groups.

4. The system of claim 1, wherein the operations further comprise generate the voice rendering using a voice profile selected from a predetermined voices database that comprises celebrity and fictional character voices.

5. The system of claim 1, wherein the operations further comprise analyze geolocation data automatically detected from a user device.

6. The system of claim 1, wherein the operations further comprise evaluate feedback received from the one or more social media platforms to update the engagement metrics of the informal expression.

7. The system of claim 1, wherein the operations further comprise receive user profile information comprising age and location and store the user profile information in association with the informal expression.TPL Docket No.: 1014-02-WO8. A computer-implemented method performed by at least one processor to manage informal expressions across demographic groups, the method comprising: receive a user input comprising an informal expression and a target demographic group; retrieve metadata associated with the informal expression from a slang dictionary stored in a data store, wherein the metadata comprises origin, usage, and demographic relevance of the informal expression; evaluate engagement metrics associated with the informal expression, wherein the engagement metrics comprise likes, shares, and usage frequency; determine whether the engagement metrics meet a viral threshold stored in the data store, wherein the viral threshold comprises a predetermined level of interaction prerequisite to include in the slang dictionary; generate an update to the slang dictionary to include the informal expression when the engagement metrics meet the viral threshold, wherein the slang dictionary is configured to store the informal expression with the associated metadata; generate a voice rendering of the informal expression using a voice profile selected by a user; transmit shareable content comprising the informal expression to one or more social media platforms selected by the user to deliver the shareable content to one or more targeted demographic groups; such that the informal expression is incorporated into the slang dictionary upon satisfying the viral threshold, thereby enabling crowd-validated slang creation and dissemination tailored to specific demographic communities.

9. The method of claim 8 further comprising retrieve a history from the slang dictionary, the history comprising when and where the informal expression was first used, who created the informal expression, and an evolution of the informal expression.

10. The method of claim 8 further comprising determine the viral threshold dynamically based on aggregate interaction data across multiple demographic groups.

11. The method of claim 8 further comprising generate the voice rendering using a voice profile selected from a predetermined voices database that comprises celebrity and fictional character voices.

12. The method of claim 8 further comprising analyze geolocation data automatically detected from a user device.TPL Docket No.: 1014-02-WO13. The method of claim 8 further comprising evaluate feedback received from the one or more social media platforms to update the engagement metrics of the informal expression.

14. The method of claim 8 further comprising receive user profile information comprising age and location and store the user profile information in association with the informal expression.TPL Docket No.: 1014-02-WO15. A computer program product (CPP) comprising a program of instructions tangibly embodied on a non-transitory computer readable medium wherein, when the instructions are executed on a processor, the processor causes operations to be performed to manage informal expressions across demographic groups, the operations comprising: receive a user input comprising an informal expression and a target demographic group; retrieve metadata associated with the informal expression from a slang dictionary stored in a data store, wherein the metadata comprises origin, usage, and demographic relevance of the informal expression; evaluate engagement metrics associated with the informal expression, wherein the engagement metrics comprise likes, shares, and usage frequency; determine whether the engagement metrics meet a viral threshold stored in the data store, wherein the viral threshold comprises a predetermined level of interaction prerequisite to include in the slang dictionary; generate an update to the slang dictionary to include the informal expression when the engagement metrics meet the viral threshold, wherein the slang dictionary is configured to store the informal expression with the associated metadata; generate a voice rendering of the informal expression using a voice profile selected by a user; transmit shareable content comprising the informal expression to one or more social media platforms selected by the user to deliver the shareable content to one or more targeted demographic groups; such that the informal expression is incorporated into the slang dictionary upon satisfying the viral threshold, thereby enabling crowd-validated slang creation and dissemination tailored to specific demographic communities.

16. The CPP of claim 15, wherein the operations further comprise retrieve a history from the slang dictionary, the history comprising when and where the informal expression was first used, who created the informal expression, and an evolution of the informal expression.

17. The CPP of claim 15, wherein the operations further comprise determine the viral threshold dynamically based on aggregate interaction data across multiple demographic groups.

18. The CPP of claim 15, wherein the operations further comprise generate the voice rendering using a voice profile selected from a predetermined voices database that comprises celebrity and fictional character voices.TPL Docket No.: 1014-02-WO19. The CPP of claim 15, wherein the operations further comprise evaluate feedback received from the one or more social media platforms to update the engagement metrics of the informal expression.

20. The CPP of claim 15, wherein the operations further comprise analyze the informal expression and filter parameters comprising location, time, and situational characterization to produce a corresponding informal expression to the target demographic group.

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