System

Abstract

To provide a system.SOLUTION: A system includes: an emotion engine that recognizes emotions of a user; adjustment means for adjusting dialects that the user will use when becoming an adventurer and adventuring around Japan, according to the emotions by inputting a specific prompt sentence into a generative AI model; means for adjusting the difficulty of puzzles the user will encounter during the adventure according to the emotion; and means for recognizing the user's current location based on location information, and providing a dialect that is appropriate for the user's current location from among the adjusted dialects.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Traditionally, dialect learning has often been done in formal environments such as textbooks and classrooms, leaving learners with few opportunities to use their dialect in real life. Furthermore, while learning a dialect is not just about learning words, it is also important to understand the culture and customs of the region, but there are limited ways to learn these things simultaneously. [Means for solving the problem]

[0005] As a means for solving this problem, the system of the present invention includes an emotion engine that recognizes the user's emotions, an adjustment means that adjusts the dialect that the user will use when becoming an adventurer and adventuring around Japan in accordance with the emotion by inputting a specific prompt sentence into a generative AI model, a means that adjusts the difficulty of the puzzles that the user will encounter during the adventure in accordance with the emotion, and a means that recognizes the user's current location based on location information and provides a dialect that is appropriate for the user's current location from among the adjusted dialects. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] The present invention is a system in which users become adventurers and participate in a treasure hunt adventure while learning dialects from all over Japan. Specifically, users access the system through an application or website and become adventurers themselves. The system uses AI to

[0029] For example, you can teach phrases and words such as "namara" (namara) and "menkoi" (menkoi), which are dialects of the Hokkaido region.

[0030] "Example 2"

[0031] For example, if the AI ​​gives a hint to solve a mystery like "It's hidden in a really cold place," and the user knows that "namara" means "very" in the Hokkaido dialect, they can guess that the treasure is hidden in a cold place, in other words, Hokkaido.

[0032] "Example 3"

[0033] The system also includes content based on local culture. For example, the dialect phrases and words taught by the AI ​​are selected based on the customs, habits, and history of the region. This allows users to experience local culture through learning the dialect.

[0034] The processing flow of each embodiment will be described below.

[0035] "Example 1"

[0036] Step 1: The user accesses the system through an application or website and becomes an adventurer.

[0037] Step 2: The system uses AI to teach the user dialect phrases and words, such as "namara" (meaning "namara") and "menkoi" (meaning "lovely"), which are used in the Hokkaido dialect.

[0038] Step 3: The user solves the mystery using the dialect they were taught and finds the treasure. For example, if the AI ​​gives the hint to solve the mystery as "It's hidden in a really cold place," the user can guess that the treasure is hidden in a cold place, i.e., Hokkaido, if they have learned that "namara" means "very" in the Hokkaido dialect.

[0039] "Example 2"

[0040] Step 1: The system includes content based on local culture. For example, the dialect phrases and words that the AI ​​teaches are selected based on the customs, habits, and history of the region.

[0041] Step 2: Users can gain experience in contact with local culture through learning dialects. For example, the AI ​​teaches that "namara" is a dialect of the Hokkaido region, and users can learn about the climate and culture of Hokkaido from its usage and background.

[0042] Example 1

[0043] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0044] Conventional language learning systems have had the problem of making it difficult for users to enjoy learning local languages ​​and cultures. Furthermore, they lacked the functionality to manage learning progress, making it impossible to maximize the effectiveness of users' learning. Furthermore, there were no systems that allowed users to actually use the language they had learned to solve puzzles.

[0045] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0046] In this invention, the server includes means for receiving user information and managing the progress of the adventure, means for retrieving language data of the selected region from a database, and means for verifying the user's input and proceeding to the next stage if correct. This allows the user to effectively manage their learning progress while having fun learning the language and culture of the region, and to have the experience of solving puzzles using the language they have learned.

[0047] A "user" is an individual who accesses the system, registers as an adventurer, and participates in a treasure hunting adventure while learning the local language.

[0048] An "adventurer" is a user whose role in the system is to participate in a treasure hunting adventure while learning the local language.

[0049] "Regional languages" refers to dialects, unique phrases, and words from all over Japan.

[0050] The "treasure hunt adventure" is an activity in which users learn the local language, solve puzzles provided within the system, and ultimately find treasure.

[0051] "Artificial Intelligence" is a technology used to teach users phrases and words in their local language.

[0052] "Language data" is data that includes information about phrases and words related to a local language.

[0053] A "server" is a computer system responsible for receiving user information, managing the progress of the adventure, retrieving language data for the selected region from a database, and validating user input.

[0054] A "terminal" is a device through which a user accesses the system, displays language data, and receives user input.

[0055] A "database" is an information management system that stores regional language data and allows the server to retrieve it as needed.

[0056] "Progress" is information that indicates the stage of the user's adventure.

[0057] "Verification" is the process of checking whether the language phrases or words entered by the user are correct.

[0058] A "stage" refers to each stage in the progression of the adventure, and the user must meet certain conditions to proceed to the next stage.

[0059] This invention is a system in which users become adventurers and participate in a treasure hunt while learning the local language. The system operates in cooperation with three parties: a server, a terminal, and a user.

[0060] Server Roles

[0061] The server receives the user's information and manages the progress of the adventure. Based on the region selected by the user, the server retrieves language data for that region from the database. For example, if the user selects the Hokkaido region, the server retrieves language data such as "namara" (naughty) and "menkoi" (cute). The server sends the retrieved language data to the user's device and verifies the user's input. If the user enters the correct language, they are allowed to proceed to the next stage.

[0062] Device Role

[0063] A terminal is a device through which a user accesses the system. The terminal receives language data sent from the server and displays it to the user. The terminal provides an interface for the user to learn the language and displays input fields for the user to enter phrases or words in the language. For example, the terminal may display the word "namara" and its meaning and provide a field for the user to enter "namara."

[0064] User Roles

[0065] A user accesses an application or website and registers as an adventurer. The user learns phrases and words in the language presented by the system and enters them into the input field. If the user enters the language correctly, the system allows the user to proceed to the next stage. For example, if the user enters "namara" correctly, they can proceed to the next stage.

[0066] Specific examples

[0067] If the user selects the Hokkaido region, the server acquires language data such as "namara" (naughty) and "menkoi" (cute) and sends it to the terminal. The terminal displays these words to the user, and if the user inputs "namara" correctly, they can proceed to the next stage.

[0068] Prompt Sentence Examples

[0069] "I want to learn the language of the Hokkaido region. Could you please tell me the meaning of the word 'namara'?"

[0070] This system is designed to allow users to enjoy a treasure hunt adventure while learning the local language. The server, terminals, and users work together to maximize the learning effect of users and provide an experience of experiencing the local culture.

[0071] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0072] Step 1:

[0073] A user accesses an application or website.

[0074] Input: User login information (ID and password)

[0075] Output: User authentication result

[0076] Specific operation: The user opens the app on their smartphone or accesses the website in their PC browser, and the login screen appears. The user enters their ID and password and presses the login button. The server receives the entered information and verifies it against the database. If authentication is successful, the user can proceed to the next screen.

[0077] Step 2:

[0078] The server receives user information and manages the progress of the adventure.

[0079] Input: User ID

[0080] Output: User's adventure progress

[0081] Specific operation: The server receives the user's ID and retrieves the user's adventure progress from the database. For example, it checks which region the user selected in the previous session and how far they progressed. The retrieved information is sent to the user's device.

[0082] Step 3:

[0083] The user selects the region for the adventure.

[0084] Input: User's selected region

[0085] Output: Information about the selected region

[0086] Specific operation: The user selects the region they want to explore from the map or list on the screen. For example, they select the Hokkaido region. After selecting, they proceed to the next screen. The server receives the user's selection and retrieves information about the selected region.

[0087] Step 4:

[0088] The server retrieves language data for the selected region from the database.

[0089] Input: Selected region information

[0090] Output: Regional language data

[0091] Specific operation: Based on the region selected by the user, the server retrieves the language data for that region from the database. For example, it retrieves "namara" (meaning "naughty") and "menkoi" (meaning "lovely") as language data for the Hokkaido region. The retrieved data is sent to the user's device.

[0092] Step 5:

[0093] The server transmits the language data to the terminal.

[0094] Input: Regional language data

[0095] Output: Language data sent to the device

[0096] Specific operation: The server sends the acquired language data to the user's device. The data is sent in JSON format, etc. The device analyzes the received data and prepares to display it to the user.

[0097] Step 6:

[0098] The terminal displays the language data to the user.

[0099] Input: Language data sent from the server

[0100] Output: Language data displayed to the user

[0101] Specific operation: The device analyzes the received language data and displays it to the user. For example, the word "namara" and its meaning are displayed. The user can check the meaning and usage of the word on the screen.

[0102] Step 7:

[0103] The user enters a phrase or word in the language.

[0104] Input: Phrases and words in the user's language

[0105] Output: User input data

[0106] Specific operation: The user learns a phrase or word in the displayed language and enters it into the input field. For example, they enter "namara." After entering, they press the send button. The device sends the user's input data to the server.

[0107] Step 8:

[0108] The server validates the user's input and if correct proceeds to the next stage.

[0109] Input: User-entered data

[0110] Output: Verification results and next stage information

[0111] Specific operation: The server receives the user's input and verifies whether it is correct. For example, it checks whether "namara" is entered correctly. If it is correct, it allows the user to proceed to the next stage, obtains new language data, and sends it to the device.

[0112] (Application example 1)

[0113] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0114] Conventional dialect learning systems often provide users with a monotonous learning experience, making it difficult to maintain motivation to learn. Furthermore, there are few opportunities to come into contact with local culture, and dialect learning often ends up being merely language learning. Furthermore, there are limited opportunities for users to actually use the dialect they have learned, making it difficult to obtain practical learning benefits.

[0115] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0116] In this invention, the server includes: a means for a user to become an adventurer and participate in a treasure hunt while learning dialects from around Japan; a means for an AI to teach dialect phrases and words; a means for the user to solve puzzles and find treasure using the dialect that the AI ​​has taught them; a means including an application to be installed on a smartphone; a means for randomly selecting dialect phrases and words and presenting them to the user; a means for randomly determining the success or failure of the mission and proceeding to the next location; and a means for randomly determining whether the treasure is found. This allows the user to have a fun time learning dialects while experiencing local culture and achieving practical learning benefits.

[0117] "User" refers to an individual who uses the system to participate in the dialect learning and treasure hunting adventure.

[0118] An "adventurer" is a user whose role is to learn dialects and go on a treasure hunt within the system.

[0119] "Dialect" refers to regional words and phrases used throughout Japan.

[0120] The "treasure hunt" refers to a virtual adventure that users go through while learning dialects within the system.

[0121] "AI" refers to a system that uses artificial intelligence technology to teach users dialect phrases and words.

[0122] A "phrase" is a combination of words that has a particular meaning.

[0123] "Language" refers to words or expressions that have a particular meaning.

[0124] "Solving the puzzle" refers to the user using the dialect they have learned to solve problems and challenges within the system.

[0125] A "smartphone" refers to a portable information terminal that can connect to the Internet and install applications.

[0126] The "application" refers to software that is installed on smartphones and allows users to learn dialects and engage in treasure hunts.

[0127] "Random" means that something is chosen at random, without following any particular rules or order.

[0128] "Mission" refers to the task or goal that a user must achieve within the system.

[0129] "Determining success or failure" refers to the system determining whether the user has accomplished the mission.

[0130] "Proceeding to the next location" refers to the user moving to a new area or stage after completing the current mission.

[0131] "Finding treasure" refers to the user discovering the treasure, which is the ultimate goal, within the system.

[0132] This invention is a system in which users become adventurers and participate in a treasure hunt while learning dialects from around Japan. The system is provided through an application installed on a smartphone.

[0133] System configuration

[0134] The system consists of the following main components:

[0135] 1. User device: Smartphone

[0136] 2. Server: A server that manages dialect data and mission data

[0137] 3. AI Model: Generative AI model to teach dialect phrases and words

[0138] Program processing

[0139] User terminal

[0140] The user device provides an interface through an application installed on a smartphone, allowing users to participate in a treasure hunt adventure while learning the dialect. Users start the application and register as adventurers.

[0141] server

[0142] The server manages dialect data and mission data and provides them to user devices. The dialect data is stored in JSON format and includes dialect phrases and their meanings for each region. The mission data includes tasks and goals that users must complete.

[0143] AI model

[0144] The AI ​​model is responsible for teaching users dialect phrases and words. Using the generative AI model, it randomly selects dialect phrases for the user's current location and presents them to the user.

[0145] Data processing and calculation

[0146] 1. Reading dialect data: The server reads the dialect data in JSON format and provides it to the user terminal.

[0147] 2. Dialect Teaching: The AI ​​model randomly selects dialect phrases based on the user's current location and presents them to the user.

[0148] 3. Mission Judgment: The server randomly judges whether the user has completed the mission and decides whether to proceed to the next location.

[0149] 4. Treasure Hunt Judgment: The server randomly judges whether the user finds the treasure or not.

[0150] Specific examples

[0151] The user launches the application and learns the Hokkaido dialect "namara." Then, they complete a mission to move on to the next location (say, Okinawa) and learn the Okinawa dialect "churasan." Finally, they repeat this process until they find the treasure.

[0152] Prompt Sentence Examples

[0153] "Please tell me the Hokkaido dialect."

[0154] "Starting the next mission."

[0155] "Have you found any treasure?"

[0156] In this way, users can enjoy a virtual adventure while learning dialects from all over Japan.

[0157] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0158] Step 1:

[0159] The user launches the application on their smartphone.

[0160] Input: User operation (application launch)

[0161] Output: Application home screen

[0162] Specific behavior: The user taps the application icon, the application launches, the home screen is displayed, and the user selects the option to register as an adventurer.

[0163] Step 2:

[0164] The user registers as an adventurer.

[0165] Input: User registration information (name, email address, etc.)

[0166] Output: User registration completion message

[0167] Specific operation: The user enters their name and email address and presses the registration button. The server receives the registration information and stores it in the database. A message that registration is complete is displayed to the user.

[0168] Step 3:

[0169] The server reads the dialect data and provides it to the user terminal.

[0170] Input: Dialect data (JSON format)

[0171] Output: Sending dialect data to the user's terminal

[0172] Specific operation: The server reads the dialect data in JSON format, selects the appropriate dialect data based on the user's current location, and sends the selected dialect data to the user's device.

[0173] Step 4:

[0174] The AI ​​model teaches users dialect phrases and words.

[0175] Input: User's current location, dialect data

[0176] Output: Dialect phrases and their meanings

[0177] How it works: The AI ​​model randomly selects a dialect phrase based on the user's current location and presents it to the user along with its meaning. The user can then view the dialect phrase and its meaning on the screen.

[0178] Step 5:

[0179] The user starts the mission.

[0180] Input: User operation (pressing the mission start button)

[0181] Output: Display mission details

[0182] Specific operation: When the user presses the mission start button, the server loads the mission data and presents it to the user. The mission details are displayed on the screen.

[0183] Step 6:

[0184] The server determines whether the mission is successful or not.

[0185] Input: User's mission progress

[0186] Output: Mission success / failure result

[0187] Specific operation: The user progresses through the mission and sends the results to the server. The server randomly determines whether the mission was successful or not and notifies the user of the result.

[0188] Step 7:

[0189] The user proceeds to the next location.

[0190] Input: Mission success result

[0191] Output: Dialect data for the following locations

[0192] Specific operation: If the mission is successful, the server randomly selects the next location and sends the dialect data of that location to the user's device. The user then proceeds to the next location.

[0193] Step 8:

[0194] The server determines whether the treasure is found.

[0195] Input: User's mission progress

[0196] Output: Judgment result of whether or not treasure was found

[0197] Specific operation: After the user completes multiple missions, the server randomly determines whether or not the treasure is found. The result is notified to the user, and if the treasure is found, the adventure ends.

[0198] Example 2

[0199] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0200] In conventional treasure hunt games, users have limited opportunities to learn about local dialects and cultures, making it difficult to combine learning with entertainment. Furthermore, when users input their answers, they lack a system that instantly determines whether their answers are correct and provides feedback to guide them to the next step.

[0201] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0202] In this invention, the server includes a means for a user to become an adventurer and participate in a treasure hunt adventure while learning the local dialect, a means for teaching dialect phrases and words using a generative AI model, a means for the user to solve puzzles and find treasure using the dialect they have been taught, a means for analyzing the user's answer and determining whether it is correct, and a means for providing the next hint or information on the location of the treasure if the answer is correct. This allows the user to enjoy treasure hunting as entertainment while learning about the local dialect and culture.

[0203] "User" refers to an individual who uses the System to participate in a treasure hunting adventure.

[0204] An "adventurer" is a user who takes part in a treasure hunt and solves mysteries while learning the dialect.

[0205] A "regional dialect" refers to words and phrases unique to a particular region.

[0206] "Generative AI model" refers to a model that generates text using artificial intelligence technology.

[0207] "Means of teaching phrases and words" refers to methods that use generative AI models to provide dialect phrases and words to users.

[0208] "Means for solving the puzzle" refers to the method by which the user solves the presented puzzle using the dialect that they have been taught.

[0209] "Means for analyzing the answer" refers to a method for analyzing the answer entered by the user and determining whether it is correct or not.

[0210] The "means for determining whether the answer is correct" refers to a method for determining whether the user's answer is correct.

[0211] "Means for providing information about the next hint or treasure location" refers to a method for providing information to the user to proceed to the next step if the user answers correctly.

[0212] "System" refers to a device or program that includes a set of means for users to enjoy a treasure hunt adventure while learning a dialect.

[0213] This invention is a system that allows users to enjoy a treasure hunt adventure while learning local dialects. The system is realized using a server, terminals, and a generative AI model.

[0214] The server uses a generative AI model to generate dialect phrases and words. Specifically, a general artificial intelligence technology (e.g., GPT-4 (registered trademark)) is used as the generative AI model. The server inputs a prompt sentence into the generative AI model and generates hints for solving the puzzle that include dialect. For example, the following prompt sentence is used:

[0215] "Generate the following hint for the user: 'It's hidden in a really cold place.' The aim of this hint is to help the user learn the Hokkaido dialect word 'namara.'"

[0216] The generated hint is sent from the server to the user's device. The device displays the received hint to the user. The user reads the hint through the device and solves the mystery while learning the meaning of the dialect. For example, if the user receives the hint "It's hidden in a very cold place," the user will learn that "namara" means "very" in the Hokkaido dialect and will infer that the treasure is hidden in a cold place, that is, Hokkaido.

[0217] When the user enters the answer into the device, the device sends the answer to the server. The server analyzes the user's answer and determines whether it is correct. The analysis uses string comparison and natural language processing techniques. If the answer is correct, the server sends the next hint and information about the location of the treasure to the device. The device displays the received information to the user and proceeds to the next step.

[0218] This system allows users to enjoy treasure hunting as entertainment while learning about local dialects and culture. For example, a user receives the hint "It's hidden in a very cold place," learns that "namara" means "very," and infers that the treasure is hidden in Hokkaido.

[0219] In this way, by combining the server, terminal, and generative AI model, we have created a system that allows users to enjoy a treasure hunt adventure while learning dialects.

[0220] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0221] Step 1:

[0222] A user accesses the system.

[0223] A user accesses the system using a web browser or a dedicated app. The device receives the user's access request and sends it to the server. The server verifies the user's authentication information and grants access. The input is the user's authentication information, and the output is the access permission status.

[0224] Step 2:

[0225] The server uses a generative AI model to generate clues to solve the puzzle.

[0226] The server inputs a prompt into the generative AI model and generates a hint for solving the puzzle, including the dialect. An example of a specific prompt is: "Generate the following hint for the user: 'It's hidden in a really cold place.' The aim of this hint is for the user to learn the Hokkaido dialect 'namara.'" The input is the prompt, and the output is the generated hint.

[0227] Step 3:

[0228] The server sends the generated hint to the terminal.

[0229] The server sends the generated hint to the user's terminal, and the terminal displays the received hint to the user. The input is the generated hint, and the output is the hint displayed to the user.

[0230] Step 4:

[0231] Users receive hints and solve puzzles while learning the meaning of dialect words.

[0232] The user reads the hint displayed on the terminal and checks the dialect meaning. For example, if the user receives the hint "It's hidden in a very cold place," the user learns that "namara" means "very" in the Hokkaido dialect. The input is the displayed hint, and the output is the user's understanding.

[0233] Step 5:

[0234] The user enters the answer into the terminal.

[0235] The user inputs the answer to the puzzle into the terminal. For example, they might input "The treasure is hidden in Hokkaido." The terminal then sends the user's answer to the server. The input is the user's answer, and the output is the answer sent to the server.

[0236] Step 6:

[0237] The server analyzes the user's answer and determines whether it is correct.

[0238] The server receives the user's input and analyzes it to determine whether it is correct. The analysis uses string comparison and natural language processing techniques. The input is the user's answer, and the output is the result of the judgment as to whether it is correct or not.

[0239] Step 7:

[0240] The server sends the results to the device, providing information about the next hint and treasure location.

[0241] The server sends the analysis results to the terminal, which displays the results to the user and, if the answer is correct, provides the next hint and information about the location of the treasure. The input is the analysis results, and the output is the result displayed to the user and the next hint.

[0242] (Application example 2)

[0243] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0244] Opportunities to learn about regional cultures and dialects are declining in modern education and entertainment. Furthermore, the number of educational materials and applications for learning dialects is limited, making learning monotonous. Furthermore, the lack of interactive experiences that allow users to have fun while learning dialects makes it difficult to keep users engaged.

[0245] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0246] In this invention, the server includes: a means for a user to become an adventurer and participate in a treasure hunt while learning dialects from around Japan; a means for an AI to teach dialect phrases and words; a means for the user to solve puzzles and find treasure using the dialect that the user has learned; a means for generating puzzles that include dialect using a generative AI model; and a means for providing an application to be installed on a smartphone. This allows users to enjoy an interactive puzzle game while learning dialects, providing an experience of experiencing local culture.

[0247] A "user" is an individual who uses the system to learn dialects and participate in a puzzle-solving game.

[0248] An "adventurer" is a user whose role is to participate in a treasure hunt adventure while learning dialects within the system.

[0249] "Dialect" refers to regional words and phrases used throughout Japan.

[0250] A "treasure hunt adventure" refers to a series of activities in which users learn dialects, solve puzzles, and ultimately find treasure.

[0251] "AI" refers to artificial intelligence, which is responsible for teaching users dialect phrases and words.

[0252] A "phrase" is a combination of words that has a particular meaning.

[0253] "Language" refers to words or expressions that have a particular meaning.

[0254] "Solving a mystery" refers to the act of understanding the given hints and questions and deriving the correct answer.

[0255] "Finding treasure" refers to solving a mystery and ultimately discovering a desired location or object.

[0256] A "generative AI model" refers to a system that uses artificial intelligence to automatically generate puzzle-solving questions that include dialects.

[0257] "Puzzle Problem" refers to a problem or challenge given to the user to solve.

[0258] A "smartphone" is a type of mobile phone that can be used to install applications.

[0259] "Application" refers to software that is installed on a smartphone and provides specific functions or services.

[0260] The system for implementing this invention allows users to become adventurers and participate in a treasure hunt while learning dialects from all over Japan. A specific embodiment of this system will be described below.

[0261] 1. System Configuration

[0262] The system consists of the following main components:

[0263] User device (smartphone)

[0264] server

[0265] Generative AI model (OpenAI® GPT-3®)

[0266] 2. Program Processing

[0267] User terminal

[0268] The user device is a smartphone with a dedicated application installed, which provides an interface for users to learn dialects and participate in puzzle-solving games.

[0269] server

[0270] The server has the following functions:

[0271] Dialect database management

[0272] Generating puzzles using generative AI models

[0273] User progress management

[0274] Generative AI Models

[0275] A generative AI model (OpenAI GPT-3) is used to automatically generate puzzles that include dialects. Specifically, given the following prompt sentence as input, it generates appropriate puzzles.

[0276] Data processing and calculation

[0277] The server retrieves specific dialects and their meanings from a dialect database and inputs prompt sentences into the generative AI model. The generated puzzles are then sent to the user's device, where the user answers them.

[0278] 3. Specific Examples

[0279] When users launch the application, they are first shown a section to learn the Hokkaido dialect. They learn that "namara" means "very." They are then presented with a puzzle generated by a generative AI model (GPT-3).

[0280] Prompt Sentence Examples

[0281] Use the Hokkaido dialect "namara" to generate puzzles that help users find the treasure.

[0282] The user remembers that "namara" means "very," and guesses that "cold place" refers to Hokkaido. After entering the correct answer, the next puzzle question is displayed.

[0283] In this way, users can enjoy an interactive puzzle game while learning dialects, providing an experience of experiencing local culture.

[0284] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0285] Step 1:

[0286] The user launches an application.

[0287] Input: User action (launching an application)

[0288] Output: Application home screen

[0289] What Happens: A user taps on an application icon on their smartphone, launching the application and displaying the home screen, which presents the user with options to begin their adventure.

[0290] Step 2:

[0291] The user selects the dialect learning section.

[0292] Input: User action (selection of dialect learning section)

[0293] Output: Dialect learning screen

[0294] What it does: A user taps the "Learn Dialects" button on the home screen and is taken to the dialect learning section, where they are shown dialects for a specific region and their meanings.

[0295] Step 3:

[0296] The server obtains dialect information from the dialect database.

[0297] Input: User's choice (specific region)

[0298] Output: Dialect information (e.g. "namara" -> "very")

[0299] Specific operation: The server accesses the dialect database and obtains dialect information for the region selected by the user. The obtained information is sent to the user's device and displayed on the dialect learning screen.

[0300] Step 4:

[0301] The user learns the dialect.

[0302] Input: Dialect information

[0303] Output: User comprehension (internal data)

[0304] What it does: The user reads and learns the dialect information displayed. The application records the user's learning progress as internal data.

[0305] Step 5:

[0306] The user starts the puzzle game.

[0307] Input: User action (start of puzzle game)

[0308] Output: Display of the puzzle

[0309] Specific operation: The user taps the "Start puzzle game" button to start the game. The server sends a prompt to the generative AI model.

[0310] Step 6:

[0311] The server sends a prompt to the generative AI model to generate a puzzle.

[0312] Input: Prompt text (e.g., "Please generate a puzzle to help the user find the treasure using the Hokkaido dialect word 'namara'.")

[0313] Output: Generated puzzle

[0314] Specific operation: The server sends a prompt to the generative AI model (OpenAI GPT-3) to generate a puzzle that includes dialect. The generated puzzle is returned to the server.

[0315] Step 7:

[0316] The server transmits the generated puzzle to the user terminal.

[0317] Input: Generated puzzle

[0318] Output: The puzzle displayed on the user's device

[0319] Specific operation: The server sends the generated puzzle to the user's terminal, and the problem is displayed on the user's terminal.

[0320] Step 8:

[0321] The user answers the puzzle questions.

[0322] Input: User's answer

[0323] Output: Answer evaluation result

[0324] Specific operation: The user enters the answer to the puzzle and taps the send button. The user device sends the answer to the server.

[0325] Step 9:

[0326] The server evaluates the user's answers.

[0327] Input: User's answer

[0328] Output: Evaluation result (correct / incorrect)

[0329] Specific operation: The server evaluates the user's answer and determines whether it is correct or incorrect. The evaluation result is sent to the user's device.

[0330] Step 10:

[0331] The user terminal displays the evaluation results.

[0332] Input: Evaluation result

[0333] Output: Feedback to the user

[0334] Specific operation: The user device receives the evaluation result and displays feedback to the user. If the answer is correct, the next puzzle question will be displayed.

[0335] Example 3

[0336] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0337] Conventional dialect learning systems have limited opportunities for users to learn about local culture, customs, and history, resulting in a lack of interactive elements that allow users to effectively learn dialect phrases and words, which often leads to a lack of motivation to learn.

[0338] The identification process by the identification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for the user to become an adventurer and participate in a treasure hunt adventure while learning the local dialect, a means for the generative AI model to generate dialect phrases and words based on a prompt sentence, a means for the user to solve puzzles and find treasure using the dialect that the user has been taught, a means for the server to compare the generated content with a local culture database and select appropriate content, and a means for the terminal to display the generated dialect phrases and words to the user. This allows the user to experience local culture through learning the dialect.

[0339] "User" refers to an individual who uses the system to participate in dialect learning and treasure hunting adventures.

[0340] "Adventurer" refers to the role of a user who takes part in a treasure hunt adventure while learning dialects within the system.

[0341] A "dialect" refers to a unique linguistic expression or phrase used in a particular region or community.

[0342] "Generative AI model" refers to an artificial intelligence algorithm that generates dialect phrases and words based on user prompts.

[0343] "Prompt text" refers to the text of an instruction or question that a user enters into a system.

[0344] "Server" refers to the computer system that manages the processing of the entire system and connects with generative AI models and databases.

[0345] A "regional cultural database" refers to a database containing information about the customs, habits, and history of a particular region.

[0346] "Terminal" refers to the device through which a User accesses the System and displays dialect phrases and words.

[0347] The "treasure hunt adventure" refers to an activity in which users solve puzzles within the system and find treasure while learning the dialect.

[0348] "Solutions" refers to how users use the dialect they have been taught to solve problems and challenges within the system.

[0349] This invention is a system that allows users to participate in a treasure hunt adventure while learning a local dialect. The system uses a generative AI model to generate dialect phrases and words based on prompts and provides them to the user. A specific embodiment of this system is described below.

[0350] Hardware and software used

[0351] server

[0352] The server manages the overall processing of the system and coordinates with the generative AI model and the local cultural database. The server is a high-performance computer system that communicates with terminals via the Internet.

[0353] Terminal

[0354] A terminal is a device through which a user accesses the system and displays dialect phrases and words. A terminal can be a PC, smartphone, tablet, or other device with an internet connection.

[0355] Generative AI Models

[0356] A generative AI model is an artificial intelligence algorithm that generates dialect phrases and words based on a user prompt, specifically using an advanced natural language processing model such as OpenAI's GPT-4.

[0357] Regional Cultural Database

[0358] A regional cultural database is a database containing information about the customs, habits, and history of a particular region. The server references this database to verify the appropriateness of generated dialect phrases and words.

[0359] Program processing

[0360] The server receives a prompt from the user and inputs it into the generative AI model. The generative AI model generates dialect phrases and words based on the prompt. The server compares the generated content with the local cultural database and selects appropriate content. The selected content is sent from the server to the device, which displays it to the user.

[0361] Specific examples

[0362] For example, if the user inputs "Teach me how to greet people in the Osaka dialect," the following processing will be performed.

[0363] 1. The user types "Tell me how to greet someone in Osaka dialect" into the input field on the terminal.

[0364] 2. The terminal sends this prompt to the server.

[0365] 3. The server receives the prompt and inputs the prompt "Tell me how to greet someone in Osaka dialect" into the generative AI model.

[0366] 4. The generative AI model generates phrases such as "Maido!" and "Ookinii!"

[0367] 5. The server matches the generated phrase with the local cultural database and selects appropriate content.

[0368] 6. The server sends the selected phrase to the terminal.

[0369] 7. The device displays "Welcome!" or "Thank you!" to the user.

[0370] In this way, the user can gain experience of coming into contact with the local culture through learning the dialect. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0371] Step 1:

[0372] The user inputs a prompt into the input field of the terminal. For example, the user inputs "Tell me how to greet someone in the Osaka dialect." The input prompt is temporarily stored in the terminal's memory.

[0373] Step 2:

[0374] The terminal sends the entered prompt text to the server. The terminal uses an Internet connection to send the prompt text to the server as a data packet. The input is the prompt text, and the output is a notification to the server that transmission has been completed.

[0375] Step 3:

[0376] The server receives the prompt text. The server analyzes the received prompt text and converts it into a format for input to the generative AI model. The input is the prompt text, and the output is the data format for input to the generative AI model.

[0377] Step 4:

[0378] The server inputs a prompt sentence into the generative AI model, which generates dialect phrases and words based on the prompt sentence. The input is the converted prompt sentence, and the output is the generated dialect phrases and words.

[0379] Step 5:

[0380] The generative AI model generates dialect phrases and words. For example, phrases such as "Maido!" and "Ookinii!" are generated. The generated data is returned to the server. The input is the prompt sentence, and the output is the generated phrase or word.

[0381] Step 6:

[0382] The server matches the generated content with the local cultural database. The server compares the generated phrases or words with the information in the database and selects the appropriate content. The input is the generated phrases or words, and the output is the match result.

[0383] Step 7:

[0384] The server selects the appropriate content. For example, it determines that "Maido!" and "Ookini!" are appropriate Osaka dialects. The selected content is stored in the server's memory. The input is the matching result, and the output is the selected phrase or word.

[0385] Step 8:

[0386] The server sends the selected content to the terminal. The server sends the selected phrase or word to the terminal as a data packet over the Internet. The input is the selected phrase or word, and the output is a notification to the terminal that transmission has been completed.

[0387] Step 9:

[0388] The terminal displays the generated dialect phrases and words to the user. The terminal analyzes the received data and displays it on the screen. For example, "Maido!" or "Ookinii!" are displayed. The input is the phrase or word received from the server, and the output is what is displayed to the user.

[0389] (Application example 3)

[0390] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0391] Conventional language learning systems have the problem that users have few opportunities to come into contact with the local culture and history, and end up only learning the language. Furthermore, there is a lack of effective ways for tourists to learn the language and culture of the region they visit, which makes it difficult to communicate smoothly with local residents. This limits the tourist experience and prevents them from fully enjoying the charm of the region.

[0392] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0393] In this invention, the server includes means for a user to become an adventurer and participate in a treasure hunt while learning the local language, means for an artificial intelligence to teach language phrases and words, means for the user to solve puzzles and find treasure using the language that has been taught, means for providing a language phrasebook for each region, means for providing a language pronunciation guide, means for providing information on the history and customs of the region, and means for providing an interactive program that helps communication with local residents. This allows users to learn the local language while experiencing the culture and history of the region, making for a deeper and richer tourist experience.

[0394] "User" refers to an individual who uses the system to learn about local languages ​​and cultures and participate in treasure hunting adventures.

[0395] "Adventurer" refers to a user whose role within the system is to participate in a treasure hunting adventure while learning the local language.

[0396] "Regional language" refers to the dialects and idiosyncratic words used in a particular region.

[0397] "Artificial intelligence" refers to computer programs used to teach users language phrases and words.

[0398] A "language phrase" refers to a short word or expression that is commonly used in a particular region.

[0399] "Language" refers to words and expressions used in a particular region.

[0400] "Solving the puzzle" refers to the act of a user using the language they have been taught to solve a problem or challenge within the system.

[0401] "Finding treasure" refers to the act of a user achieving a goal or reward set within the system.

[0402] A "language phrasebook" refers to a database of phrases in a language used in a particular region.

[0403] "Pronunciation Guide" refers to an audio playback feature that helps users learn correct pronunciation.

[0404] "Information on history and customs" refers to data about the history, culture, and habits of a particular region.

[0405] "Interactive programs" refer to chatbots and dialogue systems that help communicate with local residents.

[0406] A system for implementing this invention includes a server, a user terminal, and artificial intelligence. The server provides a program that allows users to become adventurers and participate in a treasure hunt while learning the local language. The user terminal is a device such as a smartphone or tablet, and provides an interface for users to access the system and learn the local language and culture. The artificial intelligence is used to teach the user phrases and words in the language.

[0407] The server has a means for providing regional language phrasebooks and provides appropriate language phrases based on the region selected by the user. In addition, the server provides a language pronunciation guide and has an audio playback function to help the user learn correct pronunciation. Information about the history and customs of the region is also provided by the server, allowing the user to learn about the culture and history of the region.

[0408] The user terminal provides an interactive program that helps users communicate with local residents, allowing them to communicate smoothly with local residents and gain a deeper understanding of local culture.

[0409] As a concrete example, consider a tourist visiting Osaka who uses the app. When the tourist opens the app and selects "Osaka," the following dialect phrases and cultural information are displayed:

[0410] Dialect phrase: "Nandeyanen"

[0411] Cultural information: "Osaka is a commercial center and has a rich food culture."

[0412] An example prompt is:

[0413] "Please tell me the Osaka dialect."

[0414] "Please tell me about Osaka culture."

[0415] In this way, users can learn the local language while also experiencing the local culture and history, making their tourist experience deeper and richer.

[0416] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0417] Step 1:

[0418] The user launches the application using the terminal.

[0419] Input: User actions

[0420] Output: Application launch screen

[0421] Specific action: A user taps on an app on a smartphone or tablet, and the application launches.

[0422] Step 2:

[0423] Select the area the user will visit.

[0424] Input: User's region selection

[0425] Output: Selected region information

[0426] Specific behavior: The user selects the region they want to visit (e.g., Osaka) from the in-app menu.

[0427] Step 3:

[0428] The server provides a local language phrasebook based on the selected region.

[0429] Input: Selected region information

[0430] Output: Regional language phrasebooks

[0431] Specific operation: The server retrieves language phrases for the selected region (e.g., Osaka) from the database and sends them to the user's terminal.

[0432] Step 4:

[0433] The user learns language phrases.

[0434] Input: Regional Language Phrasebook

[0435] Output: User's learning progress

[0436] What it does: The user reads the language phrases displayed in the app and plays the pronunciation guide to learn.

[0437] Step 5:

[0438] The server provides information about the history and customs of the area.

[0439] Input: Selected region information

[0440] Output: Information about the history and customs of the area

[0441] Specific operation: The server retrieves information about the history and customs of the selected region (e.g., Osaka) from the database and sends it to the user's terminal.

[0442] Step 6:

[0443] Users learn information about local history and customs.

[0444] Input: Information about local history and customs

[0445] Output: User's learning progress

[0446] Specific Action: The user reads and understands information about local history and customs displayed within the app.

[0447] Step 7:

[0448] The server provides an interactive program, allowing users to communicate with local residents.

[0449] Input: User interaction request

[0450] Output: Interactive program response

[0451] Specific operation: A user initiates a dialogue with local residents using the chatbot function within the app, and the server generates a response through a conversational program.

[0452] Step 8:

[0453] Users use the language they've been taught to solve puzzles and find treasure.

[0454] Input: User's linguistic knowledge and puzzle-solving challenges

[0455] Output: Treasure found

[0456] What it does: The user solves puzzles provided within the app and finds treasure using the language they've been taught.

[0457] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0458] "Example 1"

[0459] One embodiment of the present invention is a system that incorporates an emotion engine that recognizes a user's emotions. In this system, a user accesses the system through an application or website and becomes an adventurer. The system uses AI to teach the user dialect phrases and words. For example, the system teaches phrases and words such as "namara" (a kind of "namara") and "menkoi" (a kind of "lovely"), which are used in the Hokkaido dialect. The emotion engine then recognizes the user's emotions and adjusts the dialect phrases and words it teaches based on those emotions. For example, if the user expresses joy, the AI ​​teaches fun phrases and words to match that emotion. The user then uses the dialect they learned to solve puzzles and find treasure. The emotion engine then adjusts the difficulty of the puzzles based on the user's emotions. For example, if the user expresses frustration, the emotion engine lowers the difficulty of the puzzles to match that emotion. This allows the user to enjoy an optimal learning experience tailored to their emotions.

[0460] "Example 2"

[0461] One embodiment of the present invention is a system that incorporates an emotion engine that recognizes a user's emotions. In this system, a user accesses the system through an application or website and becomes an adventurer. The system uses AI to teach the user dialect phrases and words. For example, the system teaches phrases and words such as "namara" (a kind of "namara") and "menkoi" (a kind of "lovely"), which are used in the Hokkaido dialect. The emotion engine then recognizes the user's emotions and adjusts the dialect phrases and words it teaches based on those emotions. For example, if the user expresses joy, the AI ​​teaches fun phrases and words to match that emotion. The user then uses the dialect they learned to solve puzzles and find treasure. The emotion engine then adjusts the difficulty of the puzzles based on the user's emotions. For example, if the user expresses frustration, the emotion engine lowers the difficulty of the puzzles to match that emotion. This allows the user to enjoy an optimal learning experience tailored to their emotions.

[0462] "Example 3"

[0463] One embodiment of the present invention is a system that incorporates an emotion engine that recognizes a user's emotions. In this system, a user accesses the system through an application or website and becomes an adventurer. The system uses AI to teach the user dialect phrases and words. For example, the system teaches phrases and words such as "namara" (a kind of "namara") and "menkoi" (a kind of "lovely"), which are used in the Hokkaido dialect. The emotion engine then recognizes the user's emotions and adjusts the dialect phrases and words it teaches based on those emotions. For example, if the user expresses joy, the AI ​​teaches fun phrases and words to match that emotion. The user then uses the dialect they learned to solve puzzles and find treasure. The emotion engine then adjusts the difficulty of the puzzles based on the user's emotions. For example, if the user expresses frustration, the emotion engine lowers the difficulty of the puzzles to match that emotion. This allows the user to enjoy an optimal learning experience tailored to their emotions.

[0464] The processing flow of each embodiment will be described below.

[0465] "Example 1"

[0466] Step 1: A user accesses the system through an application or website.

[0467] Step 2: The system uses AI to teach the user dialect phrases and words, such as "namara" (meaning "namara") and "menkoi" (meaning "lovely"), which are used in the Hokkaido dialect.

[0468] Step 3: The emotion engine recognizes the user's emotions and adjusts the dialect phrases and words that the AI ​​teaches according to those emotions. For example, if the user expresses joy, the AI ​​will teach them fun phrases and words to match that emotion.

[0469] Step 4: The user solves the puzzle and finds the treasure using the dialect they were taught.

[0470] Step 5: The emotion engine adjusts the difficulty of the puzzle according to the user's emotions. For example, if the user expresses frustration, the emotion engine will lower the difficulty of the puzzle to match that emotion.

[0471] Example 1

[0472] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0473] Conventional dialect learning systems have difficulty providing a learning experience that responds to the user's emotions, which can lead to a decline in motivation to learn. Furthermore, systems that combine dialect learning and puzzle solving have the problem of not adjusting the difficulty level according to the user's emotions, which can lead to users becoming discouraged.

[0474] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0475] In this invention, the server includes means for a user to become an adventurer and participate in a treasure hunt while learning dialects from around Japan, means for an artificial intelligence to teach dialect phrases and words, means for the user to solve puzzles and find treasure using the dialect that has been taught to them, means for recognizing the user's emotions, means for adjusting the dialect phrases and words in accordance with the user's emotions, and means for adjusting the difficulty of the puzzles in accordance with the user's emotions. This provides an optimal learning experience in accordance with the user's emotions, making it possible to learn dialects while maintaining motivation to learn.

[0476] "User" refers to an individual who uses the system to participate in an adventure while learning a dialect.

[0477] "Adventurer" refers to the role of a user who takes part in a treasure hunt adventure while learning dialects within the system.

[0478] "Dialects from around Japan" refers to unique words and phrases used in specific regions of Japan.

[0479] A "treasure hunt adventure" refers to a series of activities in which users learn dialects, solve puzzles, and ultimately find treasure.

[0480] "Artificial intelligence" refers to the technology that enables computer systems to mimic human intelligence to learn and solve problems.

[0481] "Dialect phrases and words" refer to words and expressions unique to a particular region.

[0482] "Solving the puzzle" refers to the user solving a problem or challenge provided by the system.

[0483] "Emotion recognition" refers to the system analyzing the user's emotions from their facial expressions and voice.

[0484] "Adjusting according to emotions" refers to the system changing the content and difficulty level provided based on the user's emotions.

[0485] "Adjusting the difficulty level" refers to changing the difficulty level of the puzzles and challenges provided by the system according to the user's emotions and progress.

[0486] This invention is a system in which a user becomes an adventurer and participates in a treasure hunt while learning dialects from around Japan. The system operates when the user accesses it through an application or website. A specific embodiment of this system will be described below.

[0487] Hardware and software used

[0488] The system uses the following hardware and software:

[0489] Server: Manages user information, generates scenarios, and runs the AI ​​engine and emotion engine.

[0490] Device: A smartphone or PC used by a user. It is equipped with a camera and microphone and captures the user's facial expressions and voice.

[0491] Generative AI models: For example, using OpenAI's GPT-4 to generate dialect phrases and words.

[0492] Emotion engine: Recognizes user emotions using, for example, Microsoft's Azure Emotion API.

[0493] System Operation

[0494] When a user accesses an application or website, the server receives the user's information and generates an adventure scenario. The scenario includes regional dialect phrases and words, such as "namara" (meaning "namara") and "menkoi" (meaning "lovely"), which are used in the Hokkaido dialect.

[0495] The server uses a generative AI model to teach the user dialect phrases and words, and as the user progresses through the dialect, the system offers puzzle-solving tasks as the next step.

[0496] While a user is using the system through an application or website, the device's camera and microphone are used to capture the user's facial expressions and voice. The server uses an emotion engine to analyze the user's emotions from the captured data.

[0497] When the emotion engine recognizes the user's emotion, the server sends a prompt to the generative AI model based on that emotion, generating dialect phrases and words that correspond to the user's emotion. For example, if the user expresses joy, it generates a phrase such as "It's so fun!"

[0498] Users learn dialect phrases and words provided by the server and use them to solve puzzles. For example, a puzzle using the Japanese word "namara" is presented.

[0499] The server records the user's learning progress and the results of solving puzzles, and provides new dialect phrases and solutions as the next step. If the user expresses frustration, the server adjusts the difficulty of the puzzles based on the results of the emotion engine.

[0500] Specific examples

[0501] When a user joins an adventure in the Hokkaido region, the server teaches dialect words such as "namara" and "menkoi." If the user uses "namara" correctly, the next step is to solve a puzzle using "namara."

[0502] If the emotion engine recognizes the user's emotion of joy, the server will teach them phrases such as "It's so much fun!" Conversely, if the user expresses emotion of frustration, the server will provide them with "It's such an easy riddle."

[0503] Prompt Sentence Examples

[0504] Examples of prompts to input to a generative AI model include:

[0505] "A user is participating in an adventure in the Hokkaido region. Teach the user the dialect for 'namara' (meaning "namara" in Japanese). If the user is expressing joy, teach them a fun phrase."

[0506] In this way, users can have an optimal learning experience according to their own emotions.

[0507] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0508] Step 1:

[0509] A user accesses an application or website.

[0510] Input: User login information (username, password)

[0511] Output: User authentication result, user ID

[0512] How it works: A user uses a smartphone or PC to access a dedicated application or website and enters their username and password on the login screen. The server receives this and authenticates it by checking it against a database. If authentication is successful, a user ID is generated and the system proceeds to the next step.

[0513] Step 2:

[0514] The server receives the user information and generates an adventure scenario.

[0515] Input: User ID, past progress information, user's selected region

[0516] Output: Adventure scenario

[0517] Specific operation: The server retrieves past progress information from the database based on the user ID. Based on the region selected by the user (e.g., Hokkaido), it generates an appropriate adventure scenario. The scenario includes dialect phrases and words from that region.

[0518] Step 3:

[0519] The server uses generative AI models to generate dialect phrases and words.

[0520] Input: Adventure scenario, prompt

[0521] Output: Dialect phrases and words

[0522] How it works: The server sends a prompt to a generative AI model (e.g., OpenAI's GPT-4). The prompt might include something like, "Please tell me the dialect of the Hokkaido region." The generative AI model then generates dialect phrases and words based on this and returns them to the server.

[0523] Step 4:

[0524] The server uses an emotion engine to recognize the user's emotion.

[0525] Input: User's facial expression data, voice data

[0526] Output: User sentiment analysis results

[0527] Specific operation: While a user is using the system through an application or website, the device's camera and microphone are used to capture the user's facial expressions and voice. The server uses an emotion engine (e.g., Microsoft's Azure Emotion API) to analyze the user's emotions from the captured data.

[0528] Step 5:

[0529] The server adjusts dialect phrases and words according to the user's emotions.

[0530] Input: Dialect phrases and words, user sentiment analysis results

[0531] Output: Adjusted dialect phrases and words

[0532] Specific operation: Based on the analysis results of the emotion engine, the server sends prompt sentences to the generative AI model, which generates dialect phrases and words that correspond to the user's emotions. For example, if the user expresses joy, it generates a phrase such as "It's so fun!"

[0533] Step 6:

[0534] Users learn dialects and try to solve puzzles.

[0535] Input: Adjusted dialect phrases and words, puzzle questions

[0536] Output: Answers to puzzles, learning progress

[0537] Specific operation: The user learns dialect phrases and words provided by the server and attempts to solve puzzles using them. For example, a puzzle using the Japanese word "namara" is presented. When the user enters the answer, the server receives the answer and determines whether it is correct.

[0538] Step 7:

[0539] The server tracks the user's progress and provides next steps.

[0540] Input: Answers to puzzles, learning progress

[0541] Output: Next step adventure scenario, dialect phrases and words

[0542] Specific operation: The server records the user's learning progress and the results of solving puzzles, and generates new dialect phrases and solutions as the next step. If the user expresses frustration, the server lowers the difficulty of the next puzzle based on the results of the emotion engine.

[0543] (Application example 1)

[0544] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0545] Conventional dialect learning systems have had difficulty providing an appropriate learning experience that reflects the user's emotions and current location. Furthermore, systems that combine dialect learning with a treasure hunt adventure did not adjust the difficulty level based on the user's emotions or provide dialect phrases based on location information, making it difficult to maintain the user's motivation to learn. This meant that users were not provided with a satisfactory experience of learning dialects while experiencing local culture.

[0546] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0547] In this invention, the server includes: a means for a user to become an adventurer and participate in a treasure hunt while learning dialects from around Japan; a means for an AI to teach dialect phrases and words; a means for the user to solve puzzles and find treasure using the dialect that the AI ​​has taught them; a means for an emotion engine that recognizes the user's emotions to adjust the dialect phrases and words in accordance with the emotions; a means for recognizing the user's current location and providing dialect phrases based on the location information; and a means for the emotion engine to adjust the difficulty of the puzzles in accordance with the user's emotions. This provides an optimal learning experience according to the user's emotions and current location, making it possible to learn dialects while experiencing local culture.

[0548] An "adventurer" is a person who takes on the role of participating in a treasure hunt adventure while learning dialects within the system.

[0549] A "dialect" is a word or phrase that varies from region to region in Japan.

[0550] "AI" stands for artificial intelligence, a technology that teaches users dialect phrases and words.

[0551] An "emotion engine" is a technology that recognizes the user's emotions and adjusts the system's behavior according to those emotions.

[0552] "Location information" is data indicating the user's current location, and is obtained using technology such as GPS.

[0553] "Difficulty adjustment" means changing the difficulty of the puzzle according to the user's feelings.

[0554] "Treasure hunt" is an activity in which users find treasure using hints provided within the system.

[0555] A "learning experience" is an experience in which users learn dialects and come into contact with local culture through the system.

[0556] A system for implementing this invention allows users to become adventurers and participate in a treasure hunt while learning dialects from around Japan. The system combines an emotion engine that recognizes the user's emotions with a location information system that recognizes the user's current location to provide an optimal learning experience.

[0557] Hardware and software used

[0558] Hardware: Smartphone (camera, GPS)

[0559] Software: OpenCV (image processing), Keras (emotion recognition model), geopy (distance calculation)

[0560] System configuration

[0561] 1. Emotion recognition

[0562] The smartphone camera captures the user's facial expression and detects the face using OpenCV. The detected face image is input into the Keras emotion recognition model to recognize the user's emotion.

[0563] 2. Providing dialect phrases

[0564] Based on the recognized emotion, the AI ​​will select an appropriate dialect phrase. For example, if the user expresses joy, the Hokkaido dialect phrase "It's so fun!" will be provided.

[0565] 3. Obtaining location information

[0566] It uses the smartphone's GPS to obtain the user's current location, so that when the user approaches a specific location, dialect phrases related to that location are provided.

[0567] 4. Treasure Hunt

[0568] The system uses geopy to calculate the user's current location and the location of the treasure, and provides hints based on the distance. For example, when the user approaches the treasure, a hint such as "Distance to treasure: 10 meters" is displayed.

[0569] Specific examples

[0570] If the user is expressing joy: "This is so fun!"

[0571] If the user is expressing sadness: "Sorry, cheer up!"

[0572] Prompt Sentence Examples

[0573] Take a user's image as input, recognize their emotion, provide Hokkaido dialect phrases, calculate the user's current location and the location of the treasure, and provide hints for the treasure hunt.

[0574] In this way, users can have a learning experience that is optimal for their emotions and current location, allowing them to learn dialects while experiencing local culture.

[0575] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0576] Step 1:

[0577] The user takes a photo of their own facial expression using the smartphone camera.

[0578] Input: User's face image

[0579] Output: Photographed face image data

[0580] Specific operation: The user activates the smartphone camera and takes a picture of their face. The captured image is saved in the smartphone.

[0581] Step 2:

[0582] The device uses OpenCV to detect faces from captured facial images.

[0583] Input: Photographed face image data

[0584] Output: Image data of the detected face area

[0585] What it does: The device uses the OpenCV library to convert the face image to grayscale, applies a face detection algorithm, crops the detected face area, and passes it on to the next process.

[0586] Step 3:

[0587] The device uses the Keras emotion recognition model to recognize the user's emotions from the detected face area.

[0588] Input: Image data of the detected face area

[0589] Output: Recognized emotion (e.g., happy, sad)

[0590] Specific operation: The device resizes the image of the face region to 48x48 pixels and inputs it into the Keras emotion recognition model. The model predicts the emotion and outputs the emotion with the highest probability.

[0591] Step 4:

[0592] The server selects the appropriate dialect phrase based on the recognized emotion.

[0593] Input: Recognized emotion

[0594] Output: Selected dialect phrases

[0595] What it does: The server consults a database of dialect phrases corresponding to emotions and selects the phrase that best matches the recognized emotion. For example, for the emotion of joy, it selects "That's so fun!"

[0596] Step 5:

[0597] The device obtains the user's current location using the smartphone's GPS function.

[0598] Input: None (GPS function activated)

[0599] Output: User's current location (latitude and longitude)

[0600] Specific operation: The device activates the GPS function and obtains the latitude and longitude information of the current location. This information is used for the following process.

[0601] Step 6:

[0602] The server provides dialect phrases related to the location based on the user's current location.

[0603] Input: User's current location (latitude and longitude)

[0604] Output: Dialect phrases related to location

[0605] What it does: The server analyzes the user's current location and retrieves dialect phrases related to that location from a database. For example, if you're in Hokkaido, it will provide "namara."

[0606] Step 7:

[0607] The server calculates the user's current location and the location of the treasure, and provides hints based on the distance.

[0608] Input: User's current location (latitude and longitude), treasure location (latitude and longitude)

[0609] Output: distance-based hints (e.g. distance to treasure)

[0610] Specific operation: The server uses the geopy library to calculate the distance between the user's current location and the treasure location, and generates a hint based on that distance. For example, it provides a hint such as "Distance to treasure: 10 meters."

[0611] Step 8:

[0612] The terminal displays the dialect phrases and hints received from the server to the user.

[0613] Input: Dialect phrase, distance-based hints

[0614] Output: Dialect phrases and hints displayed to the user

[0615] Specific operation: The terminal displays the dialect phrases and hints received from the server on the screen and provides them to the user, who can use them to proceed with the treasure hunt.

[0616] Example 2

[0617] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0618] Conventional dialect learning systems have difficulty providing a learning experience that responds to the user's emotions, making it difficult to maintain motivation. Furthermore, they have not adequately provided users with the opportunity to experience local culture through dialect learning. This has led to a demand for a system that allows users to effectively learn dialects and continue their treasure-hunting adventure while having fun.

[0619] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0620] In this invention, the server includes means for a user to become an adventurer and participate in a treasure hunt while learning the local dialect, means for an artificial intelligence to teach dialect phrases and words, means for the user to solve puzzles and find treasure using the dialect that has been taught, means for recognizing the user's emotions using an emotion recognition engine, means for adjusting the dialect phrases and words taught by the artificial intelligence in accordance with the user's emotions, and means for adjusting the difficulty of the puzzles in accordance with the user's emotions. This makes it possible to provide an optimal learning experience in accordance with the user's emotions and to provide an experience of experiencing local culture through dialect learning.

[0621] "User" refers to an individual who accesses the system and participates in a treasure hunt adventure while learning the dialect.

[0622] "Adventurer" refers to the role of a user who embarks on a treasure hunt while learning dialects within the system.

[0623] A "regional dialect" refers to words and phrases unique to a particular region.

[0624] The "treasure hunt adventure" refers to an activity in which users solve puzzles and find treasure while learning dialects within the system.

[0625] "Artificial intelligence" refers to computer programs used to teach users dialect phrases and words.

[0626] An "emotion recognition engine" refers to technology for recognizing emotions from a user's facial expressions and voice.

[0627] "Means for recognizing emotions" refers to a process of analyzing a user's emotions using an emotion recognition engine.

[0628] "Means for teaching dialect phrases and words" refers to the process of using artificial intelligence to teach dialect phrases and words to a user.

[0629] "Puzzle solving" refers to the process by which a user solves puzzles within the system using the dialect they have been taught.

[0630] "Method of finding treasure" refers to the process by which a user solves a puzzle and finds treasure within the system.

[0631] "Emotional adjustment" refers to the process of adjusting the dialect phrases, words, or difficulty of the riddles taught by the AI ​​based on the user's emotions.

[0632] This invention is a system in which users become adventurers and participate in a treasure hunt while learning local dialects. The system recognizes the user's emotions and adjusts the learning content and difficulty of puzzles according to those emotions, providing the user with an optimal learning experience.

[0633] Hardware and software used

[0634] Hardware: Servers, user devices (PCs, smartphones, tablets)

[0635] Software: Generative AI models (e.g., GPT-4), emotion recognition engines (e.g., Affectiva)

[0636] System Overview

[0637] Users access the system through an application or website using their own devices. After logging in, the user becomes an adventurer and takes part in a treasure hunt while learning the dialect. The server uses a generative AI model to teach the user dialect phrases and words. For example, it teaches phrases and words such as "namara" (naughty) and "menkoi" (lovely), which are dialects of the Hokkaido region.

[0638] Emotion Recognition and Regulation

[0639] The server uses an emotion recognition engine to recognize emotions from the user's facial expressions and voice. For example, if the user is connected to the system via a camera or microphone, the emotion recognition engine analyzes the user's emotions in real time. If the user expresses joy, the server teaches fun phrases and words that match that emotion. Conversely, if the user expresses frustration, the server lowers the difficulty of the puzzle.

[0640] Specific examples

[0641] Consider a scenario in which a user accesses the system and learns the dialect of the Hokkaido region. The server gives a hint that "It's hidden in a very cold place." If the user has learned that "namara" means "very," he can deduce that the treasure is hidden in a cold place, i.e., Hokkaido.

[0642] The emotion recognition engine recognizes the user's emotions, and if the user expresses joy, the server will teach them the fun phrase "Menkoi." Conversely, if the user expresses frustration, the server will simplify the next hint to "Try looking for a place a little further south."

[0643] Prompt Sentence Examples

[0644] An example of a prompt sentence to be input to the generative AI model is shown below.

[0645] Assuming the user is learning the Hokkaido dialect, give them a hint like, "It's hidden in a really cold place." Also, if the user is expressing joy, give them a fun phrase, such as "Menkoi."

[0646] This system allows users to have an optimal learning experience that suits their emotions, and provides an opportunity to experience local culture through dialect learning.

[0647] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0648] Step 1:

[0649] A user accesses the system

[0650] Users access the system through an application or website using their own devices (PC, smartphone, tablet). The user enters their username and password on the login screen, and if the login is successful, the main screen of the adventure is displayed. The input is the username and password, and the output is the main screen after successful login.

[0651] Step 2:

[0652] The server recognizes the user's emotions

[0653] The server uses an emotion recognition engine to recognize emotions from the user's facial expressions and voice. If the user is connected to the system via a camera or microphone, the emotion recognition engine analyzes the user's emotions in real time. The input is the user's facial expressions and voice data, and the output is the recognized emotion data.

[0654] Step 3:

[0655] The server teaches dialect phrases and words

[0656] The server uses the generative AI model to teach the user dialect phrases and words. For example, if the server teaches the Hokkaido dialect "namara," it explains that "namara" means "very." The input is the user's emotion data and learning progress data, and the output is an explanation of the dialect phrase or word.

[0657] Step 4:

[0658] Users try to solve the puzzle

[0659] The user uses the dialect they have been taught to try to solve the puzzles posed by the server. For example, the server might give a hint like, "It's hidden in a really cold place." If the user has learned that "namara" means "very," they can guess that the treasure is hidden in a cold place, i.e., Hokkaido. The input is a dialect phrase or word description and the hint to solve the puzzle, and the output is the user's guess.

[0660] Step 5:

[0661] The server adjusts the difficulty of the puzzles according to the user's emotions.

[0662] The server uses an emotion recognition engine to analyze the user's emotions again. For example, if the user expresses frustration, the server lowers the difficulty of the puzzle. Conversely, if the user expresses joy, the server teaches fun phrases or words. The input is the user's emotion data and the current difficulty of the puzzle, and the output is an adjusted difficulty level or a new phrase.

[0663] Step 6:

[0664] The user finds the treasure

[0665] The user solves the puzzle and finds the treasure. The server confirms that the user has found the treasure and suggests the next stage or learning a new dialect. The input is the user's guess and the location data of the treasure, and the output is guidance for the next stage or the content of learning a new dialect.

[0666] (Application example 2)

[0667] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0668] Conventional dialect learning systems and puzzle-solving games do not adjust the learning content or difficulty level according to the user's emotions, which can reduce the user's motivation to learn and the quality of the experience. In addition, because they were not designed for implementation as smartphone applications, the environment for easy user access was not created. This resulted in a lack of consistency in the user's learning experience, making effective learning difficult.

[0669] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0670] In this invention, the server includes: a means for a user to become an adventurer and participate in a treasure hunt while learning dialects from around Japan; a means for an AI to teach dialect phrases and words; a means for the user to solve puzzles and find treasure using the dialect that the user has been taught; a means for recognizing the user's emotions using an emotion engine and adjusting the learning content and the difficulty of the puzzles according to the emotions; and a means implemented as a smartphone application. This provides an optimal learning experience according to the user's emotions, enabling effective learning in an easily accessible environment.

[0671] A "user" is an individual who uses the system to learn dialects and attempt to solve puzzles.

[0672] An "adventurer" is a user who takes on the role of participating in a treasure hunt adventure while learning dialects within the system.

[0673] "Dialects from around Japan" refers to unique words and phrases used in specific regions of Japan.

[0674] A "treasure hunt adventure" refers to a series of activities in which users learn dialects, solve puzzles, and ultimately find treasure.

[0675] "AI" stands for artificial intelligence and refers to technology that teaches users dialect phrases and words.

[0676] "Dialect phrases and words" refer to unique linguistic expressions used in a particular region.

[0677] "Solving the puzzle" refers to the user solving a presented problem or challenge using the dialect they have been taught.

[0678] "Finding treasure" refers to the reward or achievement that the user ultimately receives by solving the mystery.

[0679] An "emotion engine" refers to technology that recognizes a user's emotions and adjusts the system's behavior accordingly.

[0680] "Learning content" refers to the dialect phrases and words that users learn through the system, as well as related information.

[0681] "Puzzle difficulty" refers to the level of difficulty of the puzzle that the user must solve.

[0682] "Smartphone application" refers to a software program that runs on a smartphone.

[0683] The system for implementing this invention allows users to become adventurers and participate in a treasure hunt while learning dialects from around Japan. The system includes the following main components:

[0684] 1. User Device

[0685] The user device is implemented as a smartphone application. Users access the system through this application and try to solve puzzles while learning dialects. The user device uses a camera and microphone to collect data to recognize the user's emotions.

[0686] 2. Server

[0687] The server runs a program that has the following functions:

[0688] Emotion engine: Recognizes the user's emotions and adjusts the learning content and difficulty of the puzzles accordingly. The emotion engine uses emotion recognition libraries such as OpenCV and DeepFace.

[0689] Dialect Teacher: Teaches users dialect phrases and words. The dialect teacher uses a pre-collected dialect database.

[0690] Riddle Generator: Generates riddles with adjusted difficulty according to the user's emotions. The riddle generator uses a generative AI model (e.g., GPT-3) to generate riddles.

[0691] 3. Data processing and calculation

[0692] The server receives emotion data sent from the user's device and analyzes it using an emotion engine. Based on the analysis results, a dialect teacher selects appropriate dialect phrases and words and provides them to the user. Furthermore, a puzzle generator generates puzzles of a difficulty level corresponding to the user's emotion and presents them to the user.

[0693] For example, if a user smiles, the emotion engine recognizes this as "joy." The dialect teacher teaches the Hokkaido dialect "menkoi" (cute) to match the emotion of "joy." The puzzle generator then generates a relatively easy puzzle to match the emotion of "joy."

[0694] An example of a prompt is as follows:

[0695] When the user smiles, the emotion engine recognizes it as "joy" and the dialect teacher teaches the Hokkaido dialect "menkoi (cute)." The puzzle generator then generates a relatively easy puzzle to match the emotion of "joy."

[0696] In this way, users can have an optimal learning experience according to their emotions.

[0697] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0698] Step 1:

[0699] The user terminal launches the application and collects the user's facial image and voice data.

[0700] Input: User's facial image and voice data

[0701] Output: Collected facial images and audio data

[0702] Specific operation: Uses the camera and microphone on the user's device to capture the user's facial expressions and voice in real time.

[0703] Step 2:

[0704] The user terminal transmits the collected facial images and voice data to the server.

[0705] Input: Collected facial images and audio data

[0706] Output: Facial images and audio data sent to the server

[0707] Specific operation: The user terminal uploads the collected data to the server via the Internet.

[0708] Step 3:

[0709] The server uses an emotion engine to recognize the user's emotions from the received facial images and voice data.

[0710] Input: face images and audio data

[0711] Output: Recognized user emotion (e.g., happy, sad, surprised, etc.)

[0712] Specific operation: The server uses emotion recognition libraries such as OpenCV and DeepFace to analyze facial expressions and tone of voice to identify the user's emotions.

[0713] Step 4:

[0714] Based on the emotions recognized by the server, a dialect teacher selects appropriate dialect phrases and words.

[0715] Input: Perceived user emotion

[0716] Output: Selected dialect phrases and words

[0717] Specific operation: The server searches and selects dialect phrases and words from a dialect database that match the user's emotions.

[0718] Step 5:

[0719] The server sends the selected dialect phrases and words to the user's terminal.

[0720] Input: Selected dialect phrases and words

[0721] Output: Dialect phrases and words sent to the user's device

[0722] Specific operation: The server sends selected dialect phrases and words to the user's device via the Internet.

[0723] Step 6:

[0724] The user terminal displays the received dialect phrases and words to the user.

[0725] Input: Received dialect phrase or word

[0726] Output: Dialect phrases or words displayed to the user

[0727] Specific operation: The received dialect phrases and words are displayed on the screen of the user's device, allowing the user to learn them.

[0728] Step 7:

[0729] Based on the emotion recognized by the server, a puzzle generator generates a puzzle of appropriate difficulty.

[0730] Input: Perceived user emotion

[0731] Output: Generated puzzle

[0732] Specific operation: The server uses a generative AI model (e.g., GPT-3) to generate puzzles with difficulty levels that correspond to the user's emotions.

[0733] Step 8:

[0734] The server sends the generated puzzle to the user's terminal.

[0735] Input: Generated puzzle

[0736] Output: The mystery sent to the user's terminal

[0737] Specific operation: The server sends the generated puzzle to the user's terminal via the Internet.

[0738] Step 9:

[0739] The user terminal displays the received riddle to the user, who then solves the riddle.

[0740] Input: riddle received

[0741] Output: The answer entered by the user

[0742] Specific operation: The received riddle is displayed on the screen of the user's terminal, and an interface is provided for the user to input the answer.

[0743] Step 10:

[0744] The server receives the user's answer and determines whether it is correct.

[0745] Input: The answer entered by the user

[0746] Output: Judgment result (correct / incorrect)

[0747] Specific operation: The server analyzes the user's answer, determines whether it is correct, and sends the result to the user's terminal.

[0748] Step 11:

[0749] The user terminal displays the determination result to the user.

[0750] Input: Judgment result

[0751] Output: The result displayed to the user

[0752] Specific operation: The judgment result is displayed on the screen of the user's device and notified to the user.

[0753] Example 3

[0754] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0755] Conventional dialect learning systems do not adjust the learning content according to the user's emotions, which can reduce the learning effect. It is also difficult to provide a learning experience based on the local culture, limiting the opportunities for users to deeply experience the local culture. Furthermore, users can become discouraged during the learning process, making it difficult to continue learning.

[0756] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0757] In this invention, the server includes a means for a user to become an adventurer and participate in a treasure hunt while learning the local dialect, a means for an artificial intelligence to teach dialect phrases and words, a means for the user to solve puzzles and find treasure using the dialect that has been taught to them, a means for adjusting the dialect phrases and words in accordance with the user's emotions using an emotion engine that recognizes the user's emotions, and a means for adjusting the difficulty of the puzzles in accordance with the user's emotions. This provides an optimal learning experience in accordance with the user's emotions, enabling the user to deeply experience the local culture. It also allows the user to continue learning without giving up.

[0758] "User" refers to an individual who uses the system to learn dialects and participate in a treasure hunt adventure.

[0759] "Adventurer" refers to the role of a user who embarks on a treasure hunt while learning dialects within the system.

[0760] A "regional dialect" refers to words and phrases unique to a particular region.

[0761] The "treasure hunt adventure" refers to an activity in which users solve puzzles within the system and find treasure while learning the dialect.

[0762] "Artificial intelligence" refers to the machine learning models and algorithms used to teach users dialect phrases and words.

[0763] "Dialect phrases and words" refer to words and expressions unique to a particular region.

[0764] "Solving the puzzle" refers to the user using the dialect they have been taught to solve problems and challenges within the system.

[0765] An "emotion engine" refers to software or algorithms that recognize a user's emotions and adjust the system's behavior accordingly.

[0766] "Adjusting dialect phrases and words according to emotions" refers to changing the content of the dialect to be taught based on the user's emotional state.

[0767] "Adjusting the difficulty of puzzles according to emotions" refers to changing the difficulty of puzzles in the system based on the user's emotional state.

[0768] The present invention is a system in which users become adventurers and participate in a treasure hunt while learning local dialects. The system has the function of recognizing the user's emotions and adjusting the learning content and difficulty of puzzles according to those emotions.

[0769] Hardware and software used

[0770] Hardware: Servers, user devices (PCs, smartphones, tablets)

[0771] Software: Emotion engine, generative AI model, web application, database

[0772] Specific operation of the system

[0773] User Access

[0774] A user accesses an application or website using a device such as a PC or smartphone. The device receives the user's input and sends a request to the server. The server receives the request and starts a user session.

[0775] Emotion recognition

[0776] The server uses an emotion engine to analyze the user's facial expressions and voice data. For example, the emotion engine can use Microsoft Azure's Emotion API. The device uses the user's camera and microphone to collect facial expressions and voice data and send it to the server. The server receives the analysis results of the emotion engine and identifies the user's emotional state. For example, if the user is smiling, it will recognize this as "joy."

[0777] Dialect phrase generation

[0778] The server sends a prompt to a generative AI model (e.g., OpenAI's GPT-3) to generate a dialect phrase that corresponds to the user's emotion. An example of a prompt is, "If the user expresses joy, generate a fun phrase in the Hokkaido dialect." The generative AI model generates an appropriate dialect phrase based on the prompt and returns it to the server. The server receives the generated phrase and prepares it to be provided to the user.

[0779] Providing learning content

[0780] The server sends the generated dialect phrases to the user's device. The device displays the received phrases and allows the user to learn them. For example, phrases such as "It's so fun!" and "It's so cute!" are displayed. The user learns the displayed phrases and uses them within the application.

[0781] Mystery solving and treasure hunting

[0782] The user solves puzzles in the application using the dialect phrases they have learned. The server then uses the emotion engine again to check the user's emotional state. For example, if the user is confused, it will recognize this as "frustrated." The server then adjusts the difficulty of the puzzles according to the user's emotions. For example, if the user shows frustration, it will lower the difficulty of the puzzles. The user solves the puzzles of the adjusted difficulty and finds the treasure.

[0783] Specific examples

[0784] Suppose a user wants to learn the Hokkaido dialect. When the user accesses the application and the emotion engine recognizes the emotion of joy from the user's facial expression, the generative AI model will provide phrases such as "It's so fun!" or "It's so cute!". The user can then use these phrases to solve puzzles within the app and find treasure.

[0785] In this way, the user can have an optimal learning experience that suits their emotions while experiencing the local culture. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0786] Step 1:

[0787] User Access

[0788] A user accesses an application or website using a device such as a PC or smartphone. The device receives the user's input and sends a request to the server. The server receives the request and starts a user session.

[0789] Input: User access request

[0790] Output: User session started

[0791] Specific behavior: The device displays a dialog asking for permission, such as "Do you want to use the camera to recognize your facial expressions?"

[0792] Step 2:

[0793] Emotion recognition

[0794] The server uses the emotion engine to analyze the user's facial expression and voice data. The device uses the user's camera and microphone to collect facial expression and voice data and transmits it to the server. The server receives the analysis results of the emotion engine and identifies the user's emotional state.

[0795] Input: User facial and voice data

[0796] Output: User's emotional state (e.g., joy, frustration)

[0797] What happens: The device displays a message such as "Analyzing emotions..."

[0798] Step 3:

[0799] Dialect phrase generation

[0800] The server sends a prompt to the generative AI model, which generates a dialect phrase based on the user's emotion. The generative AI model generates an appropriate dialect phrase based on the prompt and returns it to the server.

[0801] Input: User's emotional state, prompt

[0802] Output: Generated dialect phrases

[0803] Specific operation: The server sends a prompt to the generative AI model, such as, "When the user expresses the emotion of joy, generate a fun phrase in the Hokkaido dialect."

[0804] Step 4:

[0805] Providing learning content

[0806] The server sends the generated dialect phrases to the user's device, which displays the received phrases and allows the user to learn them.

[0807] Input: Generated dialect phrase

[0808] Output: Dialect phrase displayed to the user

[0809] What it does: The device displays phrases like "So much fun!" or "So cute!"

[0810] Step 5:

[0811] Mystery solving and treasure hunting

[0812] The user solves puzzles in the application using the dialect phrases they have learned. The server then uses the emotion engine again to check the user's emotional state. The server adjusts the difficulty of the puzzles according to the user's emotions.

[0813] Input: User's emotional state, learned dialect phrases

[0814] Output: Puzzle with adjusted difficulty

[0815] What it does: The device displays feedback such as "Current difficulty: Medium, Emotion: Frustrated, Lowering the difficulty."

[0816] (Application example 3)

[0817] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0818] Conventional dialect learning systems do not adjust the learning content or difficulty level according to the user's emotions, and the learning experience is uniform, which can lower user motivation. It is also difficult for users to experience local culture through learning dialects based on that culture. Furthermore, there is a lack of means to provide an interactive learning experience using smartphones.

[0819] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0820] In this invention, the server includes: a means for a user to become an adventurer and participate in a treasure hunt while learning dialects from around Japan; a means for an AI to teach dialect phrases and words; a means for the user to solve puzzles and find treasure using the dialect that the AI ​​has taught them; a means for recognizing the user's emotions using an emotion engine and adjusting the learning content and difficulty level according to the emotions; a means including an application to be installed on a smartphone; a means for providing dialect phrases using a generative AI model; and a means for generating prompt sentences. This allows the user to have an optimal learning experience according to their emotions and experience experiencing local culture.

[0821] "User" refers to an individual who uses the system to enjoy the adventure of learning dialects.

[0822] An "adventurer" is a user whose role is to participate in a treasure hunt adventure while learning dialects within the system.

[0823] A "dialect" refers to unique words and phrases used in a particular region.

[0824] "Treasure Hunt Adventure" refers to a series of activities in which users solve puzzles and find treasure while learning the dialect.

[0825] "AI" refers to artificial intelligence, and has the ability to teach users dialect phrases and words.

[0826] An "emotion engine" refers to a system that has the ability to recognize the user's emotions and adjust the learning content and difficulty level according to those emotions.

[0827] A "smartphone" refers to a portable information terminal that can connect to the Internet and install applications.

[0828] The "application" refers to software that is installed on smartphones and allows users to enjoy an adventure while learning dialects.

[0829] "Generative AI model" refers to an artificial intelligence model for generating dialect phrases to be provided to users.

[0830] A "prompt sentence" refers to text data that is input into a generative AI model and serves to prompt the generation of dialect phrases.

[0831] The system for implementing this invention allows users to become adventurers and participate in a treasure hunt while learning dialects from around Japan. The system includes the following main components:

[0832] 1. System Program

[0833] The system program is implemented using the following hardware and software.

[0834] Hardware: Smartphone

[0835] Software: EmotionEngine (emotion recognition engine), DialectDatabase (dialect database), generative AI model

[0836] 2. Program Processing

[0837] The user's adventure begins

[0838] The user launches the application on their smartphone and selects the region they want to start their adventure in. The server retrieves the dialect data for the selected region from the DialectDatabase and provides it to the user.

[0839] Dialect learning

[0840] The server uses the generative AI model to generate dialect phrases and provides them to the user, who then learns the dialect phrases on their smartphone screen.

[0841] Emotional awareness and regulation

[0842] The Emotion Engine recognizes the user's emotions and adjusts the learning content and difficulty level accordingly. For example, if the user expresses joy, the server will provide fun dialect phrases. Conversely, if the user expresses frustration, the server will provide easy dialect phrases.

[0843] Mystery solving and treasure hunting

[0844] The user solves puzzles using the dialect they have learned and finds treasure. The server adjusts the difficulty of the puzzles according to the user's emotions. For example, if the user expresses frustration, the server provides an easy puzzle.

[0845] 3. Specific Examples

[0846] For example, if a user selects an adventure in the Hokkaido region, the server retrieves Hokkaido dialect words such as "namara" (naughty) and "menkoi" (cute) from the DialectDatabase and provides them to the user using a generative AI model. If the user expresses joy, the server displays "I'll teach you some fun phrases!" and provides fun dialect phrases. Conversely, if the user expresses frustration, the server displays "I'll teach you some simple phrases," and provides simple dialect phrases.

[0847] Prompt Sentence Examples

[0848] "If a user who wants to learn the Hokkaido dialect expresses joy, please tell us some fun dialect phrases."

[0849] In this way, users can experience the local culture and get the most emotionally appropriate learning experience.

[0850] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0851] Step 1:

[0852] The user starts the application using a smartphone and selects the region where the adventure begins.

[0853] Input: User's region selection

[0854] Output: Selected region information

[0855] Specific behavior: The user selects Hokkaido on the application's region selection screen.

[0856] Step 2:

[0857] The server retrieves dialect data for the selected region from the DialectDatabase and inputs it into the generative AI model.

[0858] Input: Selected region information

[0859] Output: Regional dialect data

[0860] Specific operation: The server retrieves Hokkaido dialect data (e.g., "namara" and "menkoi") from the DialectDatabase.

[0861] Step 3:

[0862] The generative AI model generates dialect phrases, which the server provides to the user.

[0863] Input: Regional dialect data

[0864] Output: Dialect phrase

[0865] Specific operation: The generative AI model generates the phrase "It's so fun!", and the server sends it to the user's smartphone.

[0866] Step 4:

[0867] The EmotionEngine recognizes the user's emotions and sends the emotional data to the server.

[0868] Input: User facial and voice data

[0869] Output: Emotion data

[0870] Specific operation: The user expresses their emotions through the smartphone's camera or microphone, and the EmotionEngine recognizes this as "joy."

[0871] Step 5:

[0872] The server adjusts the learning content and difficulty level based on the emotional data and re-inputs it into the generative AI model.

[0873] Input: Emotion data

[0874] Output: Adjusted learning content and difficulty

[0875] Specific operation: The server receives the emotion data of "joy" and inputs the prompt sentence "generate a happy phrase" into the generative AI model.

[0876] Step 6:

[0877] The generative AI model generates tailored dialect phrases, which the server provides to the user.

[0878] Input: Adjusted learning content and difficulty

[0879] Output: adjusted dialect phrase

[0880] Specific operation: The generative AI model generates the phrase "It's so fun!", and the server sends it to the user's smartphone.

[0881] Step 7:

[0882] Users solve puzzles and find treasure using the dialect they have learned, and the server adjusts the difficulty of the puzzles according to the user's emotions.

[0883] Input: User emotion data, learned dialect phrases

[0884] Output: Adjusted puzzle difficulty, treasure location information

[0885] Specific operation: The user solves the puzzle using the phrase "It's so much fun!", and the server provides a simple puzzle and sends the location information of the treasure to the user.

[0886] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0887] The data generation model 58 is a so-called generative AI (Artificial Intelligence).

[0888] An example of the data generation model 58 is ChatGPT (registered trademark)<URL: https: / / openai.com / blog / chatgpt> ) and other generative AIs.

[0889] The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0890] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0891] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0892] [Second embodiment]

[0893] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0894] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0895] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0896] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0897] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0898] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0899] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0900] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0901] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0902] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0903] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0904] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0905] "Example 1"

[0906] One aspect of the present invention is a system in which users become adventurers and participate in a treasure hunt while learning dialects from around Japan. Specifically, users access the system through an application or website and become adventurers themselves. The system uses AI to teach users dialect phrases and words. For example, it can teach phrases and words such as "namara" (naughty) and "menkoi" (lovely), which are dialects of the Hokkaido region.

[0907] "Example 2"

[0908] For example, if the AI ​​gives a hint to solve a mystery like "It's hidden in a really cold place," and the user knows that "namara" means "very" in the Hokkaido dialect, they can guess that the treasure is hidden in a cold place, in other words, Hokkaido.

[0909] "Example 3"

[0910] The system also includes content based on local culture. For example, the dialect phrases and words taught by the AI ​​are selected based on the customs, habits, and history of the region. This allows users to experience local culture through learning the dialect.

[0911] The processing flow of each embodiment will be described below.

[0912] "Example 1"

[0913] Step 1: The user accesses the system through an application or website and becomes an adventurer.

[0914] Step 2: The system uses AI to teach the user dialect phrases and words, such as "namara" (meaning "namara") and "menkoi" (meaning "lovely"), which are used in the Hokkaido dialect.

[0915] Step 3: The user solves the mystery using the dialect they were taught and finds the treasure. For example, if the AI ​​gives the hint to solve the mystery as "It's hidden in a really cold place," the user can guess that the treasure is hidden in a cold place, i.e., Hokkaido, if they have learned that "namara" means "very" in the Hokkaido dialect.

[0916] "Example 2"

[0917] Step 1: The system includes content based on local culture. For example, the dialect phrases and words that the AI ​​teaches are selected based on the customs, habits, and history of the region.

[0918] Step 2: Users can gain experience in contact with local culture through learning dialects. For example, the AI ​​teaches that "namara" is a dialect of the Hokkaido region, and users can learn about the climate and culture of Hokkaido from its usage and background.

[0919] Example 1

[0920] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0921] Conventional language learning systems have had the problem of making it difficult for users to enjoy learning local languages ​​and cultures. Furthermore, they lacked the functionality to manage learning progress, making it impossible to maximize the effectiveness of users' learning. Furthermore, there were no systems that allowed users to actually use the language they had learned to solve puzzles.

[0922] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0923] In this invention, the server includes means for receiving user information and managing the progress of the adventure, means for retrieving language data of the selected region from a database, and means for verifying the user's input and proceeding to the next stage if correct. This allows the user to effectively manage their learning progress while having fun learning the language and culture of the region, and to have the experience of solving puzzles using the language they have learned.

[0924] A "user" is an individual who accesses the system, registers as an adventurer, and participates in a treasure hunting adventure while learning the local language.

[0925] An "adventurer" is a user whose role in the system is to participate in a treasure hunting adventure while learning the local language.

[0926] "Regional languages" refers to dialects, unique phrases, and words from all over Japan.

[0927] The "treasure hunt adventure" is an activity in which users learn the local language, solve puzzles provided within the system, and ultimately find treasure.

[0928] "Artificial Intelligence" is a technology used to teach users phrases and words in their local language.

[0929] "Language data" is data that includes information about phrases and words related to a local language.

[0930] A "server" is a computer system responsible for receiving user information, managing the progress of the adventure, retrieving language data for the selected region from a database, and validating user input.

[0931] A "terminal" is a device through which a user accesses the system, displays language data, and receives user input.

[0932] A "database" is an information management system that stores regional language data and allows the server to retrieve it as needed.

[0933] "Progress" is information that indicates the stage of the user's adventure.

[0934] "Verification" is the process of checking whether the language phrases or words entered by the user are correct.

[0935] A "stage" refers to each stage in the progression of the adventure, and the user must meet certain conditions to proceed to the next stage.

[0936] This invention is a system in which users become adventurers and participate in a treasure hunt while learning the local language. The system operates in cooperation with three parties: a server, a terminal, and a user.

[0937] Server Roles

[0938] The server receives the user's information and manages the progress of the adventure. Based on the region selected by the user, the server retrieves language data for that region from the database. For example, if the user selects the Hokkaido region, the server retrieves language data such as "namara" (naughty) and "menkoi" (cute). The server sends the retrieved language data to the user's device and verifies the user's input. If the user enters the correct language, they are allowed to proceed to the next stage.

[0939] Device Role

[0940] A terminal is a device through which a user accesses the system. The terminal receives language data sent from the server and displays it to the user. The terminal provides an interface for the user to learn the language and displays input fields for the user to enter phrases or words in the language. For example, the terminal may display the word "namara" and its meaning and provide a field for the user to enter "namara."

[0941] User Roles

[0942] A user accesses an application or website and registers as an adventurer. The user learns phrases and words in the language presented by the system and enters them into the input field. If the user enters the language correctly, the system allows the user to proceed to the next stage. For example, if the user enters "namara" correctly, they can proceed to the next stage.

[0943] Specific examples

[0944] If the user selects the Hokkaido region, the server acquires language data such as "namara" (naughty) and "menkoi" (cute) and sends it to the terminal. The terminal displays these words to the user, and if the user inputs "namara" correctly, they can proceed to the next stage.

[0945] Prompt Sentence Examples

[0946] "I want to learn the language of the Hokkaido region. Could you please tell me the meaning of the word 'namara'?"

[0947] This system is designed to allow users to enjoy a treasure hunt adventure while learning the local language. The server, terminals, and users work together to maximize the learning effect of users and provide an experience of experiencing the local culture.

[0948] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0949] Step 1:

[0950] A user accesses an application or website.

[0951] Input: User login information (ID and password)

[0952] Output: User authentication result

[0953] Specific operation: The user opens the app on their smartphone or accesses the website in their PC browser, and the login screen appears. The user enters their ID and password and presses the login button. The server receives the entered information and verifies it against the database. If authentication is successful, the user can proceed to the next screen.

[0954] Step 2:

[0955] The server receives user information and manages the progress of the adventure.

[0956] Input: User ID

[0957] Output: User's adventure progress

[0958] Specific operation: The server receives the user's ID and retrieves the user's adventure progress from the database. For example, it checks which region the user selected in the previous session and how far they progressed. The retrieved information is sent to the user's device.

[0959] Step 3:

[0960] The user selects the region for the adventure.

[0961] Input: User's selected region

[0962] Output: Information about the selected region

[0963] Specific operation: The user selects the region they want to explore from the map or list on the screen. For example, they select the Hokkaido region. After selecting, they proceed to the next screen. The server receives the user's selection and retrieves information about the selected region.

[0964] Step 4:

[0965] The server retrieves language data for the selected region from the database.

[0966] Input: Selected region information

[0967] Output: Regional language data

[0968] Specific operation: Based on the region selected by the user, the server retrieves the language data for that region from the database. For example, it retrieves "namara" (meaning "naughty") and "menkoi" (meaning "lovely") as language data for the Hokkaido region. The retrieved data is sent to the user's device.

[0969] Step 5:

[0970] The server transmits the language data to the terminal.

[0971] Input: Regional language data

[0972] Output: Language data sent to the device

[0973] Specific operation: The server sends the acquired language data to the user's device. The data is sent in JSON format, etc. The device analyzes the received data and prepares to display it to the user.

[0974] Step 6:

[0975] The terminal displays the language data to the user.

[0976] Input: Language data sent from the server

[0977] Output: Language data displayed to the user

[0978] Specific operation: The device analyzes the received language data and displays it to the user. For example, the word "namara" and its meaning are displayed. The user can check the meaning and usage of the word on the screen.

[0979] Step 7:

[0980] The user enters a phrase or word in the language.

[0981] Input: Phrases and words in the user's language

[0982] Output: User input data

[0983] Specific operation: The user learns a phrase or word in the displayed language and enters it into the input field. For example, they enter "namara." After entering, they press the send button. The device sends the user's input data to the server.

[0984] Step 8:

[0985] The server validates the user's input and if correct proceeds to the next stage.

[0986] Input: User-entered data

[0987] Output: Verification results and next stage information

[0988] Specific operation: The server receives the user's input and verifies whether it is correct. For example, it checks whether "namara" is entered correctly. If it is correct, it allows the user to proceed to the next stage, obtains new language data, and sends it to the device.

[0989] (Application example 1)

[0990] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0991] Conventional dialect learning systems often provide users with a monotonous learning experience, making it difficult to maintain motivation to learn. Furthermore, there are few opportunities to come into contact with local culture, and dialect learning often ends up being merely language learning. Furthermore, there are limited opportunities for users to actually use the dialect they have learned, making it difficult to obtain practical learning benefits.

[0992] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0993] In this invention, the server includes: a means for a user to become an adventurer and participate in a treasure hunt while learning dialects from around Japan; a means for an AI to teach dialect phrases and words; a means for the user to solve puzzles and find treasure using the dialect that the AI ​​has taught them; a means including an application to be installed on a smartphone; a means for randomly selecting dialect phrases and words and presenting them to the user; a means for randomly determining the success or failure of the mission and proceeding to the next location; and a means for randomly determining whether the treasure is found. This allows the user to have a fun time learning dialects while experiencing local culture and achieving practical learning benefits.

[0994] "User" refers to an individual who uses the system to participate in the dialect learning and treasure hunting adventure.

[0995] An "adventurer" is a user whose role is to learn dialects and go on a treasure hunt within the system.

[0996] "Dialect" refers to regional words and phrases used throughout Japan.

[0997] The "treasure hunt" refers to a virtual adventure that users go through while learning dialects within the system.

[0998] "AI" refers to a system that uses artificial intelligence technology to teach users dialect phrases and words.

[0999] A "phrase" is a combination of words that has a particular meaning.

[1000] "Language" refers to words or expressions that have a particular meaning.

[1001] "Solving the puzzle" refers to the user using the dialect they have learned to solve problems and challenges within the system.

[1002] A "smartphone" refers to a portable information terminal that can connect to the Internet and install applications.

[1003] The "application" refers to software that is installed on smartphones and allows users to learn dialects and engage in treasure hunts.

[1004] "Random" means that something is chosen at random, without following any particular rules or order.

[1005] "Mission" refers to the task or goal that a user must achieve within the system.

[1006] "Determining success or failure" refers to the system determining whether the user has accomplished the mission.

[1007] "Proceeding to the next location" refers to the user moving to a new area or stage after completing the current mission.

[1008] "Finding treasure" refers to the user discovering the treasure, which is the ultimate goal, within the system.

[1009] This invention is a system in which users become adventurers and participate in a treasure hunt while learning dialects from around Japan. The system is provided through an application installed on a smartphone.

[1010] System configuration

[1011] The system consists of the following main components:

[1012] 1. User device: Smartphone

[1013] 2. Server: A server that manages dialect data and mission data.

[1014] 3. AI Model: Generative AI model to teach dialect phrases and words

[1015] Program processing

[1016] User terminal

[1017] The user device provides an interface through an application installed on a smartphone, allowing users to participate in a treasure hunt adventure while learning the dialect. Users start the application and register as adventurers.

[1018] server

[1019] The server manages dialect data and mission data and provides them to user devices. The dialect data is stored in JSON format and includes dialect phrases and their meanings for each region. The mission data includes tasks and goals that users must complete.

[1020] AI model

[1021] The AI ​​model is responsible for teaching users dialect phrases and words. Using the generative AI model, it randomly selects dialect phrases for the user's current location and presents them to the user.

[1022] Data processing and calculation

[1023] 1. Reading dialect data: The server reads the dialect data in JSON format and provides it to the user terminal.

[1024] 2. Dialect Teaching: The AI ​​model randomly selects dialect phrases based on the user's current location and presents them to the user.

[1025] 3. Mission Judgment: The server randomly judges whether the user has completed the mission and decides whether to proceed to the next location.

[1026] 4. Treasure Hunt Judgment: The server randomly judges whether the user finds the treasure or not.

[1027] Specific examples

[1028] The user launches the application and learns the Hokkaido dialect "namara." Then, they complete a mission to move on to the next location (say, Okinawa) and learn the Okinawa dialect "churasan." Finally, they repeat this process until they find the treasure.

[1029] Prompt Sentence Examples

[1030] "Please tell me the Hokkaido dialect."

[1031] "Starting the next mission."

[1032] "Have you found any treasure?"

[1033] In this way, users can enjoy a virtual adventure while learning dialects from all over Japan.

[1034] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1035] Step 1:

[1036] The user launches the application on their smartphone.

[1037] Input: User operation (application launch)

[1038] Output: Application home screen

[1039] Specific behavior: The user taps the application icon, the application launches, the home screen is displayed, and the user selects the option to register as an adventurer.

[1040] Step 2:

[1041] The user registers as an adventurer.

[1042] Input: User registration information (name, email address, etc.)

[1043] Output: User registration completion message

[1044] Specific operation: The user enters their name and email address and presses the registration button. The server receives the registration information and stores it in the database. A message that registration is complete is displayed to the user.

[1045] Step 3:

[1046] The server reads the dialect data and provides it to the user terminal.

[1047] Input: Dialect data (JSON format)

[1048] Output: Sending dialect data to the user's terminal

[1049] Specific operation: The server reads the dialect data in JSON format, selects the appropriate dialect data based on the user's current location, and sends the selected dialect data to the user's device.

[1050] Step 4:

[1051] The AI ​​model teaches users dialect phrases and words.

[1052] Input: User's current location, dialect data

[1053] Output: Dialect phrases and their meanings

[1054] How it works: The AI ​​model randomly selects a dialect phrase based on the user's current location and presents it to the user along with its meaning. The user can then view the dialect phrase and its meaning on the screen.

[1055] Step 5:

[1056] The user starts the mission.

[1057] Input: User operation (pressing the mission start button)

[1058] Output: Display mission details

[1059] Specific operation: When the user presses the mission start button, the server loads the mission data and presents it to the user. The mission details are displayed on the screen.

[1060] Step 6:

[1061] The server determines whether the mission is successful or not.

[1062] Input: User's mission progress

[1063] Output: Mission success / failure result

[1064] Specific operation: The user progresses through the mission and sends the results to the server. The server randomly determines whether the mission was successful or not and notifies the user of the result.

[1065] Step 7:

[1066] The user proceeds to the next location.

[1067] Input: Mission success result

[1068] Output: Dialect data for the following locations

[1069] Specific operation: If the mission is successful, the server randomly selects the next location and sends the dialect data of that location to the user's device. The user then proceeds to the next location.

[1070] Step 8:

[1071] The server determines whether the treasure is found.

[1072] Input: User's mission progress

[1073] Output: Judgment result of whether or not treasure was found

[1074] Specific operation: After the user completes multiple missions, the server randomly determines whether or not the treasure is found. The result is notified to the user, and if the treasure is found, the adventure ends.

[1075] Example 2

[1076] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1077] In conventional treasure hunt games, users have limited opportunities to learn about local dialects and cultures, making it difficult to combine learning with entertainment. Furthermore, when users input their answers, they lack a system that instantly determines whether their answers are correct and provides feedback to guide them to the next step.

[1078] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1079] In this invention, the server includes a means for a user to become an adventurer and participate in a treasure hunt adventure while learning the local dialect, a means for teaching dialect phrases and words using a generative AI model, a means for the user to solve puzzles and find treasure using the dialect they have been taught, a means for analyzing the user's answer and determining whether it is correct, and a means for providing the next hint or information on the location of the treasure if the answer is correct. This allows the user to enjoy treasure hunting as entertainment while learning about the local dialect and culture.

[1080] "User" refers to an individual who uses the System to participate in a treasure hunting adventure.

[1081] An "adventurer" is a user who takes part in a treasure hunt and solves mysteries while learning the dialect.

[1082] A "regional dialect" refers to words and phrases unique to a particular region.

[1083] "Generative AI model" refers to a model that generates text using artificial intelligence technology.

[1084] "Means of teaching phrases and words" refers to methods that use generative AI models to provide dialect phrases and words to users.

[1085] "Means for solving the puzzle" refers to the method by which the user solves the presented puzzle using the dialect that they have been taught.

[1086] "Means for analyzing the answer" refers to a method for analyzing the answer entered by the user and determining whether it is correct or not.

[1087] The "means for determining whether the answer is correct" refers to a method for determining whether the user's answer is correct.

[1088] "Means for providing information about the next hint or treasure location" refers to a method for providing information to the user to proceed to the next step if the user answers correctly.

[1089] "System" refers to a device or program that includes a set of means for users to enjoy a treasure hunt adventure while learning a dialect.

[1090] This invention is a system that allows users to enjoy a treasure hunt adventure while learning local dialects. The system is realized using a server, terminals, and a generative AI model.

[1091] The server uses a generative AI model to generate dialect phrases and words. Specifically, it uses a general artificial intelligence technology (e.g., GPT-4) as the generative AI model. The server inputs a prompt sentence into the generative AI model and generates hints for solving the puzzle that include dialect. For example, the following prompt sentence is used:

[1092] "Generate the following hint for the user: 'It's hidden in a really cold place.' The aim of this hint is to help the user learn the Hokkaido dialect word 'namara.'"

[1093] The generated hint is sent from the server to the user's device. The device displays the received hint to the user. The user reads the hint through the device and solves the mystery while learning the meaning of the dialect. For example, if the user receives the hint "It's hidden in a very cold place," the user will learn that "namara" means "very" in the Hokkaido dialect and will infer that the treasure is hidden in a cold place, that is, Hokkaido.

[1094] When the user enters the answer into the device, the device sends the answer to the server. The server analyzes the user's answer and determines whether it is correct. The analysis uses string comparison and natural language processing techniques. If the answer is correct, the server sends the next hint and information about the location of the treasure to the device. The device displays the received information to the user and proceeds to the next step.

[1095] This system allows users to enjoy treasure hunting as entertainment while learning about local dialects and culture. For example, a user receives the hint "It's hidden in a very cold place," learns that "namara" means "very," and infers that the treasure is hidden in Hokkaido.

[1096] In this way, by combining the server, terminal, and generative AI model, we have created a system that allows users to enjoy a treasure hunt adventure while learning dialects.

[1097] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1098] Step 1:

[1099] A user accesses the system.

[1100] A user accesses the system using a web browser or a dedicated app. The device receives the user's access request and sends it to the server. The server verifies the user's authentication information and grants access. The input is the user's authentication information, and the output is the access permission status.

[1101] Step 2:

[1102] The server uses a generative AI model to generate clues to solve the puzzle.

[1103] The server inputs a prompt into the generative AI model and generates a hint for solving the puzzle, including the dialect. An example of a specific prompt is: "Generate the following hint for the user: 'It's hidden in a really cold place.' The aim of this hint is for the user to learn the Hokkaido dialect 'namara.'" The input is the prompt, and the output is the generated hint.

[1104] Step 3:

[1105] The server sends the generated hint to the terminal.

[1106] The server sends the generated hint to the user's terminal, and the terminal displays the received hint to the user. The input is the generated hint, and the output is the hint displayed to the user.

[1107] Step 4:

[1108] Users receive hints and solve puzzles while learning the meaning of dialect words.

[1109] The user reads the hint displayed on the terminal and checks the dialect meaning. For example, if the user receives the hint "It's hidden in a very cold place," the user learns that "namara" means "very" in the Hokkaido dialect. The input is the displayed hint, and the output is the user's understanding.

[1110] Step 5:

[1111] The user enters the answer into the terminal.

[1112] The user inputs the answer to the puzzle into the terminal. For example, they might input "The treasure is hidden in Hokkaido." The terminal then sends the user's answer to the server. The input is the user's answer, and the output is the answer sent to the server.

[1113] Step 6:

[1114] The server analyzes the user's answer and determines whether it is correct.

[1115] The server receives the user's input and analyzes it to determine whether it is correct. The analysis uses string comparison and natural language processing techniques. The input is the user's answer, and the output is the result of the judgment as to whether it is correct or not.

[1116] Step 7:

[1117] The server sends the results to the device, providing information about the next hint and treasure location.

[1118] The server sends the analysis results to the terminal, which displays the results to the user and, if the answer is correct, provides the next hint and information about the location of the treasure. The input is the analysis results, and the output is the result displayed to the user and the next hint.

[1119] (Application example 2)

[1120] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1121] Opportunities to learn about regional cultures and dialects are declining in modern education and entertainment. Furthermore, the number of educational materials and applications for learning dialects is limited, making learning monotonous. Furthermore, the lack of interactive experiences that allow users to have fun while learning dialects makes it difficult to keep users engaged.

[1122] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1123] In this invention, the server includes: a means for a user to become an adventurer and participate in a treasure hunt while learning dialects from around Japan; a means for an AI to teach dialect phrases and words; a means for the user to solve puzzles and find treasure using the dialect that the user has learned; a means for generating puzzles that include dialect using a generative AI model; and a means for providing an application to be installed on a smartphone. This allows users to enjoy an interactive puzzle game while learning dialects, providing an experience of experiencing local culture.

[1124] A "user" is an individual who uses the system to learn dialects and participate in a puzzle-solving game.

[1125] An "adventurer" is a user whose role is to participate in a treasure hunt adventure while learning dialects within the system.

[1126] "Dialect" refers to regional words and phrases used throughout Japan.

[1127] A "treasure hunt adventure" refers to a series of activities in which users learn dialects, solve puzzles, and ultimately find treasure.

[1128] "AI" refers to artificial intelligence, which is responsible for teaching users dialect phrases and words.

[1129] A "phrase" is a combination of words that has a particular meaning.

[1130] "Language" refers to words or expressions that have a particular meaning.

[1131] "Solving a mystery" refers to the act of understanding the given hints and questions and deriving the correct answer.

[1132] "Finding treasure" refers to solving a mystery and ultimately discovering a desired location or object.

[1133] A "generative AI model" refers to a system that uses artificial intelligence to automatically generate puzzle-solving questions that include dialects.

[1134] "Puzzle Problem" refers to a problem or challenge given to the user to solve.

[1135] A "smartphone" is a type of mobile phone that can be used to install applications.

[1136] "Application" refers to software that is installed on a smartphone and provides specific functions or services.

[1137] The system for implementing this invention allows users to become adventurers and participate in a treasure hunt while learning dialects from all over Japan. A specific embodiment of this system will be described below.

[1138] 1. System Configuration

[1139] The system consists of the following main components:

[1140] User device (smartphone)

[1141] server

[1142] Generative AI model (OpenAI GPT-3)

[1143] 2. Program Processing

[1144] User terminal

[1145] The user device is a smartphone with a dedicated application installed, which provides an interface for users to learn dialects and participate in puzzle-solving games.

[1146] server

[1147] The server has the following functions:

[1148] Dialect database management

[1149] Generating puzzles using generative AI models

[1150] User progress management

[1151] Generative AI Models

[1152] A generative AI model (OpenAI GPT-3) is used to automatically generate puzzles that include dialects. Specifically, given the following prompt sentence as input, it generates appropriate puzzles.

[1153] Data processing and calculation

[1154] The server retrieves specific dialects and their meanings from a dialect database and inputs prompt sentences into the generative AI model. The generated puzzles are then sent to the user's device, where the user answers them.

[1155] 3. Specific Examples

[1156] When users launch the application, they are first shown a section to learn the Hokkaido dialect. They learn that "namara" means "very." They are then presented with a puzzle generated by a generative AI model (GPT-3).

[1157] Prompt Sentence Examples

[1158] Use the Hokkaido dialect "namara" to generate puzzles that help users find the treasure.

[1159] The user remembers that "namara" means "very," and guesses that "cold place" refers to Hokkaido. After entering the correct answer, the next puzzle question is displayed.

[1160] In this way, users can enjoy an interactive puzzle game while learning dialects, providing an experience of experiencing local culture.

[1161] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1162] Step 1:

[1163] The user launches an application.

[1164] Input: User action (launching an application)

[1165] Output: Application home screen

[1166] What Happens: A user taps on an application icon on their smartphone, launching the application and displaying the home screen, which presents the user with options to begin their adventure.

[1167] Step 2:

[1168] The user selects the dialect learning section.

[1169] Input: User action (selection of dialect learning section)

[1170] Output: Dialect learning screen

[1171] What it does: A user taps the "Learn Dialects" button on the home screen and is taken to the dialect learning section, where they are shown dialects for a specific region and their meanings.

[1172] Step 3:

[1173] The server obtains dialect information from the dialect database.

[1174] Input: User's choice (specific region)

[1175] Output: Dialect information (e.g. "namara" -> "very")

[1176] Specific operation: The server accesses the dialect database and obtains dialect information for the region selected by the user. The obtained information is sent to the user's device and displayed on the dialect learning screen.

[1177] Step 4:

[1178] The user learns the dialect.

[1179] Input: Dialect information

[1180] Output: User comprehension (internal data)

[1181] What it does: The user reads and learns the dialect information displayed. The application records the user's learning progress as internal data.

[1182] Step 5:

[1183] The user starts the puzzle game.

[1184] Input: User action (start of puzzle game)

[1185] Output: Display of the puzzle

[1186] Specific operation: The user taps the "Start puzzle game" button to start the game. The server sends a prompt to the generative AI model.

[1187] Step 6:

[1188] The server sends a prompt to the generative AI model to generate a puzzle.

[1189] Input: Prompt text (e.g., "Please generate a puzzle to help the user find the treasure using the Hokkaido dialect word 'namara'.")

[1190] Output: Generated puzzle

[1191] Specific operation: The server sends a prompt to the generative AI model (OpenAI GPT-3) to generate a puzzle that includes dialect. The generated puzzle is returned to the server.

[1192] Step 7:

[1193] The server transmits the generated puzzle to the user terminal.

[1194] Input: Generated puzzle

[1195] Output: The puzzle displayed on the user's device

[1196] Specific operation: The server sends the generated puzzle to the user's terminal, and the problem is displayed on the user's terminal.

[1197] Step 8:

[1198] The user answers the puzzle questions.

[1199] Input: User's answer

[1200] Output: Answer evaluation result

[1201] Specific operation: The user enters the answer to the puzzle and taps the send button. The user device sends the answer to the server.

[1202] Step 9:

[1203] The server evaluates the user's answers.

[1204] Input: User's answer

[1205] Output: Evaluation result (correct / incorrect)

[1206] Specific operation: The server evaluates the user's answer and determines whether it is correct or incorrect. The evaluation result is sent to the user's device.

[1207] Step 10:

[1208] The user terminal displays the evaluation results.

[1209] Input: Evaluation result

[1210] Output: Feedback to the user

[1211] Specific operation: The user device receives the evaluation result and displays feedback to the user. If the answer is correct, the next puzzle question will be displayed.

[1212] Example 3

[1213] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1214] Conventional dialect learning systems have limited opportunities for users to learn about local culture, customs, and history, resulting in a lack of interactive elements that allow users to effectively learn dialect phrases and words, often resulting in a lack of motivation to learn.

[1215] The identification process by the identification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for the user to become an adventurer and participate in a treasure hunt adventure while learning the local dialect, a means for the generative AI model to generate dialect phrases and words based on a prompt sentence, a means for the user to solve puzzles and find treasure using the dialect that the server has taught them, a means for the server to compare the generated content with a local culture database and select appropriate content, and a means for the terminal to display the generated dialect phrases and words to the user. This allows the user to experience local culture through learning the dialect.

[1216] "User" refers to an individual who uses the system to participate in dialect learning and treasure hunting adventures.

[1217] "Adventurer" refers to the role of a user who takes part in a treasure hunt adventure while learning dialects within the system.

[1218] A "dialect" refers to a unique linguistic expression or phrase used in a particular region or community.

[1219] "Generative AI model" refers to an artificial intelligence algorithm that generates dialect phrases and words based on user prompts.

[1220] "Prompt text" refers to the text of an instruction or question that a user enters into a system.

[1221] "Server" refers to the computer system that manages the processing of the entire system and connects with generative AI models and databases.

[1222] A "regional cultural database" refers to a database containing information about the customs, habits, and history of a particular region.

[1223] "Terminal" refers to the device through which a User accesses the System and displays dialect phrases and words.

[1224] The "treasure hunt adventure" refers to an activity in which users solve puzzles within the system and find treasure while learning the dialect.

[1225] "Solutions" refers to how users use the dialect they have been taught to solve problems and challenges within the system.

[1226] This invention is a system that allows users to participate in a treasure hunt adventure while learning a local dialect. The system uses a generative AI model to generate dialect phrases and words based on prompts and provides them to the user. A specific embodiment of this system is described below.

[1227] Hardware and software used

[1228] server

[1229] The server manages the overall processing of the system and coordinates with the generative AI model and the local cultural database. The server is a high-performance computer system that communicates with terminals via the Internet.

[1230] Terminal

[1231] A terminal is a device through which a user accesses the system and displays dialect phrases and words. A terminal can be a PC, smartphone, tablet, or other device with an internet connection.

[1232] Generative AI Models

[1233] A generative AI model is an artificial intelligence algorithm that generates dialect phrases and words based on a user prompt, specifically using an advanced natural language processing model such as OpenAI's GPT-4.

[1234] Regional Cultural Database

[1235] A regional cultural database is a database containing information about the customs, habits, and history of a particular region. The server references this database to verify the appropriateness of generated dialect phrases and words.

[1236] Program processing

[1237] The server receives a prompt from the user and inputs it into the generative AI model. The generative AI model generates dialect phrases and words based on the prompt. The server compares the generated content with the local cultural database and selects appropriate content. The selected content is sent from the server to the device, which displays it to the user.

[1238] Specific examples

[1239] For example, if the user inputs "Teach me how to greet people in the Osaka dialect," the following processing will be performed.

[1240] 1. The user types "Tell me how to greet someone in Osaka dialect" into the input field on the terminal.

[1241] 2. The terminal sends this prompt to the server.

[1242] 3. The server receives the prompt and inputs the prompt "Tell me how to greet someone in Osaka dialect" into the generative AI model.

[1243] 4. The generative AI model generates phrases such as "Maido!" and "Ookinii!"

[1244] 5. The server matches the generated phrase with the local cultural database and selects appropriate content.

[1245] 6. The server sends the selected phrase to the terminal.

[1246] 7. The device displays "Welcome!" or "Thank you!" to the user.

[1247] In this way, the user can gain experience of coming into contact with the local culture through learning the dialect. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1248] Step 1:

[1249] The user inputs a prompt into the input field of the terminal. For example, the user inputs "Tell me how to greet someone in the Osaka dialect." The input prompt is temporarily stored in the terminal's memory.

[1250] Step 2:

[1251] The terminal sends the entered prompt text to the server. The terminal uses an Internet connection to send the prompt text to the server as a data packet. The input is the prompt text, and the output is a notification to the server that transmission has been completed.

[1252] Step 3:

[1253] The server receives the prompt text. The server analyzes the received prompt text and converts it into a format for input to the generative AI model. The input is the prompt text, and the output is the data format for input to the generative AI model.

[1254] Step 4:

[1255] The server inputs a prompt sentence into the generative AI model, which generates dialect phrases and words based on the prompt sentence. The input is the converted prompt sentence, and the output is the generated dialect phrases and words.

[1256] Step 5:

[1257] The generative AI model generates dialect phrases and words. For example, phrases such as "Maido!" and "Ookinii!" are generated. The generated data is returned to the server. The input is the prompt sentence, and the output is the generated phrase or word.

[1258] Step 6:

[1259] The server matches the generated content with the local cultural database. The server compares the generated phrases or words with the information in the database and selects the appropriate content. The input is the generated phrases or words, and the output is the match result.

[1260] Step 7:

[1261] The server selects the appropriate content. For example, it determines that "Maido!" and "Ookini!" are appropriate Osaka dialects. The selected content is stored in the server's memory. The input is the matching result, and the output is the selected phrase or word.

[1262] Step 8:

[1263] The server sends the selected content to the terminal. The server sends the selected phrase or word to the terminal as a data packet over the Internet. The input is the selected phrase or word, and the output is a notification to the terminal that transmission has been completed.

[1264] Step 9:

[1265] The terminal displays the generated dialect phrases and words to the user. The terminal analyzes the received data and displays it on the screen. For example, "Maido!" or "Ookinii!" are displayed. The input is the phrase or word received from the server, and the output is what is displayed to the user.

[1266] (Application example 3)

[1267] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1268] Conventional language learning systems have the problem that users have few opportunities to come into contact with the local culture and history, and end up only learning the language. Furthermore, there is a lack of effective ways for tourists to learn the language and culture of the region they visit, which makes it difficult to communicate smoothly with local residents. This limits the tourist experience and prevents them from fully enjoying the charm of the region.

[1269] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1270] In this invention, the server includes means for a user to become an adventurer and participate in a treasure hunt while learning the local language, means for an artificial intelligence to teach language phrases and words, means for the user to solve puzzles and find treasure using the language that has been taught, means for providing a language phrasebook for each region, means for providing a language pronunciation guide, means for providing information on the history and customs of the region, and means for providing an interactive program that helps communication with local residents. This allows users to learn the local language while experiencing the culture and history of the region, making for a deeper and richer tourist experience.

[1271] "User" refers to an individual who uses the system to learn about local languages ​​and cultures and participate in treasure hunting adventures.

[1272] "Adventurer" refers to a user whose role within the system is to participate in a treasure hunting adventure while learning the local language.

[1273] "Regional language" refers to the dialects and idiosyncratic words used in a particular region.

[1274] "Artificial intelligence" refers to computer programs used to teach users language phrases and words.

[1275] A "language phrase" refers to a short word or expression that is commonly used in a particular region.

[1276] "Language" refers to words and expressions used in a particular region.

[1277] "Solving the puzzle" refers to the act of a user using the language they have been taught to solve a problem or challenge within the system.

[1278] "Finding treasure" refers to the act of a user achieving a goal or reward set within the system.

[1279] A "language phrasebook" refers to a database of phrases in a language used in a particular region.

[1280] "Pronunciation Guide" refers to an audio playback feature that helps users learn correct pronunciation.

[1281] "Information on history and customs" refers to data about the history, culture, and habits of a particular region.

[1282] "Interactive programs" refer to chatbots and dialogue systems that help communicate with local residents.

[1283] A system for implementing this invention includes a server, a user terminal, and artificial intelligence. The server provides a program that allows users to become adventurers and participate in a treasure hunt while learning the local language. The user terminal is a device such as a smartphone or tablet, and provides an interface for users to access the system and learn the local language and culture. The artificial intelligence is used to teach the user phrases and words in the language.

[1284] The server has a means for providing regional language phrasebooks and provides appropriate language phrases based on the region selected by the user. In addition, the server provides a language pronunciation guide and has an audio playback function to help the user learn correct pronunciation. Information about the history and customs of the region is also provided by the server, allowing the user to learn about the culture and history of the region.

[1285] The user terminal provides an interactive program that helps users communicate with local residents, allowing them to communicate smoothly with local residents and gain a deeper understanding of local culture.

[1286] As a concrete example, consider a tourist visiting Osaka who uses the app. When the tourist opens the app and selects "Osaka," the following dialect phrases and cultural information are displayed:

[1287] Dialect phrase: "Nandeyanen"

[1288] Cultural information: "Osaka is a commercial center and has a rich food culture."

[1289] An example prompt is:

[1290] "Please tell me the Osaka dialect."

[1291] "Please tell me about Osaka culture."

[1292] In this way, users can learn the local language while also experiencing the local culture and history, making their tourist experience deeper and richer.

[1293] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1294] Step 1:

[1295] The user launches the application using the terminal.

[1296] Input: User actions

[1297] Output: Application launch screen

[1298] Specific action: A user taps on an app on a smartphone or tablet, and the application launches.

[1299] Step 2:

[1300] Select the area the user will visit.

[1301] Input: User's region selection

[1302] Output: Selected region information

[1303] Specific behavior: The user selects the region they want to visit (e.g., Osaka) from the in-app menu.

[1304] Step 3:

[1305] The server provides a local language phrasebook based on the selected region.

[1306] Input: Selected region information

[1307] Output: Regional language phrasebooks

[1308] Specific operation: The server retrieves language phrases for the selected region (e.g., Osaka) from the database and sends them to the user's terminal.

[1309] Step 4:

[1310] The user learns language phrases.

[1311] Input: Regional Language Phrasebook

[1312] Output: User's learning progress

[1313] What it does: The user reads the language phrases displayed in the app and plays the pronunciation guide to learn.

[1314] Step 5:

[1315] The server provides information about the history and customs of the area.

[1316] Input: Selected region information

[1317] Output: Information about the history and customs of the area

[1318] Specific operation: The server retrieves information about the history and customs of the selected region (e.g., Osaka) from the database and sends it to the user's terminal.

[1319] Step 6:

[1320] Users learn information about local history and customs.

[1321] Input: Information about local history and customs

[1322] Output: User's learning progress

[1323] Specific Action: The user reads and understands information about local history and customs displayed within the app.

[1324] Step 7:

[1325] The server provides an interactive program, allowing users to communicate with local residents.

[1326] Input: User interaction request

[1327] Output: Interactive program response

[1328] Specific operation: A user initiates a dialogue with local residents using the chatbot function within the app, and the server generates a response through a conversational program.

[1329] Step 8:

[1330] Users use the language they've been taught to solve puzzles and find treasure.

[1331] Input: User's linguistic knowledge and puzzle-solving challenges

[1332] Output: Treasure found

[1333] What it does: The user solves puzzles provided within the app and finds treasure using the language they've been taught.

[1334] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1335] "Example 1"

[1336] One embodiment of the present invention is a system that incorporates an emotion engine that recognizes a user's emotions. In this system, a user accesses the system through an application or website and becomes an adventurer. The system uses AI to teach the user dialect phrases and words. For example, the system teaches phrases and words such as "namara" (a kind of "namara") and "menkoi" (a kind of "lovely"), which are used in the Hokkaido dialect. The emotion engine then recognizes the user's emotions and adjusts the dialect phrases and words it teaches based on those emotions. For example, if the user expresses joy, the AI ​​teaches fun phrases and words to match that emotion. The user then uses the dialect they learned to solve puzzles and find treasure. The emotion engine then adjusts the difficulty of the puzzles based on the user's emotions. For example, if the user expresses frustration, the emotion engine lowers the difficulty of the puzzles to match that emotion. This allows the user to enjoy an optimal learning experience tailored to their emotions.

[1337] "Example 2"

[1338] One embodiment of the present invention is a system that incorporates an emotion engine that recognizes a user's emotions. In this system, a user accesses the system through an application or website and becomes an adventurer. The system uses AI to teach the user dialect phrases and words. For example, the system teaches phrases and words such as "namara" (a kind of "namara") and "menkoi" (a kind of "lovely"), which are used in the Hokkaido dialect. The emotion engine then recognizes the user's emotions and adjusts the dialect phrases and words it teaches based on those emotions. For example, if the user expresses joy, the AI ​​teaches fun phrases and words to match that emotion. The user then uses the dialect they learned to solve puzzles and find treasure. The emotion engine then adjusts the difficulty of the puzzles based on the user's emotions. For example, if the user expresses frustration, the emotion engine lowers the difficulty of the puzzles to match that emotion. This allows the user to enjoy an optimal learning experience tailored to their emotions.

[1339] "Example 3"

[1340] One embodiment of the present invention is a system that incorporates an emotion engine that recognizes a user's emotions. In this system, a user accesses the system through an application or website and becomes an adventurer. The system uses AI to teach the user dialect phrases and words. For example, the system teaches phrases and words such as "namara" (a kind of "namara") and "menkoi" (a kind of "lovely"), which are used in the Hokkaido dialect. The emotion engine then recognizes the user's emotions and adjusts the dialect phrases and words it teaches based on those emotions. For example, if the user expresses joy, the AI ​​teaches fun phrases and words to match that emotion. The user then uses the dialect they learned to solve puzzles and find treasure. The emotion engine then adjusts the difficulty of the puzzles based on the user's emotions. For example, if the user expresses frustration, the emotion engine lowers the difficulty of the puzzles to match that emotion. This allows the user to enjoy an optimal learning experience tailored to their emotions.

[1341] The processing flow of each embodiment will be described below.

[1342] "Example 1"

[1343] Step 1: A user accesses the system through an application or website.

[1344] Step 2: The system uses AI to teach the user dialect phrases and words, such as "namara" (meaning "namara") and "menkoi" (meaning "lovely"), which are used in the Hokkaido dialect.

[1345] Step 3: The emotion engine recognizes the user's emotions and adjusts the dialect phrases and words that the AI ​​teaches according to those emotions. For example, if the user expresses joy, the AI ​​will teach them fun phrases and words to match that emotion.

[1346] Step 4: The user solves the puzzle and finds the treasure using the dialect they were taught.

[1347] Step 5: The emotion engine adjusts the difficulty of the puzzle according to the user's emotions. For example, if the user expresses frustration, the emotion engine will lower the difficulty of the puzzle to match that emotion.

[1348] Example 1

[1349] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1350] Conventional dialect learning systems have difficulty providing a learning experience that responds to the user's emotions, which can lead to a decline in motivation to learn. Furthermore, systems that combine dialect learning and puzzle solving have the problem of not adjusting the difficulty level according to the user's emotions, which can lead to users becoming discouraged.

[1351] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1352] In this invention, the server includes means for a user to become an adventurer and participate in a treasure hunt while learning dialects from around Japan, means for an artificial intelligence to teach dialect phrases and words, means for the user to solve puzzles and find treasure using the dialect that has been taught to them, means for recognizing the user's emotions, means for adjusting the dialect phrases and words in accordance with the user's emotions, and means for adjusting the difficulty of the puzzles in accordance with the user's emotions. This provides an optimal learning experience in accordance with the user's emotions, making it possible to learn dialects while maintaining motivation to learn.

[1353] "User" refers to an individual who uses the system to participate in an adventure while learning a dialect.

[1354] "Adventurer" refers to the role of a user who takes part in a treasure hunt adventure while learning dialects within the system.

[1355] "Dialects from around Japan" refers to unique words and phrases used in specific regions of Japan.

[1356] A "treasure hunt adventure" refers to a series of activities in which users learn dialects, solve puzzles, and ultimately find treasure.

[1357] "Artificial intelligence" refers to the technology that enables computer systems to mimic human intelligence to learn and solve problems.

[1358] "Dialect phrases and words" refer to words and expressions unique to a particular region.

[1359] "Solving the puzzle" refers to the user solving a problem or challenge provided by the system.

[1360] "Emotion recognition" refers to the system analyzing the user's emotions from their facial expressions and voice.

[1361] "Adjusting according to emotions" refers to the system changing the content and difficulty level provided based on the user's emotions.

[1362] "Adjusting the difficulty level" refers to changing the difficulty level of the puzzles and challenges provided by the system according to the user's emotions and progress.

[1363] This invention is a system in which a user becomes an adventurer and participates in a treasure hunt while learning dialects from around Japan. The system operates when the user accesses it through an application or website. A specific embodiment of this system will be described below.

[1364] Hardware and software used

[1365] The system uses the following hardware and software:

[1366] Server: Manages user information, generates scenarios, and runs the AI ​​engine and emotion engine.

[1367] Device: A smartphone or PC used by a user. It is equipped with a camera and microphone and captures the user's facial expressions and voice.

[1368] Generative AI models: For example, using OpenAI's GPT-4 to generate dialect phrases and words.

[1369] Emotion engine: Recognize user emotions using, for example, Microsoft's Azure Emotion API.

[1370] System Operation

[1371] When a user accesses an application or website, the server receives the user's information and generates an adventure scenario. The scenario includes regional dialect phrases and words, such as "namara" (meaning "namara") and "menkoi" (meaning "lovely"), which are used in the Hokkaido dialect.

[1372] The server uses a generative AI model to teach the user dialect phrases and words, and as the user progresses through the dialect, the system offers puzzle-solving tasks as the next step.

[1373] While a user is using the system through an application or website, the device's camera and microphone are used to capture the user's facial expressions and voice. The server uses an emotion engine to analyze the user's emotions from the captured data.

[1374] When the emotion engine recognizes the user's emotion, the server sends a prompt to the generative AI model based on that emotion, generating dialect phrases and words that correspond to the user's emotion. For example, if the user expresses joy, it generates a phrase such as "It's so fun!"

[1375] Users learn dialect phrases and words provided by the server and use them to solve puzzles. For example, a puzzle using the Japanese word "namara" is presented.

[1376] The server records the user's learning progress and the results of solving puzzles, and provides new dialect phrases and solutions as the next step. If the user expresses frustration, the server adjusts the difficulty of the puzzles based on the results of the emotion engine.

[1377] Specific examples

[1378] When a user joins an adventure in the Hokkaido region, the server teaches dialect words such as "namara" and "menkoi." If the user uses "namara" correctly, the next step is to solve a puzzle using "namara."

[1379] If the emotion engine recognizes the user's emotion of joy, the server will teach them phrases such as "It's so much fun!" Conversely, if the user expresses emotion of frustration, the server will provide them with "It's such an easy riddle."

[1380] Prompt Sentence Examples

[1381] Examples of prompts to input to a generative AI model include:

[1382] "A user is participating in an adventure in the Hokkaido region. Teach the user the dialect for 'namara' (meaning "namara" in Japanese). If the user is expressing joy, teach them a fun phrase."

[1383] In this way, users can have an optimal learning experience according to their own emotions.

[1384] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1385] Step 1:

[1386] A user accesses an application or website.

[1387] Input: User login information (username, password)

[1388] Output: User authentication result, user ID

[1389] How it works: A user uses a smartphone or PC to access a dedicated application or website and enters their username and password on the login screen. The server receives this and authenticates it by checking it against a database. If authentication is successful, a user ID is generated and the system proceeds to the next step.

[1390] Step 2:

[1391] The server receives the user information and generates an adventure scenario.

[1392] Input: User ID, past progress information, user's selected region

[1393] Output: Adventure scenario

[1394] Specific operation: The server retrieves past progress information from the database based on the user ID. Based on the region selected by the user (e.g., Hokkaido), it generates an appropriate adventure scenario. The scenario includes dialect phrases and words from that region.

[1395] Step 3:

[1396] The server uses generative AI models to generate dialect phrases and words.

[1397] Input: Adventure scenario, prompt

[1398] Output: Dialect phrases and words

[1399] How it works: The server sends a prompt to a generative AI model (e.g., OpenAI's GPT-4). The prompt might include something like, "Please tell me the dialect of the Hokkaido region." The generative AI model then generates dialect phrases and words based on this and returns them to the server.

[1400] Step 4:

[1401] The server uses an emotion engine to recognize the user's emotion.

[1402] Input: User's facial expression data, voice data

[1403] Output: User sentiment analysis results

[1404] Specific operation: While a user is using the system through an application or website, the device's camera and microphone are used to capture the user's facial expressions and voice. The server uses an emotion engine (e.g., Microsoft's Azure Emotion API) to analyze the user's emotions from the captured data.

[1405] Step 5:

[1406] The server adjusts dialect phrases and words according to the user's emotions.

[1407] Input: Dialect phrases and words, user sentiment analysis results

[1408] Output: Adjusted dialect phrases and words

[1409] Specific operation: Based on the analysis results of the emotion engine, the server sends a prompt to the generative AI model, which generates dialect phrases and words that correspond to the user's emotions. For example, if the user is expressing joy, it generates a phrase such as "It's so fun!"

[1410] Step 6:

[1411] Users learn dialects and try to solve puzzles.

[1412] Input: Adjusted dialect phrases and words, puzzle questions

[1413] Output: Answers to puzzles, learning progress

[1414] Specific operation: The user learns dialect phrases and words provided by the server and attempts to solve puzzles using them. For example, a puzzle using the Japanese word "namara" is presented. When the user enters the answer, the server receives the answer and determines whether it is correct.

[1415] Step 7:

[1416] The server tracks the user's progress and provides next steps.

[1417] Input: Answers to puzzles, learning progress

[1418] Output: Next step adventure scenario, dialect phrases and words

[1419] Specific operation: The server records the user's learning progress and the results of solving puzzles, and generates new dialect phrases and solutions as the next step. If the user expresses frustration, the server lowers the difficulty of the next puzzle based on the results of the emotion engine.

[1420] (Application example 1)

[1421] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1422] Conventional dialect learning systems have had difficulty providing an appropriate learning experience that reflects the user's emotions and current location. Furthermore, systems that combine dialect learning with a treasure hunt adventure did not adjust the difficulty level based on the user's emotions or provide dialect phrases based on location information, making it difficult to maintain the user's motivation to learn. This meant that users were not provided with a satisfactory experience of learning dialects while experiencing local culture.

[1423] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1424] In this invention, the server includes: a means for a user to become an adventurer and participate in a treasure hunt while learning dialects from around Japan; a means for an AI to teach dialect phrases and words; a means for the user to solve puzzles and find treasure using the dialect that the AI ​​has taught them; a means for an emotion engine that recognizes the user's emotions to adjust the dialect phrases and words in accordance with the emotions; a means for recognizing the user's current location and providing dialect phrases based on the location information; and a means for the emotion engine to adjust the difficulty of the puzzles in accordance with the user's emotions. This provides an optimal learning experience according to the user's emotions and current location, making it possible to learn dialects while experiencing local culture.

[1425] An "adventurer" is a person who takes on the role of participating in a treasure hunt adventure while learning dialects within the system.

[1426] A "dialect" is a word or phrase that varies from region to region in Japan.

[1427] "AI" stands for artificial intelligence, a technology that teaches users dialect phrases and words.

[1428] An "emotion engine" is a technology that recognizes the user's emotions and adjusts the system's behavior according to those emotions.

[1429] "Location information" is data indicating the user's current location, and is obtained using technology such as GPS.

[1430] "Difficulty adjustment" means changing the difficulty of the puzzle according to the user's feelings.

[1431] "Treasure hunt" is an activity in which users find treasure using hints provided within the system.

[1432] A "learning experience" is an experience in which users learn dialects and come into contact with local culture through the system.

[1433] A system for implementing this invention allows users to become adventurers and participate in a treasure hunt while learning dialects from around Japan. The system combines an emotion engine that recognizes the user's emotions with a location information system that recognizes the user's current location to provide an optimal learning experience.

[1434] Hardware and software used

[1435] Hardware: Smartphone (camera, GPS)

[1436] Software: OpenCV (image processing), Keras (emotion recognition model), geopy (distance calculation)

[1437] System configuration

[1438] 1. Emotion recognition

[1439] The smartphone camera captures the user's facial expression and detects the face using OpenCV. The detected face image is input into the Keras emotion recognition model to recognize the user's emotion.

[1440] 2. Providing dialect phrases

[1441] Based on the recognized emotion, the AI ​​will select an appropriate dialect phrase. For example, if the user expresses joy, the Hokkaido dialect phrase "It's so fun!" will be provided.

[1442] 3. Obtaining location information

[1443] It uses the smartphone's GPS to obtain the user's current location, so that when the user approaches a specific location, dialect phrases related to that location are provided.

[1444] 4. Treasure Hunt

[1445] The system uses geopy to calculate the user's current location and the location of the treasure, and provides hints based on the distance. For example, when the user approaches the treasure, a hint such as "Distance to treasure: 10 meters" is displayed.

[1446] Specific examples

[1447] If the user is expressing joy: "This is so fun!"

[1448] If the user is expressing sadness: "Sorry, cheer up!"

[1449] Prompt Sentence Examples

[1450] Take a user's image as input, recognize their emotion, provide Hokkaido dialect phrases, calculate the user's current location and the location of the treasure, and provide hints for the treasure hunt.

[1451] In this way, users can have a learning experience that is optimal for their emotions and current location, allowing them to learn dialects while experiencing local culture.

[1452] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1453] Step 1:

[1454] The user takes a photo of their own facial expression using the smartphone camera.

[1455] Input: User's face image

[1456] Output: Photographed face image data

[1457] Specific operation: The user activates the smartphone camera and takes a picture of their face. The captured image is saved in the smartphone.

[1458] Step 2:

[1459] The device uses OpenCV to detect faces from captured facial images.

[1460] Input: Photographed face image data

[1461] Output: Image data of the detected face area

[1462] What it does: The device uses the OpenCV library to convert the face image to grayscale, applies a face detection algorithm, crops the detected face area, and passes it on to the next process.

[1463] Step 3:

[1464] The device uses the Keras emotion recognition model to recognize the user's emotions from the detected face area.

[1465] Input: Image data of the detected face area

[1466] Output: Recognized emotion (e.g., happy, sad)

[1467] Specific operation: The device resizes the image of the face region to 48x48 pixels and inputs it into the Keras emotion recognition model. The model predicts the emotion and outputs the emotion with the highest probability.

[1468] Step 4:

[1469] The server selects the appropriate dialect phrase based on the recognized emotion.

[1470] Input: Recognized emotion

[1471] Output: Selected dialect phrases

[1472] What it does: The server consults a database of dialect phrases corresponding to emotions and selects the phrase that best matches the recognized emotion. For example, for the emotion of joy, it selects "That's so fun!"

[1473] Step 5:

[1474] The device obtains the user's current location using the smartphone's GPS function.

[1475] Input: None (GPS function activated)

[1476] Output: User's current location (latitude and longitude)

[1477] Specific operation: The device activates the GPS function and obtains the latitude and longitude information of the current location. This information is used for the following process.

[1478] Step 6:

[1479] The server provides dialect phrases related to the location based on the user's current location.

[1480] Input: User's current location (latitude and longitude)

[1481] Output: Dialect phrases related to location

[1482] What it does: The server analyzes the user's current location and retrieves dialect phrases related to that location from a database. For example, if you're in Hokkaido, it will provide "namara."

[1483] Step 7:

[1484] The server calculates the user's current location and the location of the treasure, and provides hints based on the distance.

[1485] Input: User's current location (latitude and longitude), treasure location (latitude and longitude)

[1486] Output: distance-based hints (e.g. distance to treasure)

[1487] Specific operation: The server uses the geopy library to calculate the distance between the user's current location and the treasure location, and generates a hint based on that distance. For example, it provides a hint such as "Distance to treasure: 10 meters."

[1488] Step 8:

[1489] The terminal displays the dialect phrases and hints received from the server to the user.

[1490] Input: Dialect phrase, distance-based hints

[1491] Output: Dialect phrases and hints displayed to the user

[1492] Specific operation: The terminal displays the dialect phrases and hints received from the server on the screen and provides them to the user, who can use them to proceed with the treasure hunt.

[1493] Example 2

[1494] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1495] Conventional dialect learning systems have difficulty providing a learning experience that responds to the user's emotions, making it difficult to maintain motivation. Furthermore, they have not adequately provided users with the opportunity to experience local culture through dialect learning. This has led to a demand for a system that allows users to effectively learn dialects and continue their treasure-hunting adventure while having fun.

[1496] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1497] In this invention, the server includes means for a user to become an adventurer and participate in a treasure hunt while learning the local dialect, means for an artificial intelligence to teach dialect phrases and words, means for the user to solve puzzles and find treasure using the dialect that has been taught, means for recognizing the user's emotions using an emotion recognition engine, means for adjusting the dialect phrases and words taught by the artificial intelligence in accordance with the user's emotions, and means for adjusting the difficulty of the puzzles in accordance with the user's emotions. This makes it possible to provide an optimal learning experience in accordance with the user's emotions and to provide an experience of experiencing local culture through dialect learning.

[1498] "User" refers to an individual who accesses the system and participates in a treasure hunt adventure while learning the dialect.

[1499] "Adventurer" refers to the role of a user who embarks on a treasure hunt while learning dialects within the system.

[1500] A "regional dialect" refers to words and phrases unique to a particular region.

[1501] The "treasure hunt adventure" refers to an activity in which users solve puzzles and find treasure while learning dialects within the system.

[1502] "Artificial intelligence" refers to computer programs used to teach users dialect phrases and words.

[1503] An "emotion recognition engine" refers to technology for recognizing emotions from a user's facial expressions and voice.

[1504] "Means for recognizing emotions" refers to a process of analyzing a user's emotions using an emotion recognition engine.

[1505] "Means for teaching dialect phrases and words" refers to the process of using artificial intelligence to teach dialect phrases and words to a user.

[1506] "Puzzle solving" refers to the process by which a user solves puzzles within the system using the dialect they have been taught.

[1507] "Method of finding treasure" refers to the process by which a user solves a puzzle and finds treasure within the system.

[1508] "Emotional adjustment" refers to the process of adjusting the dialect phrases, words, or difficulty of the riddles taught by the AI ​​based on the user's emotions.

[1509] This invention is a system in which users become adventurers and participate in a treasure hunt while learning local dialects. The system recognizes the user's emotions and adjusts the learning content and difficulty of puzzles according to those emotions, providing the user with an optimal learning experience.

[1510] Hardware and software used

[1511] Hardware: Servers, user devices (PCs, smartphones, tablets)

[1512] Software: Generative AI models (e.g., GPT-4), emotion recognition engines (e.g., Affectiva)

[1513] System Overview

[1514] Users access the system through an application or website using their own devices. After logging in, the user becomes an adventurer and takes part in a treasure hunt while learning the dialect. The server uses a generative AI model to teach the user dialect phrases and words. For example, it teaches phrases and words such as "namara" (naughty) and "menkoi" (lovely), which are dialects of the Hokkaido region.

[1515] Emotion Recognition and Regulation

[1516] The server uses an emotion recognition engine to recognize emotions from the user's facial expressions and voice. For example, if the user is connected to the system via a camera or microphone, the emotion recognition engine analyzes the user's emotions in real time. If the user expresses joy, the server teaches fun phrases and words that match that emotion. Conversely, if the user expresses frustration, the server lowers the difficulty of the puzzle.

[1517] Specific examples

[1518] Consider a scenario in which a user accesses the system and learns the dialect of the Hokkaido region. The server gives a hint that "It's hidden in a very cold place." If the user has learned that "namara" means "very," he can deduce that the treasure is hidden in a cold place, i.e., Hokkaido.

[1519] The emotion recognition engine recognizes the user's emotions, and if the user expresses joy, the server will teach them the fun phrase "Menkoi." Conversely, if the user expresses frustration, the server will simplify the next hint to "Try looking for a place a little further south."

[1520] Prompt Sentence Examples

[1521] An example of a prompt sentence to be input to the generative AI model is shown below.

[1522] Assuming the user is learning the Hokkaido dialect, give them a hint like, "It's hidden in a really cold place." Also, if the user is expressing joy, give them a fun phrase, such as "Menkoi."

[1523] This system allows users to have an optimal learning experience that suits their emotions, and provides an opportunity to experience local culture through dialect learning.

[1524] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1525] Step 1:

[1526] A user accesses the system

[1527] Users access the system through an application or website using their own devices (PC, smartphone, tablet). The user enters their username and password on the login screen, and if the login is successful, the main screen of the adventure is displayed. The input is the username and password, and the output is the main screen after successful login.

[1528] Step 2:

[1529] The server recognizes the user's emotions

[1530] The server uses an emotion recognition engine to recognize emotions from the user's facial expressions and voice. If the user is connected to the system via a camera or microphone, the emotion recognition engine analyzes the user's emotions in real time. The input is the user's facial expressions and voice data, and the output is the recognized emotion data.

[1531] Step 3:

[1532] The server teaches dialect phrases and words

[1533] The server uses the generative AI model to teach the user dialect phrases and words. For example, if the server teaches the Hokkaido dialect "namara," it explains that "namara" means "very." The input is the user's emotion data and learning progress data, and the output is an explanation of the dialect phrase or word.

[1534] Step 4:

[1535] Users try to solve the puzzle

[1536] The user uses the dialect they have been taught to try to solve the puzzles posed by the server. For example, the server might give a hint like, "It's hidden in a really cold place." If the user has learned that "namara" means "very," they can guess that the treasure is hidden in a cold place, i.e., Hokkaido. The input is a dialect phrase or word description and the hint to solve the puzzle, and the output is the user's guess.

[1537] Step 5:

[1538] The server adjusts the difficulty of the puzzles according to the user's emotions.

[1539] The server uses an emotion recognition engine to analyze the user's emotions again. For example, if the user expresses frustration, the server lowers the difficulty of the puzzle. Conversely, if the user expresses joy, the server teaches fun phrases or words. The input is the user's emotion data and the current difficulty of the puzzle, and the output is an adjusted difficulty level or a new phrase.

[1540] Step 6:

[1541] The user finds the treasure

[1542] The user solves the puzzle and finds the treasure. The server confirms that the user has found the treasure and suggests the next stage or learning a new dialect. The input is the user's guess and the location data of the treasure, and the output is guidance for the next stage or the content of learning a new dialect.

[1543] (Application example 2)

[1544] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1545] Conventional dialect learning systems and puzzle-solving games do not adjust the learning content or difficulty level according to the user's emotions, which can reduce the user's motivation to learn and the quality of the experience. In addition, because they were not designed for implementation as smartphone applications, the environment for easy user access was not created. This resulted in a lack of consistency in the user's learning experience, making effective learning difficult.

[1546] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1547] In this invention, the server includes: a means for a user to become an adventurer and participate in a treasure hunt while learning dialects from around Japan; a means for an AI to teach dialect phrases and words; a means for the user to solve puzzles and find treasure using the dialect that the user has been taught; a means for recognizing the user's emotions using an emotion engine and adjusting the learning content and the difficulty of the puzzles according to the emotions; and a means implemented as a smartphone application. This provides an optimal learning experience according to the user's emotions, enabling effective learning in an easily accessible environment.

[1548] A "user" is an individual who uses the system to learn dialects and attempt to solve puzzles.

[1549] An "adventurer" is a user who takes on the role of participating in a treasure hunt adventure while learning dialects within the system.

[1550] "Dialects from around Japan" refers to unique words and phrases used in specific regions of Japan.

[1551] A "treasure hunt adventure" refers to a series of activities in which users learn dialects, solve puzzles, and ultimately find treasure.

[1552] "AI" stands for artificial intelligence and refers to technology that teaches users dialect phrases and words.

[1553] "Dialect phrases and words" refer to unique linguistic expressions used in a particular region.

[1554] "Solving the puzzle" refers to the user solving a presented problem or challenge using the dialect they have been taught.

[1555] "Finding treasure" refers to the reward or achievement that the user ultimately receives by solving the mystery.

[1556] An "emotion engine" refers to technology that recognizes a user's emotions and adjusts the system's behavior accordingly.

[1557] "Learning content" refers to the dialect phrases and words that users learn through the system, as well as related information.

[1558] "Puzzle difficulty" refers to the level of difficulty of the puzzle that the user must solve.

[1559] "Smartphone application" refers to a software program that runs on a smartphone.

[1560] The system for implementing this invention allows users to become adventurers and participate in a treasure hunt while learning dialects from around Japan. The system includes the following main components:

[1561] 1. User Device

[1562] The user device is implemented as a smartphone application. Users access the system through this application and try to solve puzzles while learning dialects. The user device uses a camera and microphone to collect data to recognize the user's emotions.

[1563] 2. Server

[1564] The server runs a program that has the following functions:

[1565] Emotion engine: Recognizes the user's emotions and adjusts the learning content and difficulty of the puzzles accordingly. The emotion engine uses emotion recognition libraries such as OpenCV and DeepFace.

[1566] Dialect Teacher: Teaches users dialect phrases and words. The dialect teacher uses a pre-collected dialect database.

[1567] Riddle Generator: Generates riddles with adjusted difficulty according to the user's emotions. The riddle generator uses a generative AI model (e.g., GPT-3) to generate riddles.

[1568] 3. Data processing and calculation

[1569] The server receives emotion data sent from the user's device and analyzes it using an emotion engine. Based on the analysis results, a dialect teacher selects appropriate dialect phrases and words and provides them to the user. Furthermore, a puzzle generator generates puzzles of a difficulty level corresponding to the user's emotion and presents them to the user.

[1570] For example, if a user smiles, the emotion engine recognizes this as "joy." The dialect teacher teaches the Hokkaido dialect "menkoi" (cute) to match the emotion of "joy." The puzzle generator then generates a relatively easy puzzle to match the emotion of "joy."

[1571] An example of a prompt is as follows:

[1572] When the user smiles, the emotion engine recognizes it as "joy" and the dialect teacher teaches the Hokkaido dialect "menkoi (cute)." The puzzle generator then generates a relatively easy puzzle to match the emotion of "joy."

[1573] In this way, users can have an optimal learning experience according to their emotions.

[1574] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1575] Step 1:

[1576] The user terminal launches the application and collects the user's facial image and voice data.

[1577] Input: User's facial image and voice data

[1578] Output: Collected facial images and audio data

[1579] Specific operation: Uses the camera and microphone on the user's device to capture the user's facial expressions and voice in real time.

[1580] Step 2:

[1581] The user terminal transmits the collected facial images and voice data to the server.

[1582] Input: Collected facial images and audio data

[1583] Output: Facial images and audio data sent to the server

[1584] Specific operation: The user terminal uploads the collected data to the server via the Internet.

[1585] Step 3:

[1586] The server uses an emotion engine to recognize the user's emotions from the received facial images and voice data.

[1587] Input: face images and audio data

[1588] Output: Recognized user emotion (e.g., happy, sad, surprised, etc.)

[1589] Specific operation: The server uses emotion recognition libraries such as OpenCV and DeepFace to analyze facial expressions and tone of voice to identify the user's emotions.

[1590] Step 4:

[1591] Based on the emotions recognized by the server, a dialect teacher selects appropriate dialect phrases and words.

[1592] Input: Perceived user emotion

[1593] Output: Selected dialect phrases and words

[1594] Specific operation: The server searches and selects dialect phrases and words from a dialect database that match the user's emotions.

[1595] Step 5:

[1596] The server sends the selected dialect phrases and words to the user's terminal.

[1597] Input: Selected dialect phrases and words

[1598] Output: Dialect phrases and words sent to the user's device

[1599] Specific operation: The server sends selected dialect phrases and words to the user's device via the Internet.

[1600] Step 6:

[1601] The user terminal displays the received dialect phrases and words to the user.

[1602] Input: Received dialect phrase or word

[1603] Output: Dialect phrases or words displayed to the user

[1604] Specific operation: The received dialect phrases and words are displayed on the screen of the user's device, allowing the user to learn them.

[1605] Step 7:

[1606] Based on the emotion recognized by the server, a puzzle generator generates a puzzle of appropriate difficulty.

[1607] Input: Perceived user emotion

[1608] Output: Generated puzzle

[1609] Specific operation: The server uses a generative AI model (e.g., GPT-3) to generate puzzles with difficulty levels that correspond to the user's emotions.

[1610] Step 8:

[1611] The server sends the generated puzzle to the user's terminal.

[1612] Input: Generated puzzle

[1613] Output: The mystery sent to the user's terminal

[1614] Specific operation: The server sends the generated puzzle to the user's terminal via the Internet.

[1615] Step 9:

[1616] The user terminal displays the received riddle to the user, who then solves the riddle.

[1617] Input: riddle received

[1618] Output: The answer entered by the user

[1619] Specific operation: The received riddle is displayed on the screen of the user's terminal, and an interface is provided for the user to input the answer.

[1620] Step 10:

[1621] The server receives the user's answer and determines whether it is correct.

[1622] Input: The answer entered by the user

[1623] Output: Judgment result (correct / incorrect)

[1624] Specific operation: The server analyzes the user's answer, determines whether it is correct, and sends the result to the user's terminal.

[1625] Step 11:

[1626] The user terminal displays the determination result to the user.

[1627] Input: Judgment result

[1628] Output: The result displayed to the user

[1629] Specific operation: The judgment result is displayed on the screen of the user's device and notified to the user.

[1630] Example 3

[1631] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1632] Conventional dialect learning systems do not adjust the learning content according to the user's emotions, which can reduce the learning effect. It is also difficult to provide a learning experience based on the local culture, limiting the opportunities for users to deeply experience the local culture. Furthermore, users can become discouraged during the learning process, making it difficult to continue learning.

[1633] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1634] In this invention, the server includes a means for a user to become an adventurer and participate in a treasure hunt while learning the local dialect, a means for an artificial intelligence to teach dialect phrases and words, a means for the user to solve puzzles and find treasure using the dialect that has been taught to them, a means for adjusting the dialect phrases and words in accordance with the user's emotions using an emotion engine that recognizes the user's emotions, and a means for adjusting the difficulty of the puzzles in accordance with the user's emotions. This provides an optimal learning experience in accordance with the user's emotions, enabling the user to deeply experience the local culture. It also allows the user to continue learning without giving up.

[1635] "User" refers to an individual who uses the system to learn dialects and participate in a treasure hunt adventure.

[1636] "Adventurer" refers to the role of a user who embarks on a treasure hunt while learning dialects within the system.

[1637] A "regional dialect" refers to words and phrases unique to a particular region.

[1638] The "treasure hunt adventure" refers to an activity in which users solve puzzles within the system and find treasure while learning the dialect.

[1639] "Artificial intelligence" refers to the machine learning models and algorithms used to teach users dialect phrases and words.

[1640] "Dialect phrases and words" refer to words and expressions unique to a particular region.

[1641] "Solving the puzzle" refers to the user using the dialect they have been taught to solve problems and challenges within the system.

[1642] An "emotion engine" refers to software or algorithms that recognize a user's emotions and adjust the system's behavior accordingly.

[1643] "Adjusting dialect phrases and words according to emotions" refers to changing the content of the dialect to be taught based on the user's emotional state.

[1644] "Adjusting the difficulty of puzzles according to emotions" refers to changing the difficulty of puzzles in the system based on the user's emotional state.

[1645] The present invention is a system in which users become adventurers and participate in a treasure hunt while learning local dialects. The system has the function of recognizing the user's emotions and adjusting the learning content and difficulty of puzzles according to those emotions.

[1646] Hardware and software used

[1647] Hardware: Servers, user devices (PCs, smartphones, tablets)

[1648] Software: Emotion engine, generative AI model, web application, database

[1649] Specific operation of the system

[1650] User Access

[1651] A user accesses an application or website using a device such as a PC or smartphone. The device receives the user's input and sends a request to the server. The server receives the request and starts a user session.

[1652] Emotion recognition

[1653] The server uses an emotion engine to analyze the user's facial expressions and voice data. For example, the emotion engine can use Microsoft Azure's Emotion API. The device uses the user's camera and microphone to collect facial expressions and voice data and send it to the server. The server receives the analysis results of the emotion engine and identifies the user's emotional state. For example, if the user is smiling, it will recognize this as "joy."

[1654] Dialect phrase generation

[1655] The server sends a prompt to a generative AI model (e.g., OpenAI's GPT-3) to generate a dialect phrase that corresponds to the user's emotion. An example of a prompt is, "If the user expresses joy, generate a fun phrase in the Hokkaido dialect." The generative AI model generates an appropriate dialect phrase based on the prompt and returns it to the server. The server receives the generated phrase and prepares it to be provided to the user.

[1656] Providing learning content

[1657] The server sends the generated dialect phrases to the user's device. The device displays the received phrases and allows the user to learn them. For example, phrases such as "It's so fun!" and "It's so cute!" are displayed. The user learns the displayed phrases and uses them within the application.

[1658] Mystery solving and treasure hunting

[1659] The user solves puzzles in the application using the dialect phrases they have learned. The server then uses the emotion engine again to check the user's emotional state. For example, if the user is confused, it will recognize this as "frustrated." The server then adjusts the difficulty of the puzzles according to the user's emotions. For example, if the user shows frustration, it will lower the difficulty of the puzzles. The user solves the puzzles of the adjusted difficulty and finds the treasure.

[1660] Specific examples

[1661] Suppose a user wants to learn the Hokkaido dialect. When the user accesses the application and the emotion engine recognizes the emotion of joy from the user's facial expression, the generative AI model will provide phrases such as "It's so fun!" or "It's so cute!". The user can then use these phrases to solve puzzles within the app and find treasure.

[1662] In this way, the user can have an optimal learning experience that suits their emotions while experiencing the local culture. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1663] Step 1:

[1664] User Access

[1665] A user accesses an application or website using a device such as a PC or smartphone. The device receives the user's input and sends a request to the server. The server receives the request and starts a user session.

[1666] Input: User access request

[1667] Output: User session started

[1668] Specific behavior: The device displays a dialog asking for permission, such as "Do you want to use the camera to recognize your facial expressions?"

[1669] Step 2:

[1670] Emotion recognition

[1671] The server uses the emotion engine to analyze the user's facial expression and voice data. The device uses the user's camera and microphone to collect facial expression and voice data and transmits it to the server. The server receives the analysis results of the emotion engine and identifies the user's emotional state.

[1672] Input: User facial and voice data

[1673] Output: User's emotional state (e.g., joy, frustration)

[1674] What happens: The device displays a message such as "Analyzing emotions..."

[1675] Step 3:

[1676] Dialect phrase generation

[1677] The server sends a prompt to the generative AI model, which generates a dialect phrase based on the user's emotion. The generative AI model generates an appropriate dialect phrase based on the prompt and returns it to the server.

[1678] Input: User's emotional state, prompt

[1679] Output: Generated dialect phrases

[1680] Specific operation: The server sends a prompt to the generative AI model, such as, "When the user expresses the emotion of joy, generate a fun phrase in the Hokkaido dialect."

[1681] Step 4:

[1682] Providing learning content

[1683] The server sends the generated dialect phrases to the user's device, which displays the received phrases and allows the user to learn them.

[1684] Input: Generated dialect phrase

[1685] Output: Dialect phrase displayed to the user

[1686] What it does: The device displays phrases like "So much fun!" or "So cute!"

[1687] Step 5:

[1688] Mystery solving and treasure hunting

[1689] The user solves puzzles in the application using the dialect phrases they have learned. The server then uses the emotion engine again to check the user's emotional state. The server adjusts the difficulty of the puzzles according to the user's emotions.

[1690] Input: User's emotional state, learned dialect phrases

[1691] Output: Puzzle with adjusted difficulty

[1692] What it does: The device displays feedback such as "Current difficulty: Medium, Emotion: Frustrated, Lowering the difficulty."

[1693] (Application example 3)

[1694] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1695] Conventional dialect learning systems do not adjust the learning content or difficulty level according to the user's emotions, and the learning experience is uniform, which can lower user motivation. It is also difficult for users to experience local culture through learning dialects based on that culture. Furthermore, there is a lack of means to provide an interactive learning experience using smartphones.

[1696] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1697] In this invention, the server includes: a means for a user to become an adventurer and participate in a treasure hunt while learning dialects from around Japan; a means for an AI to teach dialect phrases and words; a means for the user to solve puzzles and find treasure using the dialect that the AI ​​has taught them; a means for recognizing the user's emotions using an emotion engine and adjusting the learning content and difficulty level according to the emotions; a means including an application to be installed on a smartphone; a means for providing dialect phrases using a generative AI model; and a means for generating prompt sentences. This allows the user to have an optimal learning experience according to their emotions and experience experiencing local culture.

[1698] "User" refers to an individual who uses the system to enjoy the adventure of learning dialects.

[1699] An "adventurer" is a user whose role is to participate in a treasure hunt adventure while learning dialects within the system.

[1700] A "dialect" refers to unique words and phrases used in a particular region.

[1701] "Treasure Hunt Adventure" refers to a series of activities in which users solve puzzles and find treasure while learning the dialect.

[1702] "AI" refers to artificial intelligence, and has the ability to teach users dialect phrases and words.

[1703] An "emotion engine" refers to a system that has the ability to recognize the user's emotions and adjust the learning content and difficulty level according to those emotions.

[1704] A "smartphone" refers to a portable information terminal that can connect to the Internet and install applications.

[1705] The "application" refers to software that is installed on smartphones and allows users to enjoy an adventure while learning dialects.

[1706] "Generative AI model" refers to an artificial intelligence model for generating dialect phrases to be provided to users.

[1707] A "prompt sentence" refers to text data that is input into a generative AI model and serves to prompt the generation of dialect phrases.

[1708] The system for implementing this invention allows users to become adventurers and participate in a treasure hunt while learning dialects from around Japan. The system includes the following main components:

[1709] 1. System Program

[1710] The system program is implemented using the following hardware and software.

[1711] Hardware: Smartphone

[1712] Software: EmotionEngine (emotion recognition engine), DialectDatabase (dialect database), generative AI model

[1713] 2. Program Processing

[1714] The user's adventure begins

[1715] The user launches the application on their smartphone and selects the region they want to start their adventure in. The server retrieves the dialect data for the selected region from the DialectDatabase and provides it to the user.

[1716] Dialect learning

[1717] The server uses the generative AI model to generate dialect phrases and provides them to the user, who then learns the dialect phrases on their smartphone screen.

[1718] Emotional awareness and regulation

[1719] The Emotion Engine recognizes the user's emotions and adjusts the learning content and difficulty level accordingly. For example, if the user expresses joy, the server will provide fun dialect phrases. Conversely, if the user expresses frustration, the server will provide easy dialect phrases.

[1720] Mystery solving and treasure hunting

[1721] The user solves puzzles using the dialect they have learned and finds treasure. The server adjusts the difficulty of the puzzles according to the user's emotions. For example, if the user expresses frustration, the server provides an easy puzzle.

[1722] 3. Specific Examples

[1723] For example, if a user selects an adventure in the Hokkaido region, the server retrieves Hokkaido dialect words such as "namara" (naughty) and "menkoi" (cute) from the DialectDatabase and provides them to the user using a generative AI model. If the user expresses joy, the server displays "I'll teach you some fun phrases!" and provides fun dialect phrases. Conversely, if the user expresses frustration, the server displays "I'll teach you some simple phrases," and provides simple dialect phrases.

[1724] Prompt Sentence Examples

[1725] "If a user who wants to learn the Hokkaido dialect expresses joy, please tell us some fun dialect phrases."

[1726] In this way, users can experience the local culture and get the most emotionally appropriate learning experience.

[1727] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1728] Step 1:

[1729] The user starts the application using a smartphone and selects the region where the adventure begins.

[1730] Input: User's region selection

[1731] Output: Selected region information

[1732] Specific behavior: The user selects Hokkaido on the application's region selection screen.

[1733] Step 2:

[1734] The server retrieves dialect data for the selected region from the DialectDatabase and inputs it into the generative AI model.

[1735] Input: Selected region information

[1736] Output: Regional dialect data

[1737] Specific operation: The server retrieves Hokkaido dialect data (e.g., "namara" and "menkoi") from the DialectDatabase.

[1738] Step 3:

[1739] The generative AI model generates dialect phrases, which the server provides to the user.

[1740] Input: Regional dialect data

[1741] Output: Dialect phrase

[1742] Specific operation: The generative AI model generates the phrase "It's so fun!", and the server sends it to the user's smartphone.

[1743] Step 4:

[1744] The EmotionEngine recognizes the user's emotions and sends the emotional data to the server.

[1745] Input: User facial and voice data

[1746] Output: Emotion data

[1747] Specific operation: The user expresses their emotions through the smartphone's camera or microphone, and the EmotionEngine recognizes this as "joy."

[1748] Step 5:

[1749] The server adjusts the learning content and difficulty level based on the emotional data and re-inputs it into the generative AI model.

[1750] Input: Emotion data

[1751] Output: Adjusted learning content and difficulty

[1752] Specific operation: The server receives the emotion data of "joy" and inputs the prompt sentence "generate a happy phrase" into the generative AI model.

[1753] Step 6:

[1754] The generative AI model generates tailored dialect phrases, which the server provides to the user.

[1755] Input: Adjusted learning content and difficulty

[1756] Output: adjusted dialect phrase

[1757] Specific operation: The generative AI model generates the phrase "It's so fun!", and the server sends it to the user's smartphone.

[1758] Step 7:

[1759] Users solve puzzles and find treasure using the dialect they have learned, and the server adjusts the difficulty of the puzzles according to the user's emotions.

[1760] Input: User emotion data, learned dialect phrases

[1761] Output: Adjusted puzzle difficulty, treasure location information

[1762] Specific operation: The user solves the puzzle using the phrase "It's so much fun!", and the server provides a simple puzzle and sends the location information of the treasure to the user.

[1763] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1764] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1765] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1766] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1767] [Third embodiment]

[1768] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1769] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1770] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1771] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1772] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1773] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1774] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1775] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1776] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1777] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1778] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1779] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1780] "Example 1"

[1781] One aspect of the present invention is a system in which users become adventurers and participate in a treasure hunt while learning dialects from around Japan. Specifically, users access the system through an application or website and become adventurers themselves. The system uses AI to teach users dialect phrases and words. For example, it can teach phrases and words such as "namara" (naughty) and "menkoi" (lovely), which are dialects of the Hokkaido region.

[1782] "Example 2"

[1783] For example, if the AI ​​gives a hint to solve a mystery like "It's hidden in a really cold place," and the user knows that "namara" means "very" in the Hokkaido dialect, they can guess that the treasure is hidden in a cold place, in other words, Hokkaido.

[1784] "Example 3"

[1785] The system also includes content based on local culture. For example, the dialect phrases and words taught by the AI ​​are selected based on the customs, habits, and history of the region. This allows users to experience local culture through learning the dialect.

[1786] The processing flow of each embodiment will be described below.

[1787] "Example 1"

[1788] Step 1: The user accesses the system through an application or website and becomes an adventurer.

[1789] Step 2: The system uses AI to teach the user dialect phrases and words, such as "namara" (meaning "namara") and "menkoi" (meaning "lovely"), which are used in the Hokkaido dialect.

[1790] Step 3: The user solves the mystery using the dialect they were taught and finds the treasure. For example, if the AI ​​gives the hint to solve the mystery as "It's hidden in a really cold place," the user can guess that the treasure is hidden in a cold place, i.e., Hokkaido, if they have learned that "namara" means "very" in the Hokkaido dialect.

[1791] "Example 2"

[1792] Step 1: The system includes content based on local culture. For example, the dialect phrases and words that the AI ​​teaches are selected based on the customs, habits, and history of the region.

[1793] Step 2: Users can gain experience in contact with local culture through learning dialects. For example, the AI ​​teaches that "namara" is a dialect of the Hokkaido region, and users can learn about the climate and culture of Hokkaido from its usage and background.

[1794] Example 1

[1795] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1796] Conventional language learning systems have had the problem of making it difficult for users to enjoy learning local languages ​​and cultures. Furthermore, they lacked the functionality to manage learning progress, making it impossible to maximize the effectiveness of users' learning. Furthermore, there were no systems that allowed users to actually use the language they had learned to solve puzzles.

[1797] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1798] In this invention, the server includes means for receiving user information and managing the progress of the adventure, means for retrieving language data of the selected region from a database, and means for verifying the user's input and proceeding to the next stage if correct. This allows the user to effectively manage their learning progress while having fun learning the language and culture of the region, and to have the experience of solving puzzles using the language they have learned.

[1799] A "user" is an individual who accesses the system, registers as an adventurer, and participates in a treasure hunting adventure while learning the local language.

[1800] An "adventurer" is a user whose role in the system is to participate in a treasure hunting adventure while learning the local language.

[1801] "Regional languages" refers to dialects, unique phrases, and words from all over Japan.

[1802] The "treasure hunt adventure" is an activity in which users learn the local language, solve puzzles provided within the system, and ultimately find treasure.

[1803] "Artificial Intelligence" is a technology used to teach users phrases and words in their local language.

[1804] "Language data" is data that includes information about phrases and words related to a local language.

[1805] A "server" is a computer system responsible for receiving user information, managing the progress of the adventure, retrieving language data for the selected region from a database, and validating user input.

[1806] A "terminal" is a device through which a user accesses the system, displays language data, and receives user input.

[1807] A "database" is an information management system that stores regional language data and allows the server to retrieve it as needed.

[1808] "Progress" is information that indicates the stage of the user's adventure.

[1809] "Verification" is the process of checking whether the language phrases or words entered by the user are correct.

[1810] A "stage" refers to each stage in the progression of the adventure, and the user must meet certain conditions to proceed to the next stage.

[1811] This invention is a system in which users become adventurers and participate in a treasure hunt while learning the local language. The system operates in cooperation with three parties: a server, a terminal, and a user.

[1812] Server Roles

[1813] The server receives the user's information and manages the progress of the adventure. Based on the region selected by the user, the server retrieves language data for that region from the database. For example, if the user selects the Hokkaido region, the server retrieves language data such as "namara" (naughty) and "menkoi" (cute). The server sends the retrieved language data to the user's device and verifies the user's input. If the user enters the correct language, they are allowed to proceed to the next stage.

[1814] Device Role

[1815] A terminal is a device through which a user accesses the system. The terminal receives language data sent from the server and displays it to the user. The terminal provides an interface for the user to learn the language and displays input fields for the user to enter phrases or words in the language. For example, the terminal may display the word "namara" and its meaning and provide a field for the user to enter "namara."

[1816] User Roles

[1817] A user accesses an application or website and registers as an adventurer. The user learns phrases and words in the language presented by the system and enters them into the input field. If the user enters the language correctly, the system allows the user to proceed to the next stage. For example, if the user enters "namara" correctly, they can proceed to the next stage.

[1818] Specific examples

[1819] If the user selects the Hokkaido region, the server acquires language data such as "namara" (naughty) and "menkoi" (cute) and sends it to the terminal. The terminal displays these words to the user, and if the user inputs "namara" correctly, they can proceed to the next stage.

[1820] Prompt Sentence Examples

[1821] "I want to learn the language of the Hokkaido region. Could you please tell me the meaning of the word 'namara'?"

[1822] This system is designed to allow users to enjoy a treasure hunt adventure while learning the local language. The server, terminals, and users work together to maximize the learning effect of users and provide an experience of experiencing the local culture.

[1823] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1824] Step 1:

[1825] A user accesses an application or website.

[1826] Input: User login information (ID and password)

[1827] Output: User authentication result

[1828] Specific operation: The user opens the app on their smartphone or accesses the website in their PC browser, and the login screen appears. The user enters their ID and password and presses the login button. The server receives the entered information and verifies it against the database. If authentication is successful, the user can proceed to the next screen.

[1829] Step 2:

[1830] The server receives user information and manages the progress of the adventure.

[1831] Input: User ID

[1832] Output: User's adventure progress

[1833] Specific operation: The server receives the user's ID and retrieves the user's adventure progress from the database. For example, it checks which region the user selected in the previous session and how far they progressed. The retrieved information is sent to the user's device.

[1834] Step 3:

[1835] The user selects the region for the adventure.

[1836] Input: User's selected region

[1837] Output: Information about the selected region

[1838] Specific operation: The user selects the region they want to explore from the map or list on the screen. For example, they select the Hokkaido region. After selecting, they proceed to the next screen. The server receives the user's selection and retrieves information about the selected region.

[1839] Step 4:

[1840] The server retrieves language data for the selected region from the database.

[1841] Input: Selected region information

[1842] Output: Regional language data

[1843] Specific operation: Based on the region selected by the user, the server retrieves the language data for that region from the database. For example, it retrieves "namara" (meaning "naughty") and "menkoi" (meaning "lovely") as language data for the Hokkaido region. The retrieved data is sent to the user's device.

[1844] Step 5:

[1845] The server transmits the language data to the terminal.

[1846] Input: Regional language data

[1847] Output: Language data sent to the device

[1848] Specific operation: The server sends the acquired language data to the user's device. The data is sent in JSON format, etc. The device analyzes the received data and prepares to display it to the user.

[1849] Step 6:

[1850] The terminal displays the language data to the user.

[1851] Input: Language data sent from the server

[1852] Output: Language data displayed to the user

[1853] Specific operation: The device analyzes the received language data and displays it to the user. For example, the word "namara" and its meaning are displayed. The user can check the meaning and usage of the word on the screen.

[1854] Step 7:

[1855] The user enters a phrase or word in the language.

[1856] Input: Phrases and words in the user's language

[1857] Output: User input data

[1858] Specific operation: The user learns a phrase or word in the displayed language and enters it into the input field. For example, they enter "namara." After entering, they press the send button. The device sends the user's input data to the server.

[1859] Step 8:

[1860] The server validates the user's input and if correct proceeds to the next stage.

[1861] Input: User-entered data

[1862] Output: Verification results and next stage information

[1863] Specific operation: The server receives the user's input and verifies whether it is correct. For example, it checks whether "namara" is entered correctly. If it is correct, it allows the user to proceed to the next stage, obtains new language data, and sends it to the device.

[1864] (Application example 1)

[1865] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1866] Conventional dialect learning systems often provide users with a monotonous learning experience, making it difficult to maintain motivation to learn. Furthermore, there are few opportunities to come into contact with local culture, and dialect learning often ends up being merely language learning. Furthermore, there are limited opportunities for users to actually use the dialect they have learned, making it difficult to obtain practical learning benefits.

[1867] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1868] In this invention, the server includes: a means for a user to become an adventurer and participate in a treasure hunt while learning dialects from around Japan; a means for an AI to teach dialect phrases and words; a means for the user to solve puzzles and find treasure using the dialect that the AI ​​has taught them; a means including an application to be installed on a smartphone; a means for randomly selecting dialect phrases and words and presenting them to the user; a means for randomly determining the success or failure of the mission and proceeding to the next location; and a means for randomly determining whether the treasure is found. This allows the user to have a fun time learning dialects while experiencing local culture and achieving practical learning benefits.

[1869] "User" refers to an individual who uses the system to participate in the dialect learning and treasure hunting adventure.

[1870] An "adventurer" is a user whose role is to learn dialects and go on a treasure hunt within the system.

[1871] "Dialect" refers to regional words and phrases used throughout Japan.

[1872] The "treasure hunt" refers to a virtual adventure that users go through while learning dialects within the system.

[1873] "AI" refers to a system that uses artificial intelligence technology to teach users dialect phrases and words.

[1874] A "phrase" is a combination of words that has a particular meaning.

[1875] "Language" refers to words or expressions that have a particular meaning.

[1876] "Solving the puzzle" refers to the user using the dialect they have learned to solve problems and challenges within the system.

[1877] A "smartphone" refers to a portable information terminal that can connect to the Internet and install applications.

[1878] The "application" refers to software that is installed on smartphones and allows users to learn dialects and engage in treasure hunts.

[1879] "Random" means that something is chosen at random, without following any particular rules or order.

[1880] "Mission" refers to the task or goal that a user must achieve within the system.

[1881] "Determining success or failure" refers to the system determining whether the user has accomplished the mission.

[1882] "Proceeding to the next location" refers to the user moving to a new area or stage after completing the current mission.

[1883] "Finding treasure" refers to the user discovering the treasure, which is the ultimate goal, within the system.

[1884] This invention is a system in which users become adventurers and participate in a treasure hunt while learning dialects from around Japan. The system is provided through an application installed on a smartphone.

[1885] System configuration

[1886] The system consists of the following main components:

[1887] 1. User device: Smartphone

[1888] 2. Server: A server that manages dialect data and mission data

[1889] 3. AI Model: Generative AI model to teach dialect phrases and words

[1890] Program processing

[1891] User terminal

[1892] The user device provides an interface through an application installed on a smartphone, allowing users to participate in a treasure hunt adventure while learning the dialect. Users start the application and register as adventurers.

[1893] server

[1894] The server manages dialect data and mission data and provides them to user devices. The dialect data is stored in JSON format and includes dialect phrases and their meanings for each region. The mission data includes tasks and goals that users must complete.

[1895] AI model

[1896] The AI ​​model is responsible for teaching users dialect phrases and words. Using the generative AI model, it randomly selects dialect phrases for the user's current location and presents them to the user.

[1897] Data processing and calculation

[1898] 1. Reading dialect data: The server reads the dialect data in JSON format and provides it to the user terminal.

[1899] 2. Dialect Teaching: The AI ​​model randomly selects dialect phrases based on the user's current location and presents them to the user.

[1900] 3. Mission Judgment: The server randomly judges whether the user has completed the mission and decides whether to proceed to the next location.

[1901] 4. Treasure Hunt Judgment: The server randomly judges whether the user finds the treasure or not.

[1902] Specific examples

[1903] The user launches the application and learns the Hokkaido dialect "namara." Then, they complete a mission to move on to the next location (say, Okinawa) and learn the Okinawa dialect "churasan." Finally, they repeat this process until they find the treasure.

[1904] Prompt Sentence Examples

[1905] "Please tell me the Hokkaido dialect."

[1906] "Starting the next mission."

[1907] "Have you found any treasure?"

[1908] In this way, users can enjoy a virtual adventure while learning dialects from all over Japan.

[1909] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1910] Step 1:

[1911] The user launches the application on their smartphone.

[1912] Input: User operation (application launch)

[1913] Output: Application home screen

[1914] Specific behavior: The user taps the application icon, the application launches, the home screen is displayed, and the user selects the option to register as an adventurer.

[1915] Step 2:

[1916] The user registers as an adventurer.

[1917] Input: User registration information (name, email address, etc.)

[1918] Output: User registration completion message

[1919] Specific operation: The user enters their name and email address and presses the registration button. The server receives the registration information and stores it in the database. A message that registration is complete is displayed to the user.

[1920] Step 3:

[1921] The server reads the dialect data and provides it to the user terminal.

[1922] Input: Dialect data (JSON format)

[1923] Output: Sending dialect data to the user's terminal

[1924] Specific operation: The server reads the dialect data in JSON format, selects the appropriate dialect data based on the user's current location, and sends the selected dialect data to the user's device.

[1925] Step 4:

[1926] The AI ​​model teaches users dialect phrases and words.

[1927] Input: User's current location, dialect data

[1928] Output: Dialect phrases and their meanings

[1929] How it works: The AI ​​model randomly selects a dialect phrase based on the user's current location and presents it to the user along with its meaning. The user can then view the dialect phrase and its meaning on the screen.

[1930] Step 5:

[1931] The user starts the mission.

[1932] Input: User operation (pressing the mission start button)

[1933] Output: Display mission details

[1934] Specific operation: When the user presses the mission start button, the server loads the mission data and presents it to the user. The mission details are displayed on the screen.

[1935] Step 6:

[1936] The server determines whether the mission is successful or not.

[1937] Input: User's mission progress

[1938] Output: Mission success / failure result

[1939] Specific operation: The user progresses through the mission and sends the results to the server. The server randomly determines whether the mission was successful or not and notifies the user of the result.

[1940] Step 7:

[1941] The user proceeds to the next location.

[1942] Input: Mission success result

[1943] Output: Dialect data for the following locations

[1944] Specific operation: If the mission is successful, the server randomly selects the next location and sends the dialect data of that location to the user's device. The user then proceeds to the next location.

[1945] Step 8:

[1946] The server determines whether the treasure is found.

[1947] Input: User's mission progress

[1948] Output: Judgment result of whether or not treasure was found

[1949] Specific operation: After the user completes multiple missions, the server randomly determines whether or not the treasure is found. The result is notified to the user, and if the treasure is found, the adventure ends.

[1950] Example 2

[1951] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1952] In conventional treasure hunt games, users have limited opportunities to learn about local dialects and cultures, making it difficult to combine learning with entertainment. Furthermore, when users input their answers, they lack a system that instantly determines whether their answers are correct and provides feedback to guide them to the next step.

[1953] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1954] In this invention, the server includes a means for a user to become an adventurer and participate in a treasure hunt adventure while learning the local dialect, a means for teaching dialect phrases and words using a generative AI model, a means for the user to solve puzzles and find treasure using the dialect they have been taught, a means for analyzing the user's answer and determining whether it is correct, and a means for providing the next hint or information on the location of the treasure if the answer is correct. This allows the user to enjoy treasure hunting as entertainment while learning about the local dialect and culture.

[1955] "User" refers to an individual who uses the System to participate in a treasure hunting adventure.

[1956] An "adventurer" is a user who takes part in a treasure hunt and solves mysteries while learning the dialect.

[1957] A "regional dialect" refers to words and phrases unique to a particular region.

[1958] "Generative AI model" refers to a model that generates text using artificial intelligence technology.

[1959] "Means of teaching phrases and words" refers to methods that use generative AI models to provide dialect phrases and words to users.

[1960] "Means for solving the puzzle" refers to the method by which the user solves the presented puzzle using the dialect that they have been taught.

[1961] "Means for analyzing the answer" refers to a method for analyzing the answer entered by the user and determining whether it is correct or not.

[1962] The "means for determining whether the answer is correct" refers to a method for determining whether the user's answer is correct.

[1963] "Means for providing information about the next hint or treasure location" refers to a method for providing information to the user to proceed to the next step if the user answers correctly.

[1964] "System" refers to a device or program that includes a set of means for users to enjoy a treasure hunt adventure while learning a dialect.

[1965] This invention is a system that allows users to enjoy a treasure hunt adventure while learning local dialects. The system is realized using a server, terminals, and a generative AI model.

[1966] The server uses a generative AI model to generate dialect phrases and words. Specifically, it uses a general artificial intelligence technology (e.g., GPT-4) as the generative AI model. The server inputs a prompt sentence into the generative AI model and generates hints for solving the puzzle that include dialect. For example, the following prompt sentence is used:

[1967] "Generate the following hint for the user: 'It's hidden in a really cold place.' The aim of this hint is to help the user learn the Hokkaido dialect word 'namara.'"

[1968] The generated hint is sent from the server to the user's device. The device displays the received hint to the user. The user reads the hint through the device and solves the mystery while learning the meaning of the dialect. For example, if the user receives the hint "It's hidden in a very cold place," the user will learn that "namara" means "very" in the Hokkaido dialect and will infer that the treasure is hidden in a cold place, that is, Hokkaido.

[1969] When the user enters the answer into the device, the device sends the answer to the server. The server analyzes the user's answer and determines whether it is correct. The analysis uses string comparison and natural language processing techniques. If the answer is correct, the server sends the next hint and information about the location of the treasure to the device. The device displays the received information to the user and proceeds to the next step.

[1970] This system allows users to enjoy treasure hunting as entertainment while learning about local dialects and culture. For example, a user receives the hint "It's hidden in a very cold place," learns that "namara" means "very," and infers that the treasure is hidden in Hokkaido.

[1971] In this way, by combining the server, terminal, and generative AI model, we have created a system that allows users to enjoy a treasure hunt adventure while learning dialects.

[1972] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1973] Step 1:

[1974] A user accesses the system.

[1975] A user accesses the system using a web browser or a dedicated app. The device receives the user's access request and sends it to the server. The server verifies the user's authentication information and grants access. The input is the user's authentication information, and the output is the access permission status.

[1976] Step 2:

[1977] The server uses a generative AI model to generate clues to solve the puzzle.

[1978] The server inputs a prompt into the generative AI model and generates a hint for solving the puzzle, including the dialect. An example of a specific prompt is: "Generate the following hint for the user: 'It's hidden in a really cold place.' The aim of this hint is for the user to learn the Hokkaido dialect 'namara.'" The input is the prompt, and the output is the generated hint.

[1979] Step 3:

[1980] The server sends the generated hint to the terminal.

[1981] The server sends the generated hint to the user's terminal, and the terminal displays the received hint to the user. The input is the generated hint, and the output is the hint displayed to the user.

[1982] Step 4:

[1983] Users receive hints and solve puzzles while learning the meaning of dialect words.

[1984] The user reads the hint displayed on the terminal and checks the dialect meaning. For example, if the user receives the hint "It's hidden in a very cold place," the user learns that "namara" means "very" in the Hokkaido dialect. The input is the displayed hint, and the output is the user's understanding.

[1985] Step 5:

[1986] The user enters the answer into the terminal.

[1987] The user inputs the answer to the puzzle into the terminal. For example, they might input "The treasure is hidden in Hokkaido." The terminal then sends the user's answer to the server. The input is the user's answer, and the output is the answer sent to the server.

[1988] Step 6:

[1989] The server analyzes the user's answer and determines whether it is correct.

[1990] The server receives the user's input and analyzes it to determine whether it is correct. The analysis uses string comparison and natural language processing techniques. The input is the user's answer, and the output is the result of the judgment as to whether it is correct or not.

[1991] Step 7:

[1992] The server sends the results to the device, providing information about the next hint and treasure location.

[1993] The server sends the analysis results to the terminal, which displays the results to the user and, if the answer is correct, provides the next hint and information about the location of the treasure. The input is the analysis results, and the output is the result displayed to the user and the next hint.

[1994] (Application example 2)

[1995] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1996] Opportunities to learn about regional cultures and dialects are declining in modern education and entertainment. Furthermore, the number of educational materials and applications for learning dialects is limited, making learning monotonous. Furthermore, the lack of interactive experiences that allow users to have fun while learning dialects makes it difficult to keep users engaged.

[1997] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1998] In this invention, the server includes: a means for a user to become an adventurer and participate in a treasure hunt while learning dialects from around Japan; a means for an AI to teach dialect phrases and words; a means for the user to solve puzzles and find treasure using the dialect that the user has learned; a means for generating puzzles that include dialect using a generative AI model; and a means for providing an application to be installed on a smartphone. This allows users to enjoy an interactive puzzle game while learning dialects, providing an experience of experiencing local culture.

[1999] A "user" is an individual who uses the system to learn dialects and participate in a puzzle-solving game.

[2000] An "adventurer" is a user whose role is to participate in a treasure hunt adventure while learning dialects within the system.

[2001] "Dialect" refers to regional words and phrases used throughout Japan.

[2002] A "treasure hunt adventure" refers to a series of activities in which users learn dialects, solve puzzles, and ultimately find treasure.

[2003] "AI" refers to artificial intelligence, which is responsible for teaching users dialect phrases and words.

[2004] A "phrase" is a combination of words that has a particular meaning.

[2005] "Language" refers to words or expressions that have a particular meaning.

[2006] "Solving a mystery" refers to the act of understanding the given hints and questions and deriving the correct answer.

[2007] "Finding treasure" refers to solving a mystery and ultimately discovering a desired location or object.

[2008] A "generative AI model" refers to a system that uses artificial intelligence to automatically generate puzzle-solving questions that include dialects.

[2009] "Puzzle Problem" refers to a problem or challenge given to the user to solve.

[2010] A "smartphone" is a type of mobile phone that can be used to install applications.

[2011] "Application" refers to software that is installed on a smartphone and provides specific functions or services.

[2012] The system for implementing this invention allows users to become adventurers and participate in a treasure hunt while learning dialects from all over Japan. A specific embodiment of this system will be described below.

[2013] 1. System Configuration

[2014] The system consists of the following main components:

[2015] User device (smartphone)

[2016] server

[2017] Generative AI model (OpenAI GPT-3)

[2018] 2. Program Processing

[2019] User terminal

[2020] The user device is a smartphone with a dedicated application installed, which provides an interface for users to learn dialects and participate in puzzle-solving games.

[2021] server

[2022] The server has the following functions:

[2023] Dialect database management

[2024] Generating puzzles using generative AI models

[2025] User progress management

[2026] Generative AI Models

[2027] A generative AI model (OpenAI GPT-3) is used to automatically generate puzzles that include dialects. Specifically, given the following prompt sentence as input, it generates appropriate puzzles.

[2028] Data processing and calculation

[2029] The server retrieves specific dialects and their meanings from a dialect database and inputs prompt sentences into the generative AI model. The generated puzzles are then sent to the user's device, where the user answers them.

[2030] 3. Specific Examples

[2031] When users launch the application, they are first shown a section to learn the Hokkaido dialect. They learn that "namara" means "very." They are then presented with a puzzle generated by a generative AI model (GPT-3).

[2032] Prompt Sentence Examples

[2033] Use the Hokkaido dialect "namara" to generate puzzles that help users find the treasure.

[2034] The user remembers that "namara" means "very," and guesses that "cold place" refers to Hokkaido. After entering the correct answer, the next puzzle question is displayed.

[2035] In this way, users can enjoy an interactive puzzle game while learning dialects, providing an experience of experiencing local culture.

[2036] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2037] Step 1:

[2038] The user launches an application.

[2039] Input: User action (launching an application)

[2040] Output: Application home screen

[2041] What Happens: A user taps on an application icon on their smartphone, launching the application and displaying the home screen, which presents the user with options to begin their adventure.

[2042] Step 2:

[2043] The user selects the dialect learning section.

[2044] Input: User action (selection of dialect learning section)

[2045] Output: Dialect learning screen

[2046] What it does: A user taps the "Learn Dialects" button on the home screen and is taken to the dialect learning section, where they are shown dialects and their meanings for a specific region.

[2047] Step 3:

[2048] The server obtains dialect information from the dialect database.

[2049] Input: User's choice (specific region)

[2050] Output: Dialect information (e.g. "namara" -> "very")

[2051] Specific operation: The server accesses the dialect database and obtains dialect information for the region selected by the user. The obtained information is sent to the user's device and displayed on the dialect learning screen.

[2052] Step 4:

[2053] The user learns the dialect.

[2054] Input: Dialect information

[2055] Output: User comprehension (internal data)

[2056] What it does: The user reads and learns the dialect information displayed. The application records the user's learning progress as internal data.

[2057] Step 5:

[2058] The user starts the puzzle game.

[2059] Input: User action (start of puzzle game)

[2060] Output: Display of the puzzle

[2061] Specific operation: The user taps the "Start puzzle game" button to start the game. The server sends a prompt to the generative AI model.

[2062] Step 6:

[2063] The server sends a prompt to the generative AI model to generate a puzzle.

[2064] Input: Prompt text (e.g., "Please generate a puzzle to help the user find the treasure using the Hokkaido dialect word 'namara'.")

[2065] Output: Generated puzzle

[2066] Specific operation: The server sends a prompt to the generative AI model (OpenAI GPT-3) to generate a puzzle that includes dialect. The generated puzzle is returned to the server.

[2067] Step 7:

[2068] The server transmits the generated puzzle to the user terminal.

[2069] Input: Generated puzzle

[2070] Output: The puzzle displayed on the user's device

[2071] Specific operation: The server sends the generated puzzle to the user's terminal, and the problem is displayed on the user's terminal.

[2072] Step 8:

[2073] The user answers the puzzle questions.

[2074] Input: User's answer

[2075] Output: Answer evaluation result

[2076] Specific operation: The user enters the answer to the puzzle and taps the send button. The user device sends the answer to the server.

[2077] Step 9:

[2078] The server evaluates the user's answers.

[2079] Input: User's answer

[2080] Output: Evaluation result (correct / incorrect)

[2081] Specific operation: The server evaluates the user's answer and determines whether it is correct or incorrect. The evaluation result is sent to the user's device.

[2082] Step 10:

[2083] The user terminal displays the evaluation results.

[2084] Input: Evaluation result

[2085] Output: Feedback to the user

[2086] Specific operation: The user device receives the evaluation result and displays feedback to the user. If the answer is correct, the next puzzle question will be displayed.

[2087] Example 3

[2088] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[2089] Conventional dialect learning systems have limited opportunities for users to learn about local culture, customs, and history, resulting in a lack of interactive elements that allow users to effectively learn dialect phrases and words, which often leads to a lack of motivation to learn.

[2090] The identification process by the identification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for the user to become an adventurer and participate in a treasure hunt adventure while learning the local dialect, a means for the generative AI model to generate dialect phrases and words based on a prompt sentence, a means for the user to solve puzzles and find treasure using the dialect that the user has been taught, a means for the server to compare the generated content with a local culture database and select appropriate content, and a means for the terminal to display the generated dialect phrases and words to the user. This allows the user to experience local culture through learning the dialect.

[2091] "User" refers to an individual who uses the system to participate in dialect learning and treasure hunting adventures.

[2092] "Adventurer" refers to the role of a user who takes part in a treasure hunt adventure while learning dialects within the system.

[2093] A "dialect" refers to a unique linguistic expression or phrase used in a particular region or community.

[2094] "Generative AI model" refers to an artificial intelligence algorithm that generates dialect phrases and words based on user prompts.

[2095] "Prompt text" refers to the text of an instruction or question that a user enters into a system.

[2096] "Server" refers to the computer system that manages the processing of the entire system and connects with generative AI models and databases.

[2097] A "regional cultural database" refers to a database containing information about the customs, habits, and history of a particular region.

[2098] "Terminal" refers to the device through which a User accesses the System and displays dialect phrases and words.

[2099] The "treasure hunt adventure" refers to an activity in which users solve puzzles within the system and find treasure while learning the dialect.

[2100] "Solutions" refers to how users use the dialect they have been taught to solve problems and challenges within the system.

[2101] This invention is a system that allows users to participate in a treasure hunt adventure while learning a local dialect. The system uses a generative AI model to generate dialect phrases and words based on prompts and provides them to the user. A specific embodiment of this system is described below.

[2102] Hardware and software used

[2103] server

[2104] The server manages the overall processing of the system and coordinates with the generative AI model and the local cultural database. The server is a high-performance computer system that communicates with terminals via the Internet.

[2105] Terminal

[2106] A terminal is a device through which a user accesses the system and displays dialect phrases and words. A terminal can be a PC, smartphone, tablet, or other device with an internet connection.

[2107] Generative AI Models

[2108] A generative AI model is an artificial intelligence algorithm that generates dialect phrases and words based on a user prompt, specifically using an advanced natural language processing model such as OpenAI's GPT-4.

[2109] Regional Cultural Database

[2110] A regional cultural database is a database containing information about the customs, habits, and history of a particular region. The server references this database to verify the appropriateness of generated dialect phrases and words.

[2111] Program processing

[2112] The server receives a prompt from the user and inputs it into the generative AI model. The generative AI model generates dialect phrases and words based on the prompt. The server compares the generated content with the local cultural database and selects appropriate content. The selected content is sent from the server to the device, which displays it to the user.

[2113] Specific examples

[2114] For example, if the user inputs "Teach me how to greet people in the Osaka dialect," the following processing will be performed.

[2115] 1. The user types "Tell me how to greet someone in Osaka dialect" into the input field on the terminal.

[2116] 2. The terminal sends this prompt to the server.

[2117] 3. The server receives the prompt and inputs the prompt "Tell me how to greet someone in Osaka dialect" into the generative AI model.

[2118] 4. The generative AI model generates phrases such as "Maido!" and "Ookinii!"

[2119] 5. The server matches the generated phrase with the local cultural database and selects appropriate content.

[2120] 6. The server sends the selected phrase to the terminal.

[2121] 7. The device displays "Welcome!" or "Thank you!" to the user.

[2122] In this way, the user can gain experience of coming into contact with the local culture through learning the dialect. The flow of the identification process in the third embodiment will be described with reference to FIG.

[2123] Step 1:

[2124] The user inputs a prompt into the input field of the terminal. For example, the user inputs "Tell me how to greet someone in the Osaka dialect." The input prompt is temporarily stored in the terminal's memory.

[2125] Step 2:

[2126] The terminal sends the entered prompt text to the server. The terminal uses an Internet connection to send the prompt text to the server as a data packet. The input is the prompt text, and the output is a notification to the server that transmission has been completed.

[2127] Step 3:

[2128] The server receives the prompt text. The server analyzes the received prompt text and converts it into a format for input to the generative AI model. The input is the prompt text, and the output is the data format for input to the generative AI model.

[2129] Step 4:

[2130] The server inputs a prompt sentence into the generative AI model, which generates dialect phrases and words based on the prompt sentence. The input is the converted prompt sentence, and the output is the generated dialect phrases and words.

[2131] Step 5:

[2132] The generative AI model generates dialect phrases and words. For example, phrases such as "Maido!" and "Ookinii!" are generated. The generated data is returned to the server. The input is the prompt sentence, and the output is the generated phrase or word.

[2133] Step 6:

[2134] The server matches the generated content with the local cultural database. The server compares the generated phrases or words with the information in the database and selects the appropriate content. The input is the generated phrases or words, and the output is the match result.

[2135] Step 7:

[2136] The server selects the appropriate content. For example, it determines that "Maido!" and "Ookini!" are appropriate Osaka dialects. The selected content is stored in the server's memory. The input is the matching result, and the output is the selected phrase or word.

[2137] Step 8:

[2138] The server sends the selected content to the terminal. The server sends the selected phrase or word to the terminal as a data packet over the Internet. The input is the selected phrase or word, and the output is a notification to th...

Claims

1. an emotion engine that recognizes the user's emotions; an adjustment means for adjusting a dialect used by the user when the user becomes an adventurer and adventures in Japan in accordance with the emotion by inputting a specific prompt sentence into a generation AI model; A means for adjusting the difficulty of a puzzle that the user encounters during an adventure in response to the emotion; A means for recognizing a current location of the user based on location information and providing a dialect suitable for the current location of the user from among the adjusted dialects; A system including:

2. The system according to claim 1 , wherein the adjusting means adjusts the dialect according to the culture of a region in which the user is located and the emotion.

3. 2. The system of claim 1, further comprising means for playing audio for said user to learn the correct pronunciation of said dialect.

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