System

Abstract

A system is provided.SOLUTION: The system includes a user terminal for inputting game information, a server for receiving the input from the user and analyzing the information, a generation model for providing game conquering information based on the analyzed information, a database for accumulating the conquering information provided by the user, and a means for presenting an answer to the user based on the accumulated conquering information and external information.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] The problem that this invention aims to solve is to provide devices and services that enable game players to efficiently obtain strategy information, thereby solving the problem that the desired information cannot be found immediately or the search takes a long time using conventional web searches. Another object of this invention is to provide more abundant and useful strategy information by effectively accumulating useful information provided by users and sharing it with other users. [Means for solving the problem]

[0005] The present invention provides a system that includes a user terminal for inputting game information, a server that receives input from the user and analyzes the information, a generation model that provides game strategy information based on the analyzed information, a database that stores strategy information provided by the user, and means for presenting answers to the user based on the stored strategy information and external information.The generation model uses natural language processing technology, and the server has a proprietary database search function and external internet browsing function, thereby providing a system that allows users to easily obtain strategy information.

[0006] "Game information" refers to information related to the computer game the user is playing, and specifically refers to details such as the game title, in-game missions, quests, characters, monsters, equipment, events, and items.

[0007] "User terminal" refers to a device used by a user to input and obtain information, and specifically refers to a smartphone, tablet, personal computer, or similar device.

[0008] A "server" is a computer system that receives requests from users and performs processes such as analyzing data, executing generative models, searching databases, and obtaining external information.

[0009] A "generative model" is an algorithm or program that uses natural language processing technology to generate appropriate answers to user questions, and specifically refers to GPT (Generative Pre-trained Transformer) and similar models.

[0010] A "database" is a collection of data that stores strategy guides and other game-related information provided by users and is organized to allow for easy search and retrieval.

[0011] "External Information" refers to additional game strategy and up-to-date information that the server obtains from other websites, forums, strategy sites, and official websites on the Internet.

[0012] "Means for presenting answers" refers to the chatbot screen or in-app user interface that presents the analyzed and generated game strategy information to the user visually or in text. [Brief explanation of the drawings]

[0013] [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. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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, a 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), and an APU (Accelerated Processing Unit).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. 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."

[0034] The present invention is a system that includes a user terminal for inputting game information, a server that receives input from the user and analyzes the information, a generation model that provides game strategy information based on the analyzed information, a database that stores strategy information provided by the user, and a means for presenting answers to the user based on the stored strategy information and external information. Each element of the system and the overall processing flow will be explained in detail below.

[0035] The system starts when a user accesses a dedicated chatbot app or web page using a device. The device can be a smartphone, tablet, or personal computer, and can input the game title and strategy information they want to know into the chatbot. For example, they can input information in the form of "I want to know how to beat a specific boss in a certain game."

[0036] The server receives and analyzes the data entered by the user. Specifically, it analyzes the text data from the user and extracts keywords related to the "game title" and "desired information." The server then uses a generative model to generate appropriate answers to the user's questions. This generative model uses natural language processing technology and utilizes large amounts of training data to generate walkthrough information with high accuracy.

[0037] In addition to the generated answers, the server searches its own database to retrieve previously provided walkthroughs and other accumulated data. The server also uses external internet browsing to gather data from walkthrough sites, forums, etc. to obtain the latest walkthrough information. This ensures that the most up-to-date and reliable information is provided to the user.

[0038] For example, if a user asks "How can I efficiently level up in a certain game?", the server will analyze the question through a generative model and suggest "the best way to level up in a specific mission or dungeon." It will also search the server's database and integrate useful information provided by other users and additional strategy information collected from the internet. For example, it can provide specific strategies such as "Repeatedly fighting in a specific location is efficient."

[0039] Furthermore, if a user wants to provide new strategy information, they can input the information from their device and send it to the server. The server analyzes this new information and stores it in its own database. This makes it possible for other users to provide answers based on the latest information when they ask similar questions in the future.

[0040] This system allows users to efficiently acquire game strategy information and share their knowledge to help other users, resulting in smoother and more enjoyable gameplay.

[0041] The processing flow will be explained below.

[0042] Step 1:

[0043] The user launches a dedicated app or web page on their device, allowing them to view the chatbot's UI.

[0044] Step 2:

[0045] The user inputs the game title and the strategy information they want to know. For example, they input "Tell me how to beat boss B in game A."

[0046] Step 3:

[0047] The server receives input from the user. The received text data is passed to an analysis engine, which extracts keywords (game title and strategy). The keywords obtained are "Game A" and "How to beat Boss B."

[0048] Step 4:

[0049] The server uses a generative model (using natural language processing technology) to generate an answer to the user's question. The generative model receives input such as "How to beat Boss B in Game A" and outputs the answer text.

[0050] Step 5:

[0051] The server searches its own database and retrieves relevant information about "Boss B in Game A" from the accumulated strategy information. For example, it can obtain information such as "To defeat Boss B, it is best to use a specific skill."

[0052] Step 6:

[0053] The server collects information from external internet sources, scraping strategy sites and forums to gather the latest strategy information. This is where it obtains information such as "Boss B's weakness has been changed in the latest patch."

[0054] Step 7:

[0055] The server aggregates generated answers, information from its own database, and information collected from external sources to check for consistency and reliability, and selects the most relevant information to provide to the user.

[0056] Step 8:

[0057] The server then compiles the final answer and sends it to the user's device. The user receives specific strategy information, such as, "To efficiently defeat Boss B in Game A, it is recommended that you use a specific skill and exploit a weakness that was changed in the latest patch."

[0058] Step 9:

[0059] When a user wants to provide new strategy information, they input the information through the chatbot UI on their device. The chatbot posts, "I've discovered a new strategy for defeating Boss B."

[0060] Step 10:

[0061] The server receives new posts, performs text analysis, and stores the analyzed information in its own database. The new information is then reflected in search results from the next time onwards.

[0062] The above is the specific processing flow of this system, which allows for efficient acquisition of game strategy information and promotes information sharing among users.

[0063] Example 1

[0064] Next, a description will be given of 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."

[0065] In order to provide users with game strategy information efficiently and accurately, a system is needed that can analyze the information entered by the user, generate appropriate strategy information, and even integrate and present it with external information. However, conventional systems have difficulty in quickly and accurately obtaining the strategy information users need, and also lack the means to accumulate new information.

[0066] 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.

[0067] In this invention, the server includes means for receiving input from users and analyzing the information, means for providing a data generation model that provides game strategy information based on the analyzed information, and means for searching a data storage device that stores strategy information provided by users and for using information collected from outside. This allows users to efficiently obtain the strategy information they need and further add their own knowledge to the system.

[0068] "Game information" refers to data and strategy information related to the game that the user is playing.

[0069] "Information terminal" refers to devices used by users, such as smartphones, tablets, and personal computers.

[0070] "Processing device" refers to a computer or server that analyzes data received from a user and performs the necessary processing.

[0071] A "data generation model" refers to an algorithm or software that generates game strategy information based on input data.

[0072] "Data storage device" refers to a database or storage medium for storing strategy information provided by users.

[0073] "Externally collected information" refers to the latest strategy information obtained from the Internet and other sources.

[0074] "Natural language processing technology" refers to computer technology for understanding, analyzing, and generating human language.

[0075] A "generative AI model" is an artificial intelligence model that has been trained in advance with large amounts of data and uses natural language processing techniques to generate appropriate information from input data.

[0076] MODE FOR CARRYING OUT THE INVENTION

[0077] The present invention is a system that allows users to efficiently and accurately acquire game strategy information. This system is composed of a user terminal, a server, a data generation model, a data storage device, and external information collection means. Each element and processing flow of this system will be described in detail below.

[0078] User terminal

[0079] User terminals include devices such as smartphones, tablets, and personal computers. Users access a dedicated chatbot app or web page and input game strategy information. For example, they might input something like, "I want to know how to beat a specific boss in a specific game."

[0080] server

[0081] The server is a device that receives and analyzes data entered by users. The server analyzes the received text data and extracts keywords related to the "game title" and "desired information." The analysis uses natural language processing technology.

[0082] Data Generation Model

[0083] A data generation model is an algorithm or software that generates game strategy information based on analyzed keywords. This model is highly trained using large amounts of training data and generates appropriate answers to user questions. For example, it can provide the "optimal strategy for a specific mission or dungeon."

[0084] Data Storage Device

[0085] The data storage device includes a database and storage media for storing strategy information provided by users. The server can search and retrieve past strategy information in addition to the answers of the generative model.

[0086] External information gathering means

[0087] The server can collect the latest strategy information via the external Internet. It obtains data from strategy sites and forums and provides users with reliable, up-to-date information. With this function, the system can always provide answers to users based on the latest information.

[0088] Specific examples

[0089] For example, if a user types "Tell me how to beat Monster X" into the terminal, the specific actions are as follows:

[0090] 1. User device: The user types in "Tell me how to defeat Monster X" and presses the send button.

[0091] 2. Server: Analyzes the received text "Teach me how to defeat Monster X" and extracts "Monster X" and "how to defeat" as keywords.

[0092] 3. Data generation model: Based on the extracted keywords, strategy information is generated from the training data, and specific strategy information such as "Monster X is weak against water-element attacks. It is best to attack with a water-element weapon" is provided.

[0093] 4. Data storage device: Search the database for information related to "Monster X" provided by past users.

[0094] 5. External information gathering methods: Collect the latest strategy information from the Internet.

[0095] 6. Integration and presentation: All collected information is integrated and displayed on the user's device.

[0096] Prompt Sentence Examples

[0097] Here are some examples of prompts for generative AI models:

[0098] "Generate the best answer when a user asks how to defeat Monster X."

[0099] With the above configuration and operation, this system can provide users with efficient and accurate game strategy information, and also promote the sharing of knowledge among users.

[0100] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0101] System processing flow

[0102] Step 1:

[0103] A user accesses a chatbot app or web page from a device

[0104] What happens: A user accesses a compatible chatbot app or web page using a device such as a smartphone, tablet, or personal computer.

[0105] Input: User actions to access the chatbot app or web page

[0106] Output: The app or web page is launched and the interface is presented to the user.

[0107] Step 2:

[0108] The user inputs the strategy information, and the device sends the data to the server.

[0109] Specific operation: The user inputs the game title and specific strategy information into the displayed interface. Once input is complete, the device sends this data to the server. For example, the user might input "Tell me how to beat a specific boss."

[0110] Input: Strategy information entered by the user (text data)

[0111] Output: The entered data is sent to the server.

[0112] Step 3:

[0113] The server analyzes the received data and extracts keywords.

[0114] Specific operation: The server analyzes the received text data and extracts keywords related to the "game title" and "information you want to know." This analysis uses natural language processing technology.

[0115] Input: Text data sent by the user

[0116] Output: Extracted keywords (game title, desired information)

[0117] Step 4:

[0118] The server uses the generative model to generate the appropriate answer

[0119] How it works: Based on the extracted keywords, the server uses a generative model to generate answers to the user's questions. The generative model is trained on a large amount of training data.

[0120] Input: Extracted keywords

[0121] Output: Generated answer (specific strategy information)

[0122] Step 5:

[0123] The server searches its own database to obtain relevant information

[0124] Specific operation: In addition to the answer of the generative model, the server searches and retrieves the accumulated past strategy information from the database, which allows it to provide more reference information.

[0125] Input: Generated answers, database index information

[0126] Output: Related strategy information accumulated in the past

[0127] Step 6:

[0128] The server collects the latest exploit information from the external Internet.

[0129] Specific operation: The server uses external internet browsing functions to collect the latest cheat information from cheat sites and forums. The collected data is analyzed and only reliable information is extracted.

[0130] Input: Raw exploit information collected from external sources

[0131] Output: Analyzed and reliable strategy information

[0132] Step 7:

[0133] The server consolidates all the information it has acquired and provides the answer to the user.

[0134] Specific operation: The server integrates the generative model's answers, accumulated information, and externally collected data to provide the user with the most appropriate answer. For example, it may provide information such as "Using a weapon with a specific attribute is effective."

[0135] Input: Generated answers, historical information, externally collected information

[0136] Output: Integrated strategy information presented to the user

[0137] Step 8:

[0138] The user inputs new strategy information from the terminal, and the server stores the information in the database.

[0139] Specific operation: The user inputs and submits new strategy information from their device. The server analyzes this new information and stores it in the database. This allows the server to provide more accurate information when a similar question is asked in the future.

[0140] Input: New strategy information provided by the user

[0141] Output: New strategy information analyzed and stored in the database

[0142] Through the above steps, the system provides users with efficient and accurate game strategy information and promotes knowledge sharing among users.

[0143] (Application example 1)

[0144] Next, a description will be given of Application 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."

[0145] With the large number of games available on the market today, players need a way to quickly obtain game strategy information for each game and improve their gameplay based on that information. However, game strategy information is scattered, making it difficult to guarantee its reliability and up-to-dateness. Therefore, there is a need for a system that can provide game strategy information efficiently and reliably.

[0146] 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.

[0147] In this invention, the server includes means for receiving questions from users and generating optimal walkthrough information using a generative AI model, means for acquiring and integrating the latest information from an accumulated database and external sites, and means for presenting answers to users based on the accumulated walkthrough information and external information, thereby enabling users to quickly obtain the latest and most reliable walkthrough information.

[0148] "User terminal" refers to any device that a user uses to input game information.

[0149] A "server" is a device or system that receives input from a user and analyzes the information.

[0150] A "generative model" is a model that provides game strategy information based on analyzed information, and in particular makes use of natural language processing technology.

[0151] The "database" is an information repository for storing and managing strategy information provided by users.

[0152] A "means" is a method or apparatus for performing a particular function or role.

[0153] The "means for accepting a question" refers to a method or device by which the system accepts a question input from a user.

[0154] "Means for generating optimal strategy information using a generative AI model" refers to a method or device for generating optimal answers to user questions using AI technology.

[0155] The "means for acquiring and integrating the latest information" refers to a method or device for acquiring the latest strategy information from databases and external sites and integrating it.

[0156] The "means for presenting an answer to the user" is a method or device for providing an appropriate answer to the user based on the accumulated walkthrough information and the acquired external information.

[0157] The present invention provides a system that allows users to efficiently obtain game strategy information and store their own knowledge in a database. Below, each element of the system and the overall processing flow will be explained in detail.

[0158] User terminal

[0159] The system of the present invention includes a user terminal through which a user inputs game information. This user terminal can be any device, such as a smartphone, tablet, or personal computer. Using the terminal, a user accesses a dedicated chatbot app or web page and inputs questions and strategy information about the game.

[0160] server

[0161] The server receives input from users and analyzes the information. Specifically, it analyzes the text data from the users and extracts keywords related to the "game title" and "desired information." The server then uses a generative AI model to generate appropriate answers to the users' questions. This generative AI model uses natural language processing techniques and utilizes large amounts of training data. For example, answers generated using the OpenAI (registered trademark) API are utilized.

[0162] Database

[0163] The server has a database that stores strategy information provided by users. This database stores strategy information provided in the past and related information collected from other sources. The server also has a search function for its own database and an external Internet browsing function, and obtains the latest information from external strategy sites and forums and adds it to the database.

[0164] Generative AI Models

[0165] The generative AI model is an element that generates optimal strategy information based on the content of questions from users. The generative AI model aims to provide highly accurate and reliable information, and uses natural language processing technology to analyze the content of questions and generate appropriate answers. This model can use, for example, OpenAI's API.

[0166] Server processing flow

[0167] 1. When a user enters a specific question such as "Tell me the trick to defeating Monster X," the server receives this text data.

[0168] 2. Based on the received data, keywords such as "Monster X" and "tips for defeating it" are extracted.

[0169] 3. The generative AI model generates an initial answer using OpenAI's API.

[0170] 4. Query the database to retrieve relevant information provided by past users.

[0171] 5. Obtain the latest strategy information from external sites and integrate it as supplementary information.

[0172] 6. Provide the generated answer and additional information to the user.

[0173] Prompt Sentence Examples

[0174] If a user asks "What's the trick to defeating Monster X?", the server might send the following prompt to the generative AI model:

[0175] What are some tips for defeating Monster X?

[0176] By using this prompt, the server can generate optimal strategy information, allowing the user to efficiently acquire game strategy information and improve their gameplay.

[0177] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0178] Step 1:

[0179] A user enters game information into a chatbot app or web page.

[0180] Input: The question text from the user (e.g., "What's the trick to defeating Monster X?").

[0181] Output: User input data is sent to the server.

[0182] Step 2:

[0183] The server receives the data entered by the user.

[0184] Input: The user's question text arrives at the server.

[0185] Output: The received data is sent for analysis.

[0186] Step 3:

[0187] The server analyzes the text data from the user and extracts keywords for the "game title" and "information you want to know."

[0188] Input: The user's question text.

[0189] Data processing: Perform text analysis (natural language processing) and extract keywords.

[0190] Output: Extracted keywords (e.g. "Monster X", "Tips to defeat it").

[0191] Step 4:

[0192] The server passes the extracted keywords to the generative AI model, which then creates and sends prompt text to generate optimal strategy information.

[0193] Input: Extracted keywords.

[0194] Data calculation: Pass the prompt sentence to the generative AI model and generate an answer.

[0195] Output: Answer text from the generative AI model.

[0196] Specific operation: Generate a prompt sentence, "Please tell me some tips for defeating Monster X," and send it to the AI ​​model.

[0197] Step 5:

[0198] The server searches its own database to retrieve related strategy information that has been provided in the past.

[0199] Input: Extracted keywords.

[0200] Data processing: Perform database searches and obtain relevant strategy information.

[0201] Output: Past strategy information obtained.

[0202] Specific operation: Search for strategy information related to "Monster X" in the database and obtain the results.

[0203] Step 6:

[0204] The server uses external internet browsing functions to obtain the latest strategy information from external strategy sites and forums.

[0205] Input: Extracted keywords.

[0206] Data processing: Collect strategy information using web scraping and API access.

[0207] Output: The latest external exploit information obtained.

[0208] What it does: Perform a web search to get the latest information from trusted cheat sites.

[0209] Step 7:

[0210] The server integrates the generated answers, information from the database, and information from external sites to generate a final answer.

[0211] Input: Answer text from the generative AI model, past strategy information, and the latest external information.

[0212] Data arithmetic: content integration and organization.

[0213] Output: The final answer text.

[0214] What it does: Synthesizes all the information and creates a final answer in a user-friendly format.

[0215] Step 8:

[0216] The server sends the final consolidated answer to the user.

[0217] Input: Final answer text.

[0218] Output: The answer displayed on the user's terminal.

[0219] Specific operation: The final answer is sent to the user's device and displayed on the screen.

[0220] This allows the user to efficiently acquire game strategy information and improve their gameplay.

[0221] 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.

[0222] The present invention is a system that includes a user terminal for inputting game information, a server that receives input from the user and analyzes the information, a generative model that provides game strategy information based on the analyzed information, a database that stores strategy information provided by the user, a means for presenting answers to the user based on the stored strategy information and external information, and an emotion engine that recognizes the user's emotions. Each element of the system and the overall processing flow will be explained in detail below.

[0223] User device operation

[0224] The system starts when a user accesses a dedicated chatbot app or web page using a terminal. User terminals consist of various devices, including smartphones, tablets, and personal computers. Through the chatbot's UI, users can input the game title and strategy information they want to know. For example, they can make a request to the chatbot such as, "Tell me how to beat Boss B in Game A."

[0225] Server analysis and information generation

[0226] The server receives input data from the user and passes it to the analysis engine. The analysis engine extracts keywords related to the "game title" and "desired information" from the user's text. The server then uses a generative model to generate appropriate answers to the user's questions. The generative model uses natural language processing technology and utilizes large amounts of training data to generate walkthrough information with high accuracy.

[0227] Information Integration

[0228] In addition to the generated answers, the server searches its own database to retrieve walkthroughs and other data previously provided by users. The server also uses external internet browsing functions to gather the latest walkthrough information from walkthrough sites, forums, etc. This ensures that the most up-to-date and reliable information is provided to users.

[0229] The role of the emotional engine

[0230] Another feature of the present invention is the inclusion of an emotion engine, which uses text analysis and speech recognition technology to recognize the user's emotions. The server uses the emotion data obtained from the emotion engine to adjust the tone and content of the information provided by the generative model. For example, if the user expresses dissatisfaction, the server may add more detailed explanations or words of encouragement.

[0231] Displaying Information

[0232] The server sends the integrated strategy information and the content adjusted by the emotion engine to the user's device. The user receives a response through the chatbot, such as, "To efficiently defeat Boss B in Game A, it is recommended that you use specific skills and exploit the weaknesses that have been changed in the latest patch. Good luck!"

[0233] User input

[0234] When a user wants to provide new strategy information, they can enter it through the chatbot UI on their device. They can post new information in the form of, for example, "I tried out a newly discovered strategy and was able to easily defeat a specific boss."

[0235] Accumulation in the database

[0236] The server receives the new information, performs text analysis, and stores the analyzed information in its own database. The index is updated to reflect the new information and be used in future search results.

[0237] As a concrete example, if a user asks "How do I beat Mission D in Game C?", the server uses a generative model to generate the optimal strategy and adds an encouraging message based on the user's emotions (e.g., frustration) recognized by the emotion engine. As a result, the server provides an answer such as "Using a specific item is effective in beating Mission D. There may be some difficult parts, but don't give up! Try!"

[0238] The above is a detailed description of the embodiment of the present invention. This system allows users to efficiently obtain game strategy information and also provides appropriate support that takes into consideration the user's feelings.

[0239] The processing flow will be explained below.

[0240] Step 1:

[0241] The user launches a dedicated app or web page on their device, allowing them to view the chatbot's UI.

[0242] Step 2:

[0243] The user inputs the game title and the strategy information they want to know. For example, they input "Tell me how to beat boss B in game A."

[0244] Step 3:

[0245] The server receives input data from the user. The received text data is passed to an analysis engine, which extracts keywords (game title and strategy). The keywords obtained are "Game A" and "How to beat Boss B."

[0246] Step 4:

[0247] The server uses a generative model (using natural language processing technology) to generate an answer to the user's question. The generative model receives input such as "How to beat Boss B in Game A" and outputs the answer text.

[0248] Step 5:

[0249] The server searches its own database and retrieves relevant information about "Boss B in Game A" from the accumulated strategy information. For example, it can obtain information such as "To defeat Boss B, it is best to use a specific skill."

[0250] Step 6:

[0251] The server collects information from external internet sources, scraping strategy sites and forums to gather the latest strategy information. This is where it obtains information such as "Boss B's weakness has been changed in the latest patch."

[0252] Step 7:

[0253] The server aggregates generated answers, information from its own database, and information collected from external sources to check for consistency and reliability, and selects the most relevant information to provide to the user.

[0254] Step 8:

[0255] The server uses an emotion engine to analyze emotions from the text and voice data entered by the user, for example by analyzing the context of the input text and the tone of the voice to determine whether the user is annoyed or satisfied.

[0256] Step 9:

[0257] The server adjusts the tone and content of the response based on the user's emotional data analyzed by the emotion engine. For example, if the user is frustrated, the server adjusts the response by adding more detailed explanations or encouraging words.

[0258] Step 10:

[0259] The server then sends the final adjusted answer to the user's device. The user receives specific strategy information, such as, "To efficiently defeat Boss B in Game A, it is recommended that you use specific skills and exploit the weaknesses that were changed in the latest patch. Good luck!"

[0260] Step 11:

[0261] When a user wants to provide new strategy information, they input the information through the chatbot UI on their device. The chatbot posts, "I've discovered a new strategy for defeating Boss B."

[0262] Step 12:

[0263] The server receives new posts, performs text analysis, stores the analyzed information in its own database, and updates the index to reflect the new information and ensure it is reflected in future search results.

[0264] The above is the specific processing flow of this system, which allows users to efficiently obtain game strategy information and receive adaptive support based on the user's emotions.

[0265] Example 2

[0266] Next, a description will be given of 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."

[0267] Conventional game strategy information providing systems may not always be able to provide the specific strategy information desired by the user, and may also lack consideration for the user's feelings. This raises concerns that the quality of the user experience may be reduced. The present invention aims to provide a system that efficiently provides users with game strategy information and provides appropriate support that takes the user's feelings into consideration.

[0268] 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.

[0269] In this invention, the server includes means for receiving input from a user and analyzing the information, a generative model means for providing game strategy information based on the analyzed information, and a means for presenting answers to the user based on the accumulated strategy information and external information. This makes it possible to quickly provide specific and up-to-date strategy information that the user desires. Furthermore, by using an emotion engine means, it is possible to respond according to the user's emotions, improving the quality of the user experience.

[0270] "Game information" refers to any data or information that a user has relating to a game, and specifically includes strategies for winning, strategies for defeating boss characters, details of missions, and the like.

[0271] "User terminal" refers to a device used by a user to search for and input game information, including smartphones, tablets, personal computers, etc.

[0272] "Server" refers to a computer system that receives information sent by a user, analyzes it, and provides the necessary data.

[0273] A "generative model" refers to an algorithm or program for generating game strategy information in response to a user's request, and generally uses natural language processing technology.

[0274] "Database" refers to a system that stores strategy information provided by users and information collected from external sources, and prepares it for later retrieval or access.

[0275] "External information" refers to new information that has not been stored in the database but has been collected from online strategy sites, forums, etc.

[0276] An "emotion engine" refers to technology or a program that analyzes user input data and voice data and identifies the emotion it conveys.

[0277] "Chatbot UI" refers to an interactive user interface that allows users to input questions or requests and receive responses from the system.

[0278] The present invention is a system that includes a user terminal for inputting game information, a server that receives input from users and analyzes the information, a generation model that provides game strategy information based on the analyzed information, a database that stores strategy information provided by users, a means for presenting answers to users based on the stored strategy information and external information, and an emotion engine that recognizes the user's emotions. Each element of the system and the overall processing flow will be explained in detail below.

[0279] User terminal operation

[0280] The system starts when a user accesses a dedicated chatbot app or web page using a terminal. User terminals consist of various devices, including smartphones, tablets, and personal computers. Through the chatbot's UI, users can input the game title and strategy information they want to know. For example, they can make a request to the chatbot such as, "Tell me how to beat Boss B in Game A."

[0281] Server analysis and information generation

[0282] The server receives input data from users and passes it to an analysis engine. The analysis engine extracts keywords related to the "game title" and "desired information" from the user's text. The server then uses a generative model to generate appropriate answers to the user's questions. The generative model uses natural language processing technology and utilizes large amounts of training data to generate walkthrough information with high accuracy.

[0283] Information Integration

[0284] In addition to the generated answers, the server searches its own database to retrieve walkthroughs and other data previously provided by users. The server also uses external internet browsing functions to gather the latest walkthrough information from walkthrough sites, forums, etc. This ensures that users are provided with the most up-to-date and reliable information.

[0285] The role of the emotional engine

[0286] Another feature of the present invention is the inclusion of an emotion engine. The emotion engine uses text analysis and speech recognition technology to recognize the user's emotions. The server uses the emotion data obtained from the emotion engine to adjust the tone and content of the information provided by the generative model. For example, if the user expresses dissatisfaction, the server may add more detailed explanations or words of encouragement.

[0287] Displaying Information

[0288] The server sends the integrated strategy information and content adjusted by the emotion engine to the user's device. The user receives a response through the chatbot, such as, "To efficiently defeat Boss B in Game A, it is recommended that you use specific skills and exploit the weaknesses that have been changed in the latest patch. Good luck!"

[0289] Information provided by users

[0290] When users want to provide new strategy information, they can enter it through the chatbot UI on their device. They can post new information in the form of, for example, "I tried out a newly discovered strategy and was able to easily defeat a particular boss."

[0291] Accumulation in the database

[0292] The server receives the new information, performs text analysis, and stores the analyzed information in its own database. The index is updated to reflect the new information and be used in future search results.

[0293] As a concrete example, if a user asks "How do I beat Mission D in Game C?", the server uses a generative model to generate the optimal strategy and adds an encouraging message based on the user's emotions (e.g., frustration) recognized by the emotion engine. As a result, the server provides an answer such as, "Using a specific item is effective in beating Mission D. There may be some difficult parts, but don't give up! Keep trying!"

[0294] Prompt Sentence Examples

[0295] For example, the prompt you would enter into a generative AI model might look like this:

[0296] A user asks, "Tell me how to complete Mission D in Game C." According to the emotion engine, the user seems to be frustrated. Please generate an answer that provides information on how to complete Mission D and encourages the user.

[0297] Using this prompt, the generative AI model generates an answer that takes into account the user's best information and emotions.

[0298] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0299] Step 1:

[0300] A user accesses a chatbot app or web page using a device

[0301] Input: The device starts up and opens a dedicated app or web page.

[0302] How it works: A user turns on a device such as a smartphone, tablet, or personal computer and accesses the dedicated app "GameHelper" or a web page.

[0303] Output: The home screen of the chatbot app or web page is displayed.

[0304] Step 2:

[0305] User requests walkthrough information from chatbot UI

[0306] Input: The user types a request for a specific game title or gameplay information into the chatbot's input field.

[0307] How it works: A user types a request into a chatbot, such as "Tell me how to beat boss B in game A."

[0308] Output: The user's request data is sent to the server.

[0309] Step 3:

[0310] The server receives user input and passes it to the analysis engine

[0311] Input: The server receives the request data from the user.

[0312] How it works: The server passes the request to the analysis engine, which extracts keywords for the "game title" and "information you want to know."

[0313] Output: Keywords such as "Game A" and "Boss B" are extracted.

[0314] Step 4:

[0315] The server generates and inputs prompts into the generative model.

[0316] Input: A prompt sentence based on the extracted keywords.

[0317] Operation: The server generates and inputs a prompt to a generative AI model (e.g., GPT-3 (registered trademark)). The prompt is "Tell me how to beat boss B in game A."

[0318] Output: A prompt for the generative model to answer.

[0319] Step 5:

[0320] The generative model generates the appropriate answer

[0321] Input: A generative model given a prompt sentence.

[0322] How it works: The generative model generates highly accurate strategy information based on the prompt sentence.

[0323] Output: An answer such as "Using a specific skill is effective in defeating Boss B."

[0324] Step 6:

[0325] The server integrates its own database with external information

[0326] Input: Answers from the generative model, proprietary databases, and information from external sites.

[0327] How it works: In addition to the generated answers, the server also integrates past walkthroughs from the database and the latest information from external sites.

[0328] Output: Consolidated and reliable strategy information.

[0329] Step 7:

[0330] The server analyzes the user's emotions using an emotion engine.

[0331] Input: User input data.

[0332] How it works: The emotion engine uses text and speech analysis to identify the user's emotions.

[0333] Output: User emotion (e.g., annoyance).

[0334] Step 8:

[0335] The server uses emotional data to tailor responses

[0336] Input: Generated strategy information and user emotion data.

[0337] How it works: The server uses the emotion data to adjust the tone and content of the generated responses.

[0338] Output: An answer such as "Using specific skills is effective in defeating Boss B. Also, don't give up, keep trying!"

[0339] Step 9:

[0340] The server sends the generated answer to the user's device.

[0341] Input: Adjusted response data.

[0342] Operation: The server sends the optimal strategy information to the user's device.

[0343] Output: Walkthrough information displayed on the user's device.

[0344] Step 10:

[0345] The user receives the information on the device

[0346] Input: The response data sent from the server.

[0347] How it works: The user receives the answer through the chatbot UI on their device.

[0348] Output: Strategy information displayed in the chat window.

[0349] Step 11:

[0350] Users provide new strategy information

[0351] Input: User data to input new walkthrough information.

[0352] How it works: A user posts a new strategy through the chatbot UI.

[0353] Output: New cheats from the user.

[0354] Step 12:

[0355] The server receives and analyzes the new information.

[0356] Input: New cheats provided by the user.

[0357] How it works: The server receives new exploit information and analyzes it using the analysis engine.

[0358] Output: New parsed exploit data.

[0359] Step 13:

[0360] The server stores the analyzed information in a database

[0361] Input: Parsed new exploit data.

[0362] How it works: The server stores the new information in its own database and updates the index so that it is reflected in future searches.

[0363] Output: An updated database with the latest cheats.

[0364] (Application example 2)

[0365] Next, a description will be given of Application 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."

[0366] A problem with conventional user information provision systems is that they provide uniform information without considering the user's emotions. This can lead to lower user satisfaction and the inability to receive optimal support. Furthermore, in virtual stores, customer support that does not respond to the customer's emotions can lead to a decrease in purchasing motivation.

[0367] 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.

[0368] In this invention, the server includes an emotion engine that analyzes the user's emotional state, means for adjusting responses based on the analysis results of the emotion engine, and means for analyzing user input and generating optimal information using a generative model, thereby making it possible to provide optimal information and support according to the user's emotions.

[0369] A "user terminal" is a device used by a user to input information, and includes a smartphone, tablet, personal computer, etc.

[0370] A "server" is a computer whose role is to receive input from users and analyze the information.

[0371] A "generative model" is an artificial intelligence model that uses natural language processing technology to provide appropriate answers and information based on analyzed information.

[0372] A "database" is an information management system that stores user-provided information and makes it available for later searches and queries.

[0373] The "emotion engine" is an analysis engine that has the function of analyzing the user's emotional state and adjusting the response content based on the results.

[0374] "Means of presenting information" refers to the functions and interfaces that allow the server to provide optimal information to users based on accumulated data and external information.

[0375] The "means for adjusting the response" is a function for appropriately adjusting the content of the response to the user based on the analysis results of the emotion engine, and providing optimal support and information.

[0376] "Natural language processing technology" is an artificial intelligence technology for understanding input text from users and generating appropriate responses.

[0377] A "proprietary database" is a database owned by the system that stores information obtained from users and external information.

[0378] The "external Internet browsing function" is a function that allows the server to collect external data and information via the Internet.

[0379] The present invention realizes the provision of information that takes into consideration the user's emotions by constructing a customer support system that includes a user terminal, a server, a generative model, a database, an emotion engine, and an information presentation means.

[0380] System configuration

[0381] User device operation

[0382] Users access a dedicated application or web page using a user device such as a smartphone, tablet, or personal computer and enter information. For example, the system starts by sending a request such as, "Please tell me more about the newly released smartphone."

[0383] Server Features

[0384] The server receives input data from the user and passes it to the analysis engine. The analysis engine extracts keywords related to the "question" and "related information" from the user's text. The generative model then generates the most appropriate answer. This generative model uses natural language processing technology to provide answers to the user's questions with high accuracy.

[0385] The role of the emotional engine

[0386] The emotion engine uses text analysis and speech recognition technology to recognize the user's emotional state. The server then adjusts the tone and content of the information generated by the generative model based on the emotional data obtained from the emotion engine. For example, if the user is excited, the server adds a positive response with detailed information.

[0387] Information Integration

[0388] In addition to the generated answers, the server searches its own database to retrieve information previously provided by users and external information, and also uses external internet browsing functions to gather the latest relevant information, allowing it to provide the most reliable and up-to-date information.

[0389] Displaying Information

[0390] The server sends the integrated information to the user's device, and the user can receive the information through the chatbot. For example, a response such as "The newly released smartphone has advanced camera functions and is in stock. We are also running a special campaign!" is provided.

[0391] About program processing

[0392] The server operates using the following processing means:

[0393] Emotion engine that analyzes emotional states: Analyzes emotions from user text using OpenAI's API.

[0394] How responses are tailored: Generative AI models are used based on the analysis results to generate optimal responses for the user.

[0395] Means of analyzing input and generating information: A live AI model using natural language processing techniques is used to generate appropriate answers to user questions.

[0396] Examples of concrete examples and prompts

[0397] For example, if a user sends a request such as "Tell me more about the new smartphone," the server analyzes the user's emotional state and uses a generative model to generate the best answer. Here is an example prompt:

[0398] A customer asked the following question: Can you tell me more about the newly launched smartphone?

[0399] The customer's emotion is excitement. Generate an appropriate response.

[0400] This results in a response like, "Our new smartphone has advanced camera features and is in stock. We're also running a special promotion!"

[0401] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0402] Step 1:

[0403] A user accesses a dedicated application or web page using a user terminal such as a smartphone or personal computer and inputs information. An example of input data is a request such as "Please tell me more about the newly released smartphone." This request is sent to the server.

[0404] Step 2:

[0405] The server receives input data from the user. It passes this received data to the analysis engine and begins analysis. The analysis engine extracts keywords for the "question content" and "related information" from the user's text. For example, the keyword "newly released smartphone" is extracted from the input data "Please tell me more about the newly released smartphone."

[0406] Step 3:

[0407] The server uses a generative model based on the analysis results to generate the optimal answer. The generative AI model uses natural language processing technology to generate the optimal answer from the extracted keywords. The prompt sentence in this case will be in the form below.

[0408] A customer asked the following question: Can you tell me more about the newly launched smartphone?

[0409] The customer's emotion is excitement. Generate an appropriate response.

[0410] Step 4:

[0411] In addition to the generated answer, the server searches its own database to retrieve information previously provided by the user and the latest related information. It also uses an external internet browsing function to collect the latest related information. This process retrieves data related to "new smartphones" from its own database and the latest information from external websites.

[0412] Step 5:

[0413] The emotion engine analyzes user input data to recognize their emotional state. For example, a request like "Tell me more about the new smartphone" might be interpreted as indicating that the user is excited.

[0414] Step 6:

[0415] The server then adjusts the responses generated by the generative model based on the analysis results of the emotion engine. For example, if the user is excited, a proactive and detailed response will be generated. This process is performed to adapt the content of the response to the user's emotional state.

[0416] Step 7:

[0417] The server sends the integrated information to the user's device, where it is displayed on the screen, and the user receives a response from the chatbot: "The newly released smartphone has advanced camera functions, is in stock, and we're also running a special campaign!"

[0418] In this way, a system is realized that takes into consideration the user's feelings and can provide appropriate and timely information.

[0419] 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.

[0420] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) 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.

[0421] 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.

[0422] [Second embodiment]

[0423] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0435] The present invention is a system that includes a user terminal for inputting game information, a server that receives input from the user and analyzes the information, a generation model that provides game strategy information based on the analyzed information, a database that stores strategy information provided by the user, and a means for presenting answers to the user based on the stored strategy information and external information. Each element of the system and the overall processing flow will be explained in detail below.

[0436] The system starts when a user accesses a dedicated chatbot app or web page using a device. The device can be a smartphone, tablet, or personal computer, and can input the game title and strategy information they want to know into the chatbot. For example, they can input information in the form of "I want to know how to beat a specific boss in a certain game."

[0437] The server receives and analyzes the data entered by the user. Specifically, it analyzes the text data from the user and extracts keywords related to the "game title" and "desired information." The server then uses a generative model to generate appropriate answers to the user's questions. This generative model uses natural language processing technology and utilizes large amounts of training data to generate walkthrough information with high accuracy.

[0438] In addition to the generated answers, the server searches its own database to retrieve previously provided walkthroughs and other accumulated data. The server also uses external internet browsing to gather data from walkthrough sites, forums, etc. to obtain the latest walkthrough information. This ensures that the most up-to-date and reliable information is provided to the user.

[0439] For example, if a user asks "How can I efficiently level up in a certain game?", the server will analyze the question through a generative model and suggest "the best way to level up in a specific mission or dungeon." It will also search the server's database and integrate useful information provided by other users and additional strategy information collected from the internet. For example, it can provide specific strategies such as "Repeatedly fighting in a specific location is efficient."

[0440] Furthermore, if a user wants to provide new strategy information, they can input the information from their device and send it to the server. The server analyzes this new information and stores it in its own database. This makes it possible for other users to provide answers based on the latest information when they ask similar questions in the future.

[0441] This system allows users to efficiently acquire game strategy information and share their knowledge to help other users, resulting in smoother and more enjoyable gameplay.

[0442] The processing flow will be explained below.

[0443] Step 1:

[0444] The user launches a dedicated app or web page on their device, allowing them to view the chatbot's UI.

[0445] Step 2:

[0446] The user inputs the game title and the strategy information they want to know. For example, they input "Tell me how to beat boss B in game A."

[0447] Step 3:

[0448] The server receives input from the user. The received text data is passed to an analysis engine, which extracts keywords (game title and strategy). The keywords obtained are "Game A" and "How to beat Boss B."

[0449] Step 4:

[0450] The server uses a generative model (using natural language processing technology) to generate an answer to the user's question. The generative model receives input such as "How to beat Boss B in Game A" and outputs the answer text.

[0451] Step 5:

[0452] The server searches its own database and retrieves relevant information about "Boss B in Game A" from the accumulated strategy information. For example, it can obtain information such as "To defeat Boss B, it is best to use a specific skill."

[0453] Step 6:

[0454] The server collects information from external internet sources, scraping strategy sites and forums to gather the latest strategy information. This is where it obtains information such as "Boss B's weakness has been changed in the latest patch."

[0455] Step 7:

[0456] The server aggregates generated answers, information from its own database, and information collected from external sources to check for consistency and reliability, and selects the most relevant information to provide to the user.

[0457] Step 8:

[0458] The server then compiles the final answer and sends it to the user's device. The user receives specific strategy information, such as, "To efficiently defeat Boss B in Game A, it is recommended that you use a specific skill and exploit a weakness that was changed in the latest patch."

[0459] Step 9:

[0460] When a user wants to provide new strategy information, they input the information through the chatbot UI on their device. The chatbot posts, "I've discovered a new strategy for defeating Boss B."

[0461] Step 10:

[0462] The server receives new posts, performs text analysis, and stores the analyzed information in its own database. The new information is then reflected in search results from the next time onwards.

[0463] The above is the specific processing flow of this system, which allows for efficient acquisition of game strategy information and promotes information sharing among users.

[0464] Example 1

[0465] Next, a description will be given of 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."

[0466] In order to provide users with game strategy information efficiently and accurately, a system is needed that can analyze the information entered by the user, generate appropriate strategy information, and even integrate and present it with external information. However, conventional systems have difficulty in quickly and accurately obtaining the strategy information users need, and also lack the means to accumulate new information.

[0467] 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.

[0468] In this invention, the server includes means for receiving input from users and analyzing the information, means for providing a data generation model that provides game strategy information based on the analyzed information, and means for searching a data storage device that stores strategy information provided by users and for using information collected from outside. This allows users to efficiently obtain the strategy information they need and further add their own knowledge to the system.

[0469] "Game information" refers to data and strategy information related to the game that the user is playing.

[0470] "Information terminal" refers to devices used by users, such as smartphones, tablets, and personal computers.

[0471] "Processing device" refers to a computer or server that analyzes data received from a user and performs the necessary processing.

[0472] A "data generation model" refers to an algorithm or software that generates game strategy information based on input data.

[0473] "Data storage device" refers to a database or storage medium for storing strategy information provided by users.

[0474] "Externally collected information" refers to the latest strategy information obtained from the Internet and other sources.

[0475] "Natural language processing technology" refers to computer technology for understanding, analyzing, and generating human language.

[0476] A "generative AI model" is an artificial intelligence model that has been trained in advance with large amounts of data and uses natural language processing techniques to generate appropriate information from input data.

[0477] MODE FOR CARRYING OUT THE INVENTION

[0478] The present invention is a system that allows users to efficiently and accurately acquire game strategy information. This system is composed of a user terminal, a server, a data generation model, a data storage device, and external information collection means. Each element and processing flow of this system will be described in detail below.

[0479] User terminal

[0480] User terminals include devices such as smartphones, tablets, and personal computers. Users access a dedicated chatbot app or web page and input game strategy information. For example, they might input something like, "I want to know how to beat a specific boss in a specific game."

[0481] server

[0482] The server is a device that receives and analyzes data entered by users. The server analyzes the received text data and extracts keywords related to the "game title" and "desired information." The analysis uses natural language processing technology.

[0483] Data Generation Model

[0484] A data generation model is an algorithm or software that generates game strategy information based on analyzed keywords. This model is highly trained using large amounts of training data and generates appropriate answers to user questions. For example, it can provide the "optimal strategy for a specific mission or dungeon."

[0485] Data Storage Device

[0486] The data storage device includes a database and storage media for storing strategy information provided by users. The server can search and retrieve past strategy information in addition to the answers of the generative model.

[0487] External information gathering means

[0488] The server can collect the latest strategy information via the external Internet. It obtains data from strategy sites and forums and provides users with reliable, up-to-date information. With this function, the system can always provide answers to users based on the latest information.

[0489] Specific examples

[0490] For example, if a user types "Tell me how to beat Monster X" into the terminal, the specific actions are as follows:

[0491] 1. User device: The user types in "Tell me how to defeat Monster X" and presses the send button.

[0492] 2. Server: Analyzes the received text "Teach me how to defeat Monster X" and extracts "Monster X" and "how to defeat" as keywords.

[0493] 3. Data generation model: Based on the extracted keywords, strategy information is generated from the training data, and specific strategy information such as "Monster X is weak against water-element attacks. It is best to attack with a water-element weapon" is provided.

[0494] 4. Data storage device: Search the database for information related to "Monster X" provided by past users.

[0495] 5. External information gathering methods: Collect the latest strategy information from the Internet.

[0496] 6. Integration and presentation: All collected information is integrated and displayed on the user's device.

[0497] Prompt Sentence Examples

[0498] Here are some examples of prompts for generative AI models:

[0499] "Generate the best answer when a user asks how to defeat Monster X."

[0500] With the above configuration and operation, this system can provide users with efficient and accurate game strategy information, and also promote the sharing of knowledge among users.

[0501] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0502] System processing flow

[0503] Step 1:

[0504] A user accesses a chatbot app or web page from a device

[0505] What happens: A user accesses a compatible chatbot app or web page using a device such as a smartphone, tablet, or personal computer.

[0506] Input: User actions to access the chatbot app or web page

[0507] Output: The app or web page is launched and the interface is presented to the user.

[0508] Step 2:

[0509] The user inputs the strategy information, and the device sends the data to the server.

[0510] Specific operation: The user inputs the game title and specific strategy information into the displayed interface. Once input is complete, the device sends this data to the server. For example, the user might input "Tell me how to beat a specific boss."

[0511] Input: Strategy information entered by the user (text data)

[0512] Output: The entered data is sent to the server.

[0513] Step 3:

[0514] The server analyzes the received data and extracts keywords.

[0515] Specific operation: The server analyzes the received text data and extracts keywords related to the "game title" and "information you want to know." This analysis uses natural language processing technology.

[0516] Input: Text data sent by the user

[0517] Output: Extracted keywords (game title, desired information)

[0518] Step 4:

[0519] The server uses the generative model to generate the appropriate answer

[0520] How it works: Based on the extracted keywords, the server uses a generative model to generate answers to the user's questions. The generative model is trained on a large amount of training data.

[0521] Input: Extracted keywords

[0522] Output: Generated answer (specific strategy information)

[0523] Step 5:

[0524] The server searches its own database to obtain relevant information

[0525] Specific operation: In addition to the answer of the generative model, the server searches and retrieves the accumulated past strategy information from the database, which allows it to provide more reference information.

[0526] Input: Generated answers, database index information

[0527] Output: Related strategy information accumulated in the past

[0528] Step 6:

[0529] The server collects the latest exploit information from the external Internet.

[0530] Specific operation: The server uses external internet browsing functions to collect the latest cheat information from cheat sites and forums. The collected data is analyzed and only reliable information is extracted.

[0531] Input: Raw exploit information collected from external sources

[0532] Output: Analyzed and reliable strategy information

[0533] Step 7:

[0534] The server consolidates all the information it has acquired and provides the answer to the user.

[0535] Specific operation: The server integrates the generative model's answers, accumulated information, and externally collected data to provide the user with the most appropriate answer. For example, it may provide information such as "Using a weapon with a specific attribute is effective."

[0536] Input: Generated answers, historical information, externally collected information

[0537] Output: Integrated strategy information presented to the user

[0538] Step 8:

[0539] The user inputs new strategy information from the terminal, and the server stores the information in the database.

[0540] Specific operation: The user inputs and submits new strategy information from their device. The server analyzes this new information and stores it in the database. This allows the server to provide more accurate information when a similar question is asked in the future.

[0541] Input: New strategy information provided by the user

[0542] Output: New strategy information analyzed and stored in the database

[0543] Through the above steps, the system provides users with efficient and accurate game strategy information and promotes knowledge sharing among users.

[0544] (Application example 1)

[0545] Next, a description will be given of Application 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."

[0546] With the large number of games available on the market today, players need a way to quickly obtain game strategy information for each game and improve their gameplay based on that information. However, game strategy information is scattered, making it difficult to guarantee its reliability and up-to-dateness. Therefore, there is a need for a system that can provide game strategy information efficiently and reliably.

[0547] 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.

[0548] In this invention, the server includes means for receiving questions from users and generating optimal walkthrough information using a generative AI model, means for acquiring and integrating the latest information from an accumulated database and external sites, and means for presenting answers to users based on the accumulated walkthrough information and external information, thereby enabling users to quickly obtain the latest and most reliable walkthrough information.

[0549] "User terminal" refers to any device that a user uses to input game information.

[0550] A "server" is a device or system that receives input from a user and analyzes the information.

[0551] A "generative model" is a model that provides game strategy information based on analyzed information, and in particular makes use of natural language processing technology.

[0552] The "database" is an information repository for storing and managing strategy information provided by users.

[0553] A "means" is a method or apparatus for performing a particular function or role.

[0554] The "means for accepting a question" refers to a method or device by which the system accepts a question input from a user.

[0555] "Means for generating optimal strategy information using a generative AI model" refers to a method or device for generating optimal answers to user questions using AI technology.

[0556] The "means for acquiring and integrating the latest information" refers to a method or device for acquiring the latest strategy information from databases and external sites and integrating it.

[0557] The "means for presenting an answer to the user" is a method or device for providing an appropriate answer to the user based on the accumulated walkthrough information and the acquired external information.

[0558] The present invention provides a system that allows users to efficiently obtain game strategy information and store their own knowledge in a database. Below, each element of the system and the overall processing flow will be explained in detail.

[0559] User terminal

[0560] The system of the present invention includes a user terminal through which a user inputs game information. This user terminal can be any device, such as a smartphone, tablet, or personal computer. Using the terminal, a user accesses a dedicated chatbot app or web page and inputs questions and strategy information about the game.

[0561] server

[0562] The server receives input from users and analyzes the information. Specifically, it analyzes the text data from the user and extracts keywords related to the "game title" and "information the user wants to know." The server then uses a generative AI model to generate appropriate answers to the user's questions. This generative AI model uses natural language processing technology and utilizes large amounts of training data. For example, answers generated using OpenAI's API are utilized.

[0563] Database

[0564] The server has a database that stores strategy information provided by users. This database stores strategy information provided in the past and related information collected from other sources. The server also has a search function for its own database and an external Internet browsing function, and obtains the latest information from external strategy sites and forums and adds it to the database.

[0565] Generative AI Models

[0566] The generative AI model is an element that generates optimal strategy information based on the content of questions from users. The generative AI model aims to provide highly accurate and reliable information, and uses natural language processing technology to analyze the content of questions and generate appropriate answers. This model can use, for example, OpenAI's API.

[0567] Server processing flow

[0568] 1. When a user enters a specific question such as "Tell me the trick to defeating Monster X," the server receives this text data.

[0569] 2. Based on the received data, keywords such as "Monster X" and "tips for defeating it" are extracted.

[0570] 3. The generative AI model generates an initial answer using OpenAI's API.

[0571] 4. Query the database to retrieve relevant information provided by past users.

[0572] 5. Obtain the latest strategy information from external sites and integrate it as supplementary information.

[0573] 6. Provide the generated answer and additional information to the user.

[0574] Prompt Sentence Examples

[0575] If a user asks "What's the trick to defeating Monster X?", the server might send the following prompt to the generative AI model:

[0576] What are some tips for defeating Monster X?

[0577] By using this prompt, the server can generate optimal strategy information, allowing the user to efficiently acquire game strategy information and improve their gameplay.

[0578] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0579] Step 1:

[0580] A user enters game information into a chatbot app or web page.

[0581] Input: The question text from the user (e.g., "What's the trick to defeating Monster X?").

[0582] Output: User input data is sent to the server.

[0583] Step 2:

[0584] The server receives the data entered by the user.

[0585] Input: The user's question text arrives at the server.

[0586] Output: The received data is sent for analysis.

[0587] Step 3:

[0588] The server analyzes the text data from the user and extracts keywords for the "game title" and "information you want to know."

[0589] Input: The user's question text.

[0590] Data processing: Perform text analysis (natural language processing) and extract keywords.

[0591] Output: Extracted keywords (e.g. "Monster X", "Tips to defeat it").

[0592] Step 4:

[0593] The server passes the extracted keywords to the generative AI model, which then creates and sends prompt text to generate optimal strategy information.

[0594] Input: Extracted keywords.

[0595] Data calculation: Pass the prompt sentence to the generative AI model and generate an answer.

[0596] Output: Answer text from the generative AI model.

[0597] Specific operation: Generate a prompt sentence, "Please tell me some tips for defeating Monster X," and send it to the AI ​​model.

[0598] Step 5:

[0599] The server searches its own database to retrieve related strategy information that has been provided in the past.

[0600] Input: Extracted keywords.

[0601] Data processing: Perform database searches and obtain relevant strategy information.

[0602] Output: Past strategy information obtained.

[0603] Specific operation: Search for strategy information related to "Monster X" in the database and obtain the results.

[0604] Step 6:

[0605] The server uses external internet browsing functions to obtain the latest strategy information from external strategy sites and forums.

[0606] Input: Extracted keywords.

[0607] Data processing: Collect strategy information using web scraping and API access.

[0608] Output: The latest external exploit information obtained.

[0609] What it does: Perform a web search to get the latest information from trusted cheat sites.

[0610] Step 7:

[0611] The server integrates the generated answers, information from the database, and information from external sites to generate a final answer.

[0612] Input: Answer text from the generative AI model, past strategy information, and the latest external information.

[0613] Data arithmetic: content integration and organization.

[0614] Output: The final answer text.

[0615] What it does: Synthesizes all the information and creates a final answer in a user-friendly format.

[0616] Step 8:

[0617] The server sends the final consolidated answer to the user.

[0618] Input: Final answer text.

[0619] Output: The answer displayed on the user's terminal.

[0620] Specific operation: The final answer is sent to the user's device and displayed on the screen.

[0621] This allows the user to efficiently acquire game strategy information and improve their gameplay.

[0622] 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.

[0623] The present invention is a system that includes a user terminal for inputting game information, a server that receives input from the user and analyzes the information, a generative model that provides game strategy information based on the analyzed information, a database that stores strategy information provided by the user, a means for presenting answers to the user based on the stored strategy information and external information, and an emotion engine that recognizes the user's emotions. Each element of the system and the overall processing flow will be explained in detail below.

[0624] User device operation

[0625] The system starts when a user accesses a dedicated chatbot app or web page using a terminal. User terminals consist of various devices, including smartphones, tablets, and personal computers. Through the chatbot's UI, users can input the game title and strategy information they want to know. For example, they can make a request to the chatbot such as, "Tell me how to beat Boss B in Game A."

[0626] Server analysis and information generation

[0627] The server receives input data from the user and passes it to the analysis engine. The analysis engine extracts keywords related to the "game title" and "desired information" from the user's text. The server then uses a generative model to generate appropriate answers to the user's questions. The generative model uses natural language processing technology and utilizes large amounts of training data to generate walkthrough information with high accuracy.

[0628] Information Integration

[0629] In addition to the generated answers, the server searches its own database to retrieve walkthroughs and other data previously provided by users. The server also uses external internet browsing functions to gather the latest walkthrough information from walkthrough sites, forums, etc. This ensures that the most up-to-date and reliable information is provided to users.

[0630] The role of the emotional engine

[0631] Another feature of the present invention is the inclusion of an emotion engine, which uses text analysis and speech recognition technology to recognize the user's emotions. The server uses the emotion data obtained from the emotion engine to adjust the tone and content of the information provided by the generative model. For example, if the user expresses dissatisfaction, the server may add more detailed explanations or words of encouragement.

[0632] Displaying Information

[0633] The server sends the integrated strategy information and the content adjusted by the emotion engine to the user's device. The user receives a response through the chatbot, such as, "To efficiently defeat Boss B in Game A, it is recommended that you use specific skills and exploit the weaknesses that have been changed in the latest patch. Good luck!"

[0634] User input

[0635] When a user wants to provide new strategy information, they can enter it through the chatbot UI on their device. They can post new information in the form of, for example, "I tried out a newly discovered strategy and was able to easily defeat a specific boss."

[0636] Accumulation in the database

[0637] The server receives the new information, performs text analysis, and stores the analyzed information in its own database. The index is updated to reflect the new information and be used in future search results.

[0638] As a concrete example, if a user asks "How do I beat Mission D in Game C?", the server uses a generative model to generate the optimal strategy and adds an encouraging message based on the user's emotions (e.g., frustration) recognized by the emotion engine. As a result, the server provides an answer such as "Using a specific item is effective in beating Mission D. There may be some difficult parts, but don't give up! Try!"

[0639] The above is a detailed description of the embodiment of the present invention. This system allows users to efficiently obtain game strategy information and also provides appropriate support that takes into consideration the user's feelings.

[0640] The processing flow will be explained below.

[0641] Step 1:

[0642] The user launches a dedicated app or web page on their device, allowing them to view the chatbot's UI.

[0643] Step 2:

[0644] The user inputs the game title and the strategy information they want to know. For example, they input "Tell me how to beat boss B in game A."

[0645] Step 3:

[0646] The server receives input data from the user. The received text data is passed to an analysis engine, which extracts keywords (game title and strategy). The keywords obtained are "Game A" and "How to beat Boss B."

[0647] Step 4:

[0648] The server uses a generative model (using natural language processing technology) to generate an answer to the user's question. The generative model receives input such as "How to beat Boss B in Game A" and outputs the answer text.

[0649] Step 5:

[0650] The server searches its own database and retrieves relevant information about "Boss B in Game A" from the accumulated strategy information. For example, it can obtain information such as "To defeat Boss B, it is best to use a specific skill."

[0651] Step 6:

[0652] The server collects information from external internet sources, scraping strategy sites and forums to gather the latest strategy information. This is where it obtains information such as "Boss B's weakness has been changed in the latest patch."

[0653] Step 7:

[0654] The server aggregates generated answers, information from its own database, and information collected from external sources to check for consistency and reliability, and selects the most relevant information to provide to the user.

[0655] Step 8:

[0656] The server uses an emotion engine to analyze emotions from the text and voice data entered by the user, for example by analyzing the context of the input text and the tone of the voice to determine whether the user is annoyed or satisfied.

[0657] Step 9:

[0658] The server adjusts the tone and content of the response based on the user's emotional data analyzed by the emotion engine. For example, if the user is frustrated, the server adjusts the response by adding more detailed explanations or encouraging words.

[0659] Step 10:

[0660] The server then sends the final adjusted answer to the user's device. The user receives specific strategy information, such as, "To efficiently defeat Boss B in Game A, it is recommended that you use specific skills and exploit the weaknesses that were changed in the latest patch. Good luck!"

[0661] Step 11:

[0662] When a user wants to provide new strategy information, they input the information through the chatbot UI on their device. The chatbot posts, "I've discovered a new strategy for defeating Boss B."

[0663] Step 12:

[0664] The server receives new posts, performs text analysis, stores the analyzed information in its own database, and updates the index to reflect the new information and ensure it is reflected in future search results.

[0665] The above is the specific processing flow of this system, which allows users to efficiently obtain game strategy information and receive adaptive support based on the user's emotions.

[0666] Example 2

[0667] Next, a description will be given of 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."

[0668] Conventional game strategy information providing systems may not always be able to provide the specific strategy information desired by the user, and may also lack consideration for the user's feelings. This raises concerns that the quality of the user experience may be reduced. The present invention aims to provide a system that efficiently provides users with game strategy information and provides appropriate support that takes the user's feelings into consideration.

[0669] 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.

[0670] In this invention, the server includes means for receiving input from a user and analyzing the information, a generative model means for providing game strategy information based on the analyzed information, and a means for presenting answers to the user based on the accumulated strategy information and external information. This makes it possible to quickly provide specific and up-to-date strategy information that the user desires. Furthermore, by using an emotion engine means, it is possible to respond according to the user's emotions, improving the quality of the user experience.

[0671] "Game information" refers to any data or information that a user has relating to a game, and specifically includes strategies for winning, strategies for defeating boss characters, details of missions, and the like.

[0672] "User terminal" refers to a device used by a user to search for and input game information, including smartphones, tablets, personal computers, etc.

[0673] "Server" refers to a computer system that receives information sent by a user, analyzes it, and provides the necessary data.

[0674] A "generative model" refers to an algorithm or program for generating game strategy information in response to a user's request, and generally uses natural language processing technology.

[0675] "Database" refers to a system that stores strategy information provided by users and information collected from external sources, and prepares it for later retrieval or access.

[0676] "External information" refers to new information that has not been stored in the database but has been collected from online strategy sites, forums, etc.

[0677] An "emotion engine" refers to technology or a program that analyzes user input data and voice data and identifies the emotion it conveys.

[0678] "Chatbot UI" refers to an interactive user interface that allows users to input questions or requests and receive responses from the system.

[0679] The present invention is a system that includes a user terminal for inputting game information, a server that receives input from users and analyzes the information, a generation model that provides game strategy information based on the analyzed information, a database that stores strategy information provided by users, a means for presenting answers to users based on the stored strategy information and external information, and an emotion engine that recognizes the user's emotions. Each element of the system and the overall processing flow will be explained in detail below.

[0680] User terminal operation

[0681] The system starts when a user accesses a dedicated chatbot app or web page using a terminal. User terminals consist of various devices, including smartphones, tablets, and personal computers. Through the chatbot's UI, users can input the game title and strategy information they want to know. For example, they can make a request to the chatbot such as, "Tell me how to beat Boss B in Game A."

[0682] Server analysis and information generation

[0683] The server receives input data from users and passes it to an analysis engine. The analysis engine extracts keywords related to the "game title" and "desired information" from the user's text. The server then uses a generative model to generate appropriate answers to the user's questions. The generative model uses natural language processing technology and utilizes large amounts of training data to generate walkthrough information with high accuracy.

[0684] Information Integration

[0685] In addition to the generated answers, the server searches its own database to retrieve walkthroughs and other data previously provided by users. The server also uses external internet browsing functions to gather the latest walkthrough information from walkthrough sites, forums, etc. This ensures that users are provided with the most up-to-date and reliable information.

[0686] The role of the emotional engine

[0687] Another feature of the present invention is the inclusion of an emotion engine. The emotion engine uses text analysis and speech recognition technology to recognize the user's emotions. The server uses the emotion data obtained from the emotion engine to adjust the tone and content of the information provided by the generative model. For example, if the user expresses dissatisfaction, the server may add more detailed explanations or words of encouragement.

[0688] Displaying Information

[0689] The server sends the integrated strategy information and content adjusted by the emotion engine to the user's device. The user receives a response through the chatbot, such as, "To efficiently defeat Boss B in Game A, it is recommended that you use specific skills and exploit the weaknesses that have been changed in the latest patch. Good luck!"

[0690] Information provided by users

[0691] When users want to provide new strategy information, they can enter it through the chatbot UI on their device. They can post new information in the form of, for example, "I tried out a newly discovered strategy and was able to easily defeat a particular boss."

[0692] Accumulation in the database

[0693] The server receives the new information, performs text analysis, and stores the analyzed information in its own database. The index is updated to reflect the new information and be used in future search results.

[0694] As a concrete example, if a user asks "How do I beat Mission D in Game C?", the server uses a generative model to generate the optimal strategy and adds an encouraging message based on the user's emotions (e.g., frustration) recognized by the emotion engine. As a result, the server provides an answer such as, "Using a specific item is effective in beating Mission D. There may be some difficult parts, but don't give up! Keep trying!"

[0695] Prompt Sentence Examples

[0696] For example, the prompt you would enter into a generative AI model might look like this:

[0697] A user asks, "Tell me how to complete Mission D in Game C." According to the emotion engine, the user seems to be frustrated. Please generate an answer that provides information on how to complete Mission D and encourages the user.

[0698] Using this prompt, the generative AI model generates an answer that takes into account the user's best information and emotions.

[0699] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0700] Step 1:

[0701] A user accesses a chatbot app or web page using a device

[0702] Input: The device starts up and opens a dedicated app or web page.

[0703] How it works: A user turns on a device such as a smartphone, tablet, or personal computer and accesses the dedicated app "GameHelper" or a web page.

[0704] Output: The home screen of the chatbot app or web page is displayed.

[0705] Step 2:

[0706] User requests walkthrough information from chatbot UI

[0707] Input: The user types a request for a specific game title or gameplay information into the chatbot's input field.

[0708] How it works: A user types a request into a chatbot, such as "Tell me how to beat boss B in game A."

[0709] Output: The user's request data is sent to the server.

[0710] Step 3:

[0711] The server receives user input and passes it to the analysis engine

[0712] Input: The server receives the request data from the user.

[0713] How it works: The server passes the request to the analysis engine, which extracts keywords for the "game title" and "information you want to know."

[0714] Output: Keywords such as "Game A" and "Boss B" are extracted.

[0715] Step 4:

[0716] The server generates and inputs prompts into the generative model.

[0717] Input: A prompt sentence based on the extracted keywords.

[0718] How it works: The server generates and inputs a prompt to a generative AI model (e.g., GPT-3). The prompt is "Tell me how to beat boss B in game A."

[0719] Output: A prompt for the generative model to answer.

[0720] Step 5:

[0721] The generative model generates the appropriate answer

[0722] Input: A generative model given a prompt sentence.

[0723] How it works: The generative model generates highly accurate strategy information based on the prompt sentence.

[0724] Output: An answer such as "Using a specific skill is effective in defeating Boss B."

[0725] Step 6:

[0726] The server integrates its own database with external information

[0727] Input: Answers from the generative model, proprietary databases, and information from external sites.

[0728] How it works: In addition to the generated answers, the server also integrates past walkthroughs from the database and the latest information from external sites.

[0729] Output: Consolidated and reliable strategy information.

[0730] Step 7:

[0731] The server analyzes the user's emotions using an emotion engine.

[0732] Input: User input data.

[0733] How it works: The emotion engine uses text and speech analysis to identify the user's emotions.

[0734] Output: User emotion (e.g., annoyance).

[0735] Step 8:

[0736] The server uses emotional data to tailor responses

[0737] Input: Generated strategy information and user emotion data.

[0738] How it works: The server uses the emotion data to adjust the tone and content of the generated responses.

[0739] Output: An answer such as "Using specific skills is effective in defeating Boss B. Also, don't give up, keep trying!"

[0740] Step 9:

[0741] The server sends the generated answer to the user's device.

[0742] Input: Adjusted response data.

[0743] Operation: The server sends the optimal strategy information to the user's device.

[0744] Output: Walkthrough information displayed on the user's device.

[0745] Step 10:

[0746] The user receives the information on the device

[0747] Input: The response data sent from the server.

[0748] How it works: The user receives the answer through the chatbot UI on their device.

[0749] Output: Strategy information displayed in the chat window.

[0750] Step 11:

[0751] Users provide new strategy information

[0752] Input: User data to input new walkthrough information.

[0753] How it works: A user posts a new strategy through the chatbot UI.

[0754] Output: New cheats from the user.

[0755] Step 12:

[0756] The server receives and analyzes the new information.

[0757] Input: New cheats provided by the user.

[0758] How it works: The server receives new exploit information and analyzes it using the analysis engine.

[0759] Output: New parsed exploit data.

[0760] Step 13:

[0761] The server stores the analyzed information in a database

[0762] Input: Parsed new exploit data.

[0763] How it works: The server stores the new information in its own database and updates the index so that it is reflected in future searches.

[0764] Output: An updated database with the latest cheats.

[0765] (Application example 2)

[0766] Next, a description will be given of Application 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."

[0767] A problem with conventional user information provision systems is that they provide uniform information without considering the user's emotions. This can lead to lower user satisfaction and the inability to receive optimal support. Furthermore, in virtual stores, customer support that does not respond to the customer's emotions can lead to a decrease in purchasing motivation.

[0768] 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.

[0769] In this invention, the server includes an emotion engine that analyzes the user's emotional state, means for adjusting responses based on the analysis results of the emotion engine, and means for analyzing user input and generating optimal information using a generative model, thereby making it possible to provide optimal information and support according to the user's emotions.

[0770] A "user terminal" is a device used by a user to input information, and includes a smartphone, tablet, personal computer, etc.

[0771] A "server" is a computer whose role is to receive input from users and analyze the information.

[0772] A "generative model" is an artificial intelligence model that uses natural language processing technology to provide appropriate answers and information based on analyzed information.

[0773] A "database" is an information management system that stores user-provided information and makes it available for later searches and queries.

[0774] The "emotion engine" is an analysis engine that has the function of analyzing the user's emotional state and adjusting the response content based on the results.

[0775] "Means of presenting information" refers to the functions and interfaces that allow the server to provide optimal information to users based on accumulated data and external information.

[0776] The "means for adjusting the response" is a function for appropriately adjusting the content of the response to the user based on the analysis results of the emotion engine, and providing optimal support and information.

[0777] "Natural language processing technology" is an artificial intelligence technology for understanding input text from users and generating appropriate responses.

[0778] A "proprietary database" is a database owned by the system that stores information obtained from users and external information.

[0779] The "external Internet browsing function" is a function that allows the server to collect external data and information via the Internet.

[0780] The present invention realizes the provision of information that takes into consideration the user's emotions by constructing a customer support system that includes a user terminal, a server, a generative model, a database, an emotion engine, and an information presentation means.

[0781] System configuration

[0782] User device operation

[0783] Users access a dedicated application or web page using a user device such as a smartphone, tablet, or personal computer and enter information. For example, the system starts by sending a request such as, "Please tell me more about the newly released smartphone."

[0784] Server Features

[0785] The server receives input data from the user and passes it to the analysis engine. The analysis engine extracts keywords related to the "question" and "related information" from the user's text. The generative model then generates the most appropriate answer. This generative model uses natural language processing technology to provide answers to the user's questions with high accuracy.

[0786] The role of the emotional engine

[0787] The emotion engine uses text analysis and speech recognition technology to recognize the user's emotional state. The server then adjusts the tone and content of the information generated by the generative model based on the emotional data obtained from the emotion engine. For example, if the user is excited, the server adds a positive response with detailed information.

[0788] Information Integration

[0789] In addition to the generated answers, the server searches its own database to retrieve information previously provided by users and external information, and also uses external internet browsing functions to gather the latest relevant information, allowing it to provide the most reliable and up-to-date information.

[0790] Displaying Information

[0791] The server sends the integrated information to the user's device, and the user can receive the information through the chatbot. For example, a response such as "The newly released smartphone has advanced camera functions and is in stock. We are also running a special campaign!" is provided.

[0792] About program processing

[0793] The server operates using the following processing means:

[0794] Emotion engine that analyzes emotional states: Analyzes emotions from user text using OpenAI's API.

[0795] How responses are tailored: Generative AI models are used based on the analysis results to generate optimal responses for the user.

[0796] Means of analyzing input and generating information: A live AI model using natural language processing techniques is used to generate appropriate answers to user questions.

[0797] Examples of concrete examples and prompts

[0798] For example, if a user sends a request such as "Tell me more about the new smartphone," the server analyzes the user's emotional state and uses a generative model to generate the best answer. Here is an example prompt:

[0799] A customer asked the following question: Can you tell me more about the newly launched smartphone?

[0800] The customer's emotion is excitement. Generate an appropriate response.

[0801] This results in a response like, "Our new smartphone has advanced camera features and is in stock. We're also running a special promotion!"

[0802] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0803] Step 1:

[0804] A user accesses a dedicated application or web page using a user terminal such as a smartphone or personal computer and inputs information. An example of input data is a request such as "Please tell me more about the newly released smartphone." This request is sent to the server.

[0805] Step 2:

[0806] The server receives input data from the user. It passes this received data to the analysis engine and begins analysis. The analysis engine extracts keywords for the "question content" and "related information" from the user's text. For example, the keyword "newly released smartphone" is extracted from the input data "Please tell me more about the newly released smartphone."

[0807] Step 3:

[0808] The server uses a generative model based on the analysis results to generate the optimal answer. The generative AI model uses natural language processing technology to generate the optimal answer from the extracted keywords. The prompt sentence in this case will be in the form below.

[0809] A customer asked the following question: Can you tell me more about the newly launched smartphone?

[0810] The customer's emotion is excitement. Generate an appropriate response.

[0811] Step 4:

[0812] In addition to the generated answer, the server searches its own database to retrieve information previously provided by the user and the latest related information. It also uses an external internet browsing function to collect the latest related information. This process retrieves data related to "new smartphones" from its own database and the latest information from external websites.

[0813] Step 5:

[0814] The emotion engine analyzes user input data to recognize their emotional state. For example, a request like "Tell me more about the new smartphone" might be interpreted as indicating that the user is excited.

[0815] Step 6:

[0816] The server then adjusts the responses generated by the generative model based on the analysis results of the emotion engine. For example, if the user is excited, a proactive and detailed response will be generated. This process is performed to adapt the content of the response to the user's emotional state.

[0817] Step 7:

[0818] The server sends the integrated information to the user's device, where it is displayed on the screen, and the user receives a response from the chatbot: "The newly released smartphone has advanced camera functions, is in stock, and we're also running a special campaign!"

[0819] In this way, a system is realized that takes into consideration the user's feelings and can provide appropriate and timely information.

[0820] 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.

[0821] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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.

[0822] 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.

[0823] [Third embodiment]

[0824] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0825] 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.

[0826] 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).

[0827] 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.

[0828] 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.

[0829] 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).

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0836] The present invention is a system that includes a user terminal for inputting game information, a server that receives input from the user and analyzes the information, a generation model that provides game strategy information based on the analyzed information, a database that stores strategy information provided by the user, and a means for presenting answers to the user based on the stored strategy information and external information. Each element of the system and the overall processing flow will be explained in detail below.

[0837] The system starts when a user accesses a dedicated chatbot app or web page using a device. The device can be a smartphone, tablet, or personal computer, and can input the game title and strategy information they want to know into the chatbot. For example, they can input information in the form of "I want to know how to beat a specific boss in a certain game."

[0838] The server receives and analyzes the data entered by the user. Specifically, it analyzes the text data from the user and extracts keywords related to the "game title" and "desired information." The server then uses a generative model to generate appropriate answers to the user's questions. This generative model uses natural language processing technology and utilizes large amounts of training data to generate walkthrough information with high accuracy.

[0839] In addition to the generated answers, the server searches its own database to retrieve previously provided walkthroughs and other accumulated data. The server also uses external internet browsing to gather data from walkthrough sites, forums, etc. to obtain the latest walkthrough information. This ensures that the most up-to-date and reliable information is provided to the user.

[0840] For example, if a user asks "How can I efficiently level up in a certain game?", the server will analyze the question through a generative model and suggest "the best way to level up in a specific mission or dungeon." It will also search the server's database and integrate useful information provided by other users and additional strategy information collected from the internet. For example, it can provide specific strategies such as "Repeatedly fighting in a specific location is efficient."

[0841] Furthermore, if a user wants to provide new strategy information, they can input the information from their device and send it to the server. The server analyzes this new information and stores it in its own database. This makes it possible for other users to provide answers based on the latest information when they ask similar questions in the future.

[0842] This system allows users to efficiently acquire game strategy information and share their knowledge to help other users, resulting in smoother and more enjoyable gameplay.

[0843] The processing flow will be explained below.

[0844] Step 1:

[0845] The user launches a dedicated app or web page on their device, allowing them to view the chatbot's UI.

[0846] Step 2:

[0847] The user inputs the game title and the strategy information they want to know. For example, they input "Tell me how to beat boss B in game A."

[0848] Step 3:

[0849] The server receives input from the user. The received text data is passed to an analysis engine, which extracts keywords (game title and strategy). The keywords obtained are "Game A" and "How to beat Boss B."

[0850] Step 4:

[0851] The server uses a generative model (using natural language processing technology) to generate an answer to the user's question. The generative model receives input such as "How to beat Boss B in Game A" and outputs the answer text.

[0852] Step 5:

[0853] The server searches its own database and retrieves relevant information about "Boss B in Game A" from the accumulated strategy information. For example, it can obtain information such as "To defeat Boss B, it is best to use a specific skill."

[0854] Step 6:

[0855] The server collects information from external internet sources, scraping strategy sites and forums to gather the latest strategy information. This is where it obtains information such as "Boss B's weakness has been changed in the latest patch."

[0856] Step 7:

[0857] The server aggregates generated answers, information from its own database, and information collected from external sources to check for consistency and reliability, and selects the most relevant information to provide to the user.

[0858] Step 8:

[0859] The server then compiles the final answer and sends it to the user's device. The user receives specific strategy information, such as, "To efficiently defeat Boss B in Game A, it is recommended that you use a specific skill and exploit a weakness that was changed in the latest patch."

[0860] Step 9:

[0861] When a user wants to provide new strategy information, they input the information through the chatbot UI on their device. The chatbot posts, "I've discovered a new strategy for defeating Boss B."

[0862] Step 10:

[0863] The server receives new posts, performs text analysis, and stores the analyzed information in its own database. The new information is then reflected in search results from the next time onwards.

[0864] The above is the specific processing flow of this system, which allows for efficient acquisition of game strategy information and promotes information sharing among users.

[0865] Example 1

[0866] Next, a description will be given of 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."

[0867] In order to provide users with game strategy information efficiently and accurately, a system is needed that can analyze the information entered by the user, generate appropriate strategy information, and even integrate and present it with external information. However, conventional systems have difficulty in quickly and accurately obtaining the strategy information users need, and also lack the means to accumulate new information.

[0868] 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.

[0869] In this invention, the server includes means for receiving input from users and analyzing the information, means for providing a data generation model that provides game strategy information based on the analyzed information, and means for searching a data storage device that stores strategy information provided by users and for using information collected from outside. This allows users to efficiently obtain the strategy information they need and further add their own knowledge to the system.

[0870] "Game information" refers to data and strategy information related to the game that the user is playing.

[0871] "Information terminal" refers to devices used by users, such as smartphones, tablets, and personal computers.

[0872] "Processing device" refers to a computer or server that analyzes data received from a user and performs the necessary processing.

[0873] A "data generation model" refers to an algorithm or software that generates game strategy information based on input data.

[0874] "Data storage device" refers to a database or storage medium for storing strategy information provided by users.

[0875] "Externally collected information" refers to the latest strategy information obtained from the Internet and other sources.

[0876] "Natural language processing technology" refers to computer technology for understanding, analyzing, and generating human language.

[0877] A "generative AI model" is an artificial intelligence model that has been trained in advance with large amounts of data and uses natural language processing techniques to generate appropriate information from input data.

[0878] MODE FOR CARRYING OUT THE INVENTION

[0879] The present invention is a system that allows users to efficiently and accurately acquire game strategy information. This system is composed of a user terminal, a server, a data generation model, a data storage device, and external information collection means. Each element and processing flow of this system will be described in detail below.

[0880] User terminal

[0881] User terminals include devices such as smartphones, tablets, and personal computers. Users access a dedicated chatbot app or web page and input game strategy information. For example, they might input something like, "I want to know how to beat a specific boss in a specific game."

[0882] server

[0883] The server is a device that receives and analyzes data entered by users. The server analyzes the received text data and extracts keywords related to the "game title" and "desired information." The analysis uses natural language processing technology.

[0884] Data Generation Model

[0885] A data generation model is an algorithm or software that generates game strategy information based on analyzed keywords. This model is highly trained using large amounts of training data and generates appropriate answers to user questions. For example, it can provide the "optimal strategy for a specific mission or dungeon."

[0886] Data Storage Device

[0887] The data storage device includes a database and storage media for storing strategy information provided by users. The server can search and retrieve past strategy information in addition to the answers of the generative model.

[0888] External information gathering means

[0889] The server can collect the latest strategy information via the external Internet. It obtains data from strategy sites and forums and provides users with reliable, up-to-date information. With this function, the system can always provide answers to users based on the latest information.

[0890] Specific examples

[0891] For example, if a user types "Tell me how to beat Monster X" into the terminal, the specific actions are as follows:

[0892] 1. User device: The user types in "Tell me how to defeat Monster X" and presses the send button.

[0893] 2. Server: Analyzes the received text "Teach me how to defeat Monster X" and extracts "Monster X" and "how to defeat" as keywords.

[0894] 3. Data generation model: Based on the extracted keywords, strategy information is generated from the training data, and specific strategy information such as "Monster X is weak against water-element attacks. It is best to attack with a water-element weapon" is provided.

[0895] 4. Data storage device: Search the database for information related to "Monster X" provided by past users.

[0896] 5. External information gathering methods: Collect the latest strategy information from the Internet.

[0897] 6. Integration and presentation: All collected information is integrated and displayed on the user's device.

[0898] Prompt Sentence Examples

[0899] Here are some examples of prompts for generative AI models:

[0900] "Generate the best answer when a user asks how to defeat Monster X."

[0901] With the above configuration and operation, this system can provide users with efficient and accurate game strategy information, and also promote the sharing of knowledge among users.

[0902] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0903] System processing flow

[0904] Step 1:

[0905] A user accesses a chatbot app or web page from a device

[0906] What happens: A user accesses a compatible chatbot app or web page using a device such as a smartphone, tablet, or personal computer.

[0907] Input: User actions to access the chatbot app or web page

[0908] Output: The app or web page is launched and the interface is presented to the user.

[0909] Step 2:

[0910] The user inputs the strategy information, and the device sends the data to the server.

[0911] Specific operation: The user inputs the game title and specific strategy information into the displayed interface. Once input is complete, the device sends this data to the server. For example, the user might input "Tell me how to beat a specific boss."

[0912] Input: Strategy information entered by the user (text data)

[0913] Output: The entered data is sent to the server.

[0914] Step 3:

[0915] The server analyzes the received data and extracts keywords.

[0916] Specific operation: The server analyzes the received text data and extracts keywords related to the "game title" and "information you want to know." This analysis uses natural language processing technology.

[0917] Input: Text data sent by the user

[0918] Output: Extracted keywords (game title, desired information)

[0919] Step 4:

[0920] The server uses the generative model to generate the appropriate answer

[0921] How it works: Based on the extracted keywords, the server uses a generative model to generate answers to the user's questions. The generative model is trained on a large amount of training data.

[0922] Input: Extracted keywords

[0923] Output: Generated answer (specific strategy information)

[0924] Step 5:

[0925] The server searches its own database to obtain relevant information

[0926] Specific operation: In addition to the answer of the generative model, the server searches and retrieves the accumulated past strategy information from the database, which allows it to provide more reference information.

[0927] Input: Generated answers, database index information

[0928] Output: Related strategy information accumulated in the past

[0929] Step 6:

[0930] The server collects the latest exploit information from the external Internet.

[0931] Specific operation: The server uses external internet browsing functions to collect the latest cheat information from cheat sites and forums. The collected data is analyzed and only reliable information is extracted.

[0932] Input: Raw exploit information collected from external sources

[0933] Output: Analyzed and reliable strategy information

[0934] Step 7:

[0935] The server consolidates all the information it has acquired and provides the answer to the user.

[0936] Specific operation: The server integrates the generative model's answers, accumulated information, and externally collected data to provide the user with the most appropriate answer. For example, it may provide information such as "Using a weapon with a specific attribute is effective."

[0937] Input: Generated answers, historical information, externally collected information

[0938] Output: Integrated strategy information presented to the user

[0939] Step 8:

[0940] The user inputs new strategy information from the terminal, and the server stores the information in the database.

[0941] Specific operation: The user inputs and submits new strategy information from their device. The server analyzes this new information and stores it in the database. This allows the server to provide more accurate information when a similar question is asked in the future.

[0942] Input: New strategy information provided by the user

[0943] Output: New strategy information analyzed and stored in the database

[0944] Through the above steps, the system provides users with efficient and accurate game strategy information and promotes knowledge sharing among users.

[0945] (Application example 1)

[0946] Next, a description will be given of Application 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."

[0947] With the large number of games available on the market today, players need a way to quickly obtain game strategy information for each game and improve their gameplay based on that information. However, game strategy information is scattered, making it difficult to guarantee its reliability and up-to-dateness. Therefore, there is a need for a system that can provide game strategy information efficiently and reliably.

[0948] 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.

[0949] In this invention, the server includes means for receiving questions from users and generating optimal walkthrough information using a generative AI model, means for acquiring and integrating the latest information from an accumulated database and external sites, and means for presenting answers to users based on the accumulated walkthrough information and external information, thereby enabling users to quickly obtain the latest and most reliable walkthrough information.

[0950] "User terminal" refers to any device that a user uses to input game information.

[0951] A "server" is a device or system that receives input from a user and analyzes the information.

[0952] A "generative model" is a model that provides game strategy information based on analyzed information, and in particular makes use of natural language processing technology.

[0953] The "database" is an information repository for storing and managing strategy information provided by users.

[0954] A "means" is a method or apparatus for performing a particular function or role.

[0955] The "means for accepting a question" refers to a method or device by which the system accepts a question input from a user.

[0956] "Means for generating optimal strategy information using a generative AI model" refers to a method or device for generating optimal answers to user questions using AI technology.

[0957] The "means for acquiring and integrating the latest information" refers to a method or device for acquiring the latest strategy information from databases and external sites and integrating it.

[0958] The "means for presenting an answer to the user" is a method or device for providing an appropriate answer to the user based on the accumulated walkthrough information and the acquired external information.

[0959] The present invention provides a system that allows users to efficiently obtain game strategy information and store their own knowledge in a database. Below, each element of the system and the overall processing flow will be explained in detail.

[0960] User terminal

[0961] The system of the present invention includes a user terminal through which a user inputs game information. This user terminal can be any device, such as a smartphone, tablet, or personal computer. Using the terminal, a user accesses a dedicated chatbot app or web page and inputs questions and strategy information about the game.

[0962] server

[0963] The server receives input from users and analyzes the information. Specifically, it analyzes the text data from the user and extracts keywords related to the "game title" and "information the user wants to know." The server then uses a generative AI model to generate appropriate answers to the user's questions. This generative AI model uses natural language processing technology and utilizes large amounts of training data. For example, answers generated using OpenAI's API are utilized.

[0964] Database

[0965] The server has a database that stores strategy information provided by users. This database stores strategy information provided in the past and related information collected from other sources. The server also has a search function for its own database and an external Internet browsing function, and obtains the latest information from external strategy sites and forums and adds it to the database.

[0966] Generative AI Models

[0967] The generative AI model is an element that generates optimal strategy information based on the content of questions from users. The generative AI model aims to provide highly accurate and reliable information, and uses natural language processing technology to analyze the content of questions and generate appropriate answers. This model can use, for example, OpenAI's API.

[0968] Server processing flow

[0969] 1. When a user enters a specific question such as "Tell me the trick to defeating Monster X," the server receives this text data.

[0970] 2. Based on the received data, keywords such as "Monster X" and "tips for defeating it" are extracted.

[0971] 3. The generative AI model generates an initial answer using OpenAI's API.

[0972] 4. Query the database to retrieve relevant information provided by past users.

[0973] 5. Obtain the latest strategy information from external sites and integrate it as supplementary information.

[0974] 6. Provide the generated answer and additional information to the user.

[0975] Prompt Sentence Examples

[0976] If a user asks "What's the trick to defeating Monster X?", the server might send the following prompt to the generative AI model:

[0977] What are some tips for defeating Monster X?

[0978] By using this prompt, the server can generate optimal strategy information, allowing the user to efficiently acquire game strategy information and improve their gameplay.

[0979] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0980] Step 1:

[0981] A user enters game information into a chatbot app or web page.

[0982] Input: The question text from the user (e.g., "What's the trick to defeating Monster X?").

[0983] Output: User input data is sent to the server.

[0984] Step 2:

[0985] The server receives the data entered by the user.

[0986] Input: The user's question text arrives at the server.

[0987] Output: The received data is sent for analysis.

[0988] Step 3:

[0989] The server analyzes the text data from the user and extracts keywords for the "game title" and "information you want to know."

[0990] Input: The user's question text.

[0991] Data processing: Perform text analysis (natural language processing) and extract keywords.

[0992] Output: Extracted keywords (e.g. "Monster X", "Tips to defeat it").

[0993] Step 4:

[0994] The server passes the extracted keywords to the generative AI model, which then creates and sends prompt text to generate optimal strategy information.

[0995] Input: Extracted keywords.

[0996] Data calculation: Pass the prompt sentence to the generative AI model and generate an answer.

[0997] Output: Answer text from the generative AI model.

[0998] Specific operation: Generate a prompt sentence, "Please tell me some tips for defeating Monster X," and send it to the AI ​​model.

[0999] Step 5:

[1000] The server searches its own database to retrieve related strategy information that has been provided in the past.

[1001] Input: Extracted keywords.

[1002] Data processing: Perform database searches and obtain relevant strategy information.

[1003] Output: Past strategy information obtained.

[1004] Specific operation: Search for strategy information related to "Monster X" in the database and obtain the results.

[1005] Step 6:

[1006] The server uses external internet browsing functions to obtain the latest strategy information from external strategy sites and forums.

[1007] Input: Extracted keywords.

[1008] Data processing: Collect strategy information using web scraping and API access.

[1009] Output: The latest external exploit information obtained.

[1010] What it does: Perform a web search to get the latest information from trusted cheat sites.

[1011] Step 7:

[1012] The server integrates the generated answers, information from the database, and information from external sites to generate a final answer.

[1013] Input: Answer text from the generative AI model, past strategy information, and the latest external information.

[1014] Data arithmetic: content integration and organization.

[1015] Output: The final answer text.

[1016] What it does: Synthesizes all the information and creates a final answer in a user-friendly format.

[1017] Step 8:

[1018] The server sends the final consolidated answer to the user.

[1019] Input: Final answer text.

[1020] Output: The answer displayed on the user's terminal.

[1021] Specific operation: The final answer is sent to the user's device and displayed on the screen.

[1022] This allows the user to efficiently acquire game strategy information and improve their gameplay.

[1023] 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.

[1024] The present invention is a system that includes a user terminal for inputting game information, a server that receives input from the user and analyzes the information, a generative model that provides game strategy information based on the analyzed information, a database that stores strategy information provided by the user, a means for presenting answers to the user based on the stored strategy information and external information, and an emotion engine that recognizes the user's emotions. Each element of the system and the overall processing flow will be explained in detail below.

[1025] User device operation

[1026] The system starts when a user accesses a dedicated chatbot app or web page using a terminal. User terminals consist of various devices, including smartphones, tablets, and personal computers. Through the chatbot's UI, users can input the game title and strategy information they want to know. For example, they can make a request to the chatbot such as, "Tell me how to beat Boss B in Game A."

[1027] Server analysis and information generation

[1028] The server receives input data from the user and passes it to the analysis engine. The analysis engine extracts keywords related to the "game title" and "desired information" from the user's text. The server then uses a generative model to generate appropriate answers to the user's questions. The generative model uses natural language processing technology and utilizes large amounts of training data to generate walkthrough information with high accuracy.

[1029] Information Integration

[1030] In addition to the generated answers, the server searches its own database to retrieve walkthroughs and other data previously provided by users. The server also uses external internet browsing functions to gather the latest walkthrough information from walkthrough sites, forums, etc. This ensures that the most up-to-date and reliable information is provided to users.

[1031] The role of the emotional engine

[1032] Another feature of the present invention is the inclusion of an emotion engine, which uses text analysis and speech recognition technology to recognize the user's emotions. The server uses the emotion data obtained from the emotion engine to adjust the tone and content of the information provided by the generative model. For example, if the user expresses dissatisfaction, the server may add more detailed explanations or words of encouragement.

[1033] Displaying Information

[1034] The server sends the integrated strategy information and the content adjusted by the emotion engine to the user's device. The user receives a response through the chatbot, such as, "To efficiently defeat Boss B in Game A, it is recommended that you use specific skills and exploit the weaknesses that have been changed in the latest patch. Good luck!"

[1035] User input

[1036] When a user wants to provide new strategy information, they can enter it through the chatbot UI on their device. They can post new information in the form of, for example, "I tried out a newly discovered strategy and was able to easily defeat a specific boss."

[1037] Accumulation in the database

[1038] The server receives the new information, performs text analysis, and stores the analyzed information in its own database. The index is updated to reflect the new information and be used in future search results.

[1039] As a concrete example, if a user asks "How do I beat Mission D in Game C?", the server uses a generative model to generate the optimal strategy and adds an encouraging message based on the user's emotions (e.g., frustration) recognized by the emotion engine. As a result, the server provides an answer such as "Using a specific item is effective in beating Mission D. There may be some difficult parts, but don't give up! Try!"

[1040] The above is a detailed description of the embodiment of the present invention. This system allows users to efficiently obtain game strategy information and also provides appropriate support that takes into consideration the user's feelings.

[1041] The processing flow will be explained below.

[1042] Step 1:

[1043] The user launches a dedicated app or web page on their device, allowing them to view the chatbot's UI.

[1044] Step 2:

[1045] The user inputs the game title and the strategy information they want to know. For example, they input "Tell me how to beat boss B in game A."

[1046] Step 3:

[1047] The server receives input data from the user. The received text data is passed to an analysis engine, which extracts keywords (game title and strategy). The keywords obtained are "Game A" and "How to beat Boss B."

[1048] Step 4:

[1049] The server uses a generative model (using natural language processing technology) to generate an answer to the user's question. The generative model receives input such as "How to beat Boss B in Game A" and outputs the answer text.

[1050] Step 5:

[1051] The server searches its own database and retrieves relevant information about "Boss B in Game A" from the accumulated strategy information. For example, it can obtain information such as "To defeat Boss B, it is best to use a specific skill."

[1052] Step 6:

[1053] The server collects information from external internet sources, scraping strategy sites and forums to gather the latest strategy information. This is where it obtains information such as "Boss B's weakness has been changed in the latest patch."

[1054] Step 7:

[1055] The server aggregates generated answers, information from its own database, and information collected from external sources to check for consistency and reliability, and selects the most relevant information to provide to the user.

[1056] Step 8:

[1057] The server uses an emotion engine to analyze emotions from the text and voice data entered by the user, for example by analyzing the context of the input text and the tone of the voice to determine whether the user is annoyed or satisfied.

[1058] Step 9:

[1059] The server adjusts the tone and content of the response based on the user's emotional data analyzed by the emotion engine. For example, if the user is frustrated, the server adjusts the response by adding more detailed explanations or encouraging words.

[1060] Step 10:

[1061] The server then sends the final adjusted answer to the user's device. The user receives specific strategy information, such as, "To efficiently defeat Boss B in Game A, it is recommended that you use specific skills and exploit the weaknesses that were changed in the latest patch. Good luck!"

[1062] Step 11:

[1063] When a user wants to provide new strategy information, they input the information through the chatbot UI on their device. The chatbot posts, "I've discovered a new strategy for defeating Boss B."

[1064] Step 12:

[1065] The server receives new posts, performs text analysis, stores the analyzed information in its own database, and updates the index to reflect the new information and ensure it is reflected in future search results.

[1066] The above is the specific processing flow of this system, which allows users to efficiently obtain game strategy information and receive adaptive support based on the user's emotions.

[1067] Example 2

[1068] Next, a description will be given of 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."

[1069] Conventional game strategy information providing systems may not always be able to provide the specific strategy information desired by the user, and may also lack consideration for the user's feelings. This raises concerns that the quality of the user experience may be reduced. The present invention aims to provide a system that efficiently provides users with game strategy information and provides appropriate support that takes the user's feelings into consideration.

[1070] 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.

[1071] In this invention, the server includes means for receiving input from a user and analyzing the information, a generative model means for providing game strategy information based on the analyzed information, and a means for presenting answers to the user based on the accumulated strategy information and external information. This makes it possible to quickly provide specific and up-to-date strategy information that the user desires. Furthermore, by using an emotion engine means, it is possible to respond according to the user's emotions, improving the quality of the user experience.

[1072] "Game information" refers to any data or information that a user has relating to a game, and specifically includes strategies for winning, strategies for defeating boss characters, details of missions, and the like.

[1073] "User terminal" refers to a device used by a user to search for and input game information, including smartphones, tablets, personal computers, etc.

[1074] "Server" refers to a computer system that receives information sent by a user, analyzes it, and provides the necessary data.

[1075] A "generative model" refers to an algorithm or program for generating game strategy information in response to a user's request, and generally uses natural language processing technology.

[1076] "Database" refers to a system that stores strategy information provided by users and information collected from external sources, and prepares it for later retrieval or access.

[1077] "External information" refers to new information that has not been stored in the database but has been collected from online strategy sites, forums, etc.

[1078] An "emotion engine" refers to technology or a program that analyzes user input data and voice data and identifies the emotion it conveys.

[1079] "Chatbot UI" refers to an interactive user interface that allows users to input questions or requests and receive responses from the system.

[1080] The present invention is a system that includes a user terminal for inputting game information, a server that receives input from users and analyzes the information, a generation model that provides game strategy information based on the analyzed information, a database that stores strategy information provided by users, a means for presenting answers to users based on the stored strategy information and external information, and an emotion engine that recognizes the user's emotions. Each element of the system and the overall processing flow will be explained in detail below.

[1081] User terminal operation

[1082] The system starts when a user accesses a dedicated chatbot app or web page using a terminal. User terminals consist of various devices, including smartphones, tablets, and personal computers. Through the chatbot's UI, users can input the game title and strategy information they want to know. For example, they can make a request to the chatbot such as, "Tell me how to beat Boss B in Game A."

[1083] Server analysis and information generation

[1084] The server receives input data from users and passes it to an analysis engine. The analysis engine extracts keywords related to the "game title" and "desired information" from the user's text. The server then uses a generative model to generate appropriate answers to the user's questions. The generative model uses natural language processing technology and utilizes large amounts of training data to generate walkthrough information with high accuracy.

[1085] Information Integration

[1086] In addition to the generated answers, the server searches its own database to retrieve walkthroughs and other data previously provided by users. The server also uses external internet browsing functions to gather the latest walkthrough information from walkthrough sites, forums, etc. This ensures that users are provided with the most up-to-date and reliable information.

[1087] The role of the emotional engine

[1088] Another feature of the present invention is the inclusion of an emotion engine. The emotion engine uses text analysis and speech recognition technology to recognize the user's emotions. The server uses the emotion data obtained from the emotion engine to adjust the tone and content of the information provided by the generative model. For example, if the user expresses dissatisfaction, the server may add more detailed explanations or words of encouragement.

[1089] Displaying Information

[1090] The server sends the integrated strategy information and content adjusted by the emotion engine to the user's device. The user receives a response through the chatbot, such as, "To efficiently defeat Boss B in Game A, it is recommended that you use specific skills and exploit the weaknesses that have been changed in the latest patch. Good luck!"

[1091] Information provided by users

[1092] When users want to provide new strategy information, they can enter it through the chatbot UI on their device. They can post new information in the form of, for example, "I tried out a newly discovered strategy and was able to easily defeat a particular boss."

[1093] Accumulation in the database

[1094] The server receives the new information, performs text analysis, and stores the analyzed information in its own database. The index is updated to reflect the new information and be used in future search results.

[1095] As a concrete example, if a user asks "How do I beat Mission D in Game C?", the server uses a generative model to generate the optimal strategy and adds an encouraging message based on the user's emotions (e.g., frustration) recognized by the emotion engine. As a result, the server provides an answer such as, "Using a specific item is effective in beating Mission D. There may be some difficult parts, but don't give up! Keep trying!"

[1096] Prompt Sentence Examples

[1097] For example, the prompt you would enter into a generative AI model might look like this:

[1098] A user asks, "Tell me how to complete Mission D in Game C." According to the emotion engine, the user seems to be frustrated. Please generate an answer that provides information on how to complete Mission D and encourages the user.

[1099] Using this prompt, the generative AI model generates an answer that takes into account the user's best information and emotions.

[1100] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1101] Step 1:

[1102] A user accesses a chatbot app or web page using a device

[1103] Input: The device starts up and opens a dedicated app or web page.

[1104] How it works: A user turns on a device such as a smartphone, tablet, or personal computer and accesses the dedicated app "GameHelper" or a web page.

[1105] Output: The home screen of the chatbot app or web page is displayed.

[1106] Step 2:

[1107] User requests walkthrough information from chatbot UI

[1108] Input: The user types a request for a specific game title or gameplay information into the chatbot's input field.

[1109] How it works: A user types a request into a chatbot, such as "Tell me how to beat boss B in game A."

[1110] Output: The user's request data is sent to the server.

[1111] Step 3:

[1112] The server receives user input and passes it to the analysis engine

[1113] Input: The server receives the request data from the user.

[1114] How it works: The server passes the request to the analysis engine, which extracts keywords for the "game title" and "information you want to know."

[1115] Output: Keywords such as "Game A" and "Boss B" are extracted.

[1116] Step 4:

[1117] The server generates and inputs prompts into the generative model.

[1118] Input: A prompt sentence based on the extracted keywords.

[1119] How it works: The server generates and inputs a prompt to a generative AI model (e.g., GPT-3). The prompt is "Tell me how to beat boss B in game A."

[1120] Output: A prompt for the generative model to answer.

[1121] Step 5:

[1122] The generative model generates the appropriate answer

[1123] Input: A generative model given a prompt sentence.

[1124] How it works: The generative model generates highly accurate strategy information based on the prompt sentence.

[1125] Output: An answer such as "Using a specific skill is effective in defeating Boss B."

[1126] Step 6:

[1127] The server integrates its own database with external information

[1128] Input: Answers from the generative model, proprietary databases, and information from external sites.

[1129] How it works: In addition to the generated answers, the server also integrates past walkthroughs from the database and the latest information from external sites.

[1130] Output: Consolidated and reliable strategy information.

[1131] Step 7:

[1132] The server analyzes the user's emotions using an emotion engine.

[1133] Input: User input data.

[1134] How it works: The emotion engine uses text and speech analysis to identify the user's emotions.

[1135] Output: User emotion (e.g., annoyance).

[1136] Step 8:

[1137] The server uses emotional data to tailor responses

[1138] Input: Generated strategy information and user emotion data.

[1139] How it works: The server uses the emotion data to adjust the tone and content of the generated responses.

[1140] Output: An answer such as "Using specific skills is effective in defeating Boss B. Also, don't give up, keep trying!"

[1141] Step 9:

[1142] The server sends the generated answer to the user's device.

[1143] Input: Adjusted response data.

[1144] Operation: The server sends the optimal strategy information to the user's device.

[1145] Output: Walkthrough information displayed on the user's device.

[1146] Step 10:

[1147] The user receives the information on the device

[1148] Input: The response data sent from the server.

[1149] How it works: The user receives the answer through the chatbot UI on their device.

[1150] Output: Strategy information displayed in the chat window.

[1151] Step 11:

[1152] Users provide new strategy information

[1153] Input: User data to input new walkthrough information.

[1154] How it works: A user posts a new strategy through the chatbot UI.

[1155] Output: New cheats from the user.

[1156] Step 12:

[1157] The server receives and analyzes the new information.

[1158] Input: New cheats provided by the user.

[1159] How it works: The server receives new exploit information and analyzes it using the analysis engine.

[1160] Output: New parsed exploit data.

[1161] Step 13:

[1162] The server stores the analyzed information in a database

[1163] Input: Parsed new exploit data.

[1164] How it works: The server stores the new information in its own database and updates the index so that it is reflected in future searches.

[1165] Output: An updated database with the latest cheats.

[1166] (Application example 2)

[1167] Next, a description will be given of Application 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."

[1168] A problem with conventional user information provision systems is that they provide uniform information without considering the user's emotions. This can lead to lower user satisfaction and the inability to receive optimal support. Furthermore, in virtual stores, customer support that does not respond to the customer's emotions can lead to a decrease in purchasing motivation.

[1169] 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.

[1170] In this invention, the server includes an emotion engine that analyzes the user's emotional state, means for adjusting responses based on the analysis results of the emotion engine, and means for analyzing user input and generating optimal information using a generative model, thereby making it possible to provide optimal information and support according to the user's emotions.

[1171] A "user terminal" is a device used by a user to input information, and includes a smartphone, tablet, personal computer, etc.

[1172] A "server" is a computer whose role is to receive input from users and analyze the information.

[1173] A "generative model" is an artificial intelligence model that uses natural language processing technology to provide appropriate answers and information based on analyzed information.

[1174] A "database" is an information management system that stores user-provided information and makes it available for later searches and queries.

[1175] The "emotion engine" is an analysis engine that has the function of analyzing the user's emotional state and adjusting the response content based on the results.

[1176] "Means of presenting information" refers to the functions and interfaces that allow the server to provide optimal information to users based on accumulated data and external information.

[1177] The "means for adjusting the response" is a function for appropriately adjusting the content of the response to the user based on the analysis results of the emotion engine, and providing optimal support and information.

[1178] "Natural language processing technology" is an artificial intelligence technology for understanding input text from users and generating appropriate responses.

[1179] A "proprietary database" is a database owned by the system that stores information obtained from users and external information.

[1180] The "external Internet browsing function" is a function that allows the server to collect external data and information via the Internet.

[1181] The present invention realizes the provision of information that takes into consideration the user's emotions by constructing a customer support system that includes a user terminal, a server, a generative model, a database, an emotion engine, and an information presentation means.

[1182] System configuration

[1183] User device operation

[1184] Users access a dedicated application or web page using a user device such as a smartphone, tablet, or personal computer and enter information. For example, the system starts by sending a request such as, "Please tell me more about the newly released smartphone."

[1185] Server Features

[1186] The server receives input data from the user and passes it to the analysis engine. The analysis engine extracts keywords related to the "question" and "related information" from the user's text. The generative model then generates the most appropriate answer. This generative model uses natural language processing technology to provide answers to the user's questions with high accuracy.

[1187] The role of the emotional engine

[1188] The emotion engine uses text analysis and speech recognition technology to recognize the user's emotional state. The server then adjusts the tone and content of the information generated by the generative model based on the emotional data obtained from the emotion engine. For example, if the user is excited, the server adds a positive response with detailed information.

[1189] Information Integration

[1190] In addition to the generated answers, the server searches its own database to retrieve information previously provided by users and external information, and also uses external internet browsing functions to gather the latest relevant information, allowing it to provide the most reliable and up-to-date information.

[1191] Displaying Information

[1192] The server sends the integrated information to the user's device, and the user can receive the information through the chatbot. For example, a response such as "The newly released smartphone has advanced camera functions and is in stock. We are also running a special campaign!" is provided.

[1193] About program processing

[1194] The server operates using the following processing means:

[1195] Emotion engine that analyzes emotional states: Analyzes emotions from user text using OpenAI's API.

[1196] How responses are tailored: Generative AI models are used based on the analysis results to generate optimal responses for the user.

[1197] Means of analyzing input and generating information: A live AI model using natural language processing techniques is used to generate appropriate answers to user questions.

[1198] Examples of concrete examples and prompts

[1199] For example, if a user sends a request such as "Tell me more about the new smartphone," the server analyzes the user's emotional state and uses a generative model to generate the best answer. Here is an example prompt:

[1200] A customer asked the following question: Can you tell me more about the newly launched smartphone?

[1201] The customer's emotion is excitement. Generate an appropriate response.

[1202] This results in a response like, "Our new smartphone has advanced camera features and is in stock. We're also running a special promotion!"

[1203] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1204] Step 1:

[1205] A user accesses a dedicated application or web page using a user terminal such as a smartphone or personal computer and inputs information. An example of input data is a request such as "Please tell me more about the newly released smartphone." This request is sent to the server.

[1206] Step 2:

[1207] The server receives input data from the user. It passes this received data to the analysis engine and begins analysis. The analysis engine extracts keywords for the "question content" and "related information" from the user's text. For example, the keyword "newly released smartphone" is extracted from the input data "Please tell me more about the newly released smartphone."

[1208] Step 3:

[1209] The server uses a generative model based on the analysis results to generate the optimal answer. The generative AI model uses natural language processing technology to generate the optimal answer from the extracted keywords. The prompt sentence in this case will be in the form below.

[1210] A customer asked the following question: Can you tell me more about the newly launched smartphone?

[1211] The customer's emotion is excitement. Generate an appropriate response.

[1212] Step 4:

[1213] In addition to the generated answer, the server searches its own database to retrieve information previously provided by the user and the latest related information. It also uses an external internet browsing function to collect the latest related information. This process retrieves data related to "new smartphones" from its own database and the latest information from external websites.

[1214] Step 5:

[1215] The emotion engine analyzes user input data to recognize their emotional state. For example, a request like "Tell me more about the new smartphone" might be interpreted as indicating that the user is excited.

[1216] Step 6:

[1217] The server then adjusts the responses generated by the generative model based on the analysis results of the emotion engine. For example, if the user is excited, a proactive and detailed response will be generated. This process is performed to adapt the content of the response to the user's emotional state.

[1218] Step 7:

[1219] The server sends the integrated information to the user's device, where it is displayed on the screen, and the user receives a response from the chatbot: "The newly released smartphone has advanced camera functions, is in stock, and we're also running a special campaign!"

[1220] In this way, a system is realized that takes into consideration the user's feelings and can provide appropriate and timely information.

[1221] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 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.

[1222] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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.

[1223] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1224] [Fourth embodiment]

[1225] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1226] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1227] 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).

[1228] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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 control target 443 are also connected to the bus 52.

[1229] 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.

[1230] 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).

[1231] 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.

[1232] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1233] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, 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.

[1234] 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.

[1235] 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.

[1236] In the robot 414, the processor 46 performs the reception output process. 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.

[1237] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1238] The present invention is a system that includes a user terminal for inputting game information, a server that receives input from the user and analyzes the information, a generation model that provides game strategy information based on the analyzed information, a database that stores strategy information provided by the user, and a means for presenting answers to the user based on the stored strategy information and external information. Each element of the system and the overall processing flow will be explained in detail below.

[1239] The system starts when a user accesses a dedicated chatbot app or web page using a device. The device can be a smartphone, tablet, or personal computer, and can input the game title and strategy information they want to know into the chatbot. For example, they can input information in the form of "I want to know how to beat a specific boss in a certain game."

[1240] The server receives and analyzes the data entered by the user. Specifically, it analyzes the text data from the user and extracts keywords related to the "game title" and "desired information." The server then uses a generative model to generate appropriate answers to the user's questions. This generative model uses natural language processing technology and utilizes large amounts of training data to generate walkthrough information with high accuracy.

[1241] In addition to the generated answers, the server searches its own database to retrieve previously provided walkthroughs and other accumulated data. The server also uses external internet browsing to gather data from walkthrough sites, forums, etc. to obtain the latest walkthrough information. This ensures that the most up-to-date and reliable information is provided to the user.

[1242] For example, if a user asks "How can I efficiently level up in a certain game?", the server will analyze the question through a generative model and suggest "the best way to level up in a specific mission or dungeon." It will also search the server's database and integrate useful information provided by other users and additional strategy information collected from the internet. For example, it can provide specific strategies such as "Repeatedly fighting in a specific location is efficient."

[1243] Furthermore, if a user wants to provide new strategy information, they can input the information from their device and send it to the server. The server analyzes this new information and stores it in its own database. This makes it possible for other users to provide answers based on the latest information when they ask similar questions in the future.

[1244] This system allows users to efficiently acquire game strategy information and share their knowledge to help other users, resulting in smoother and more enjoyable gameplay.

[1245] The processing flow will be explained below.

[1246] Step 1:

[1247] The user launches a dedicated app or web page on their device, allowing them to view the chatbot's UI.

[1248] Step 2:

[1249] The user inputs the game title and the strategy information they want to know. For example, they input "Tell me how to beat boss B in game A."

[1250] Step 3:

[1251] The server receives input from the user. The received text data is passed to an analysis engine, which extracts keywords (game title and strategy). The keywords obtained are "Game A" and "How to beat Boss B."

[1252] Step 4:

[1253] The server uses a generative model (using natural language processing technology) to generate an answer to the user's question. The generative model receives input such as "How to beat Boss B in Game A" and outputs the answer text.

[1254] Step 5:

[1255] The server searches its own database and retrieves relevant information about "Boss B in Game A" from the accumulated strategy information. For example, it can obtain information such as "To defeat Boss B, it is best to use a specific skill."

[1256] Step 6:

[1257] The server collects information from external internet sources, scraping strategy sites and forums to gather the latest strategy information. This is where it obtains information such as "Boss B's weakness has been changed in the latest patch."

[1258] Step 7:

[1259] The server aggregates generated answers, information from its own database, and information collected from external sources to check for consistency and reliability, and selects the most relevant information to provide to the user.

[1260] Step 8:

[1261] The server then compiles the final answer and sends it to the user's device. The user receives specific strategy information, such as, "To efficiently defeat Boss B in Game A, it is recommended that you use a specific skill and exploit a weakness that was changed in the latest patch."

[1262] Step 9:

[1263] When a user wants to provide new strategy information, they input the information through the chatbot UI on their device. The chatbot posts, "I've discovered a new strategy for defeating Boss B."

[1264] Step 10:

[1265] The server receives new posts, performs text analysis, and stores the analyzed information in its own database. The new information is then reflected in search results from the next time onwards.

[1266] The above is the specific processing flow of this system, which allows for efficient acquisition of game strategy information and promotes information sharing among users.

[1267] Example 1

[1268] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1269] In order to provide users with game strategy information efficiently and accurately, a system is needed that can analyze the information entered by the user, generate appropriate strategy information, and even integrate and present it with external information. However, conventional systems have difficulty in quickly and accurately obtaining the strategy information users need, and also lack the means to accumulate new information.

[1270] 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.

[1271] In this invention, the server includes means for receiving input from users and analyzing the information, means for providing a data generation model that provides game strategy information based on the analyzed information, and means for searching a data storage device that stores strategy information provided by users and for using information collected from outside. This allows users to efficiently obtain the strategy information they need and further add their own knowledge to the system.

[1272] "Game information" refers to data and strategy information related to the game that the user is playing.

[1273] "Information terminal" refers to devices used by users, such as smartphones, tablets, and personal computers.

[1274] "Processing device" refers to a computer or server that analyzes data received from a user and performs the necessary processing.

[1275] A "data generation model" refers to an algorithm or software that generates game strategy information based on input data.

[1276] "Data storage device" refers to a database or storage medium for storing strategy information provided by users.

[1277] "Externally collected information" refers to the latest strategy information obtained from the Internet and other sources.

[1278] "Natural language processing technology" refers to computer technology for understanding, analyzing, and generating human language.

[1279] A "generative AI model" is an artificial intelligence model that has been trained in advance with large amounts of data and uses natural language processing techniques to generate appropriate information from input data.

[1280] MODE FOR CARRYING OUT THE INVENTION

[1281] The present invention is a system that allows users to efficiently and accurately acquire game strategy information. This system is composed of a user terminal, a server, a data generation model, a data storage device, and external information collection means. Each element and processing flow of this system will be described in detail below.

[1282] User terminal

[1283] User terminals include devices such as smartphones, tablets, and personal computers. Users access a dedicated chatbot app or web page and input game strategy information. For example, they might input something like, "I want to know how to beat a specific boss in a specific game."

[1284] server

[1285] The server is a device that receives and analyzes data entered by users. The server analyzes the received text data and extracts keywords related to the "game title" and "desired information." The analysis uses natural language processing technology.

[1286] Data Generation Model

[1287] A data generation model is an algorithm or software that generates game strategy information based on analyzed keywords. This model is highly trained using large amounts of training data and generates appropriate answers to user questions. For example, it can provide the "optimal strategy for a specific mission or dungeon."

[1288] Data Storage Device

[1289] The data storage device includes a database and storage media for storing strategy information provided by users. The server can search and retrieve past strategy information in addition to the answers of the generative model.

[1290] External information gathering means

[1291] The server can collect the latest strategy information via the external Internet. It obtains data from strategy sites and forums and provides users with reliable, up-to-date information. With this function, the system can always provide answers to users based on the latest information.

[1292] Specific examples

[1293] For example, if a user types "Tell me how to beat Monster X" into the terminal, the specific actions are as follows:

[1294] 1. User device: The user types in "Tell me how to defeat Monster X" and presses the send button.

[1295] 2. Server: Analyzes the received text "Teach me how to defeat Monster X" and extracts "Monster X" and "how to defeat" as keywords.

[1296] 3. Data generation model: Based on the extracted keywords, strategy information is generated from the training data, and specific strategy information such as "Monster X is weak against water-element attacks. It is best to attack with a water-element weapon" is provided.

[1297] 4. Data storage device: Search the database for information related to "Monster X" provided by past users.

[1298] 5. External information gathering methods: Collect the latest strategy information from the Internet.

[1299] 6. Integration and presentation: All collected information is integrated and displayed on the user's device.

[1300] Prompt Sentence Examples

[1301] Here are some examples of prompts for generative AI models:

[1302] "Generate the best answer when a user asks how to defeat Monster X."

[1303] With the above configuration and operation, this system can provide users with efficient and accurate game strategy information, and also promote the sharing of knowledge among users.

[1304] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1305] System processing flow

[1306] Step 1:

[1307] A user accesses a chatbot app or web page from a device

[1308] What happens: A user accesses a compatible chatbot app or web page using a device such as a smartphone, tablet, or personal computer.

[1309] Input: User actions to access the chatbot app or web page

[1310] Output: The app or web page is launched and the interface is presented to the user.

[1311] Step 2:

[1312] The user inputs the strategy information, and the device sends the data to the server.

[1313] Specific operation: The user inputs the game title and specific strategy information into the displayed interface. Once input is complete, the device sends this data to the server. For example, the user might input "Tell me how to beat a specific boss."

[1314] Input: Strategy information entered by the user (text data)

[1315] Output: The entered data is sent to the server.

[1316] Step 3:

[1317] The server analyzes the received data and extracts keywords.

[1318] Specific operation: The server analyzes the received text data and extracts keywords related to the "game title" and "information you want to know." This analysis uses natural language processing technology.

[1319] Input: Text data sent by the user

[1320] Output: Extracted keywords (game title, desired information)

[1321] Step 4:

[1322] The server uses the generative model to generate the appropriate answer

[1323] How it works: Based on the extracted keywords, the server uses a generative model to generate answers to the user's questions. The generative model is trained on a large amount of training data.

[1324] Input: Extracted keywords

[1325] Output: Generated answer (specific strategy information)

[1326] Step 5:

[1327] The server searches its own database to obtain relevant information

[1328] Specific operation: In addition to the answer of the generative model, the server searches and retrieves the accumulated past strategy information from the database, which allows it to provide more reference information.

[1329] Input: Generated answers, database index information

[1330] Output: Related strategy information accumulated in the past

[1331] Step 6:

[1332] The server collects the latest exploit information from the external Internet.

[1333] Specific operation: The server uses external internet browsing functions to collect the latest cheat information from cheat sites and forums. The collected data is analyzed and only reliable information is extracted.

[1334] Input: Raw exploit information collected from external sources

[1335] Output: Analyzed and reliable strategy information

[1336] Step 7:

[1337] The server consolidates all the information it has acquired and provides the answer to the user.

[1338] Specific operation: The server integrates the generative model's answers, accumulated information, and externally collected data to provide the user with the most appropriate answer. For example, it may provide information such as "Using a weapon with a specific attribute is effective."

[1339] Input: Generated answers, historical information, externally collected information

[1340] Output: Integrated strategy information presented to the user

[1341] Step 8:

[1342] The user inputs new strategy information from the terminal, and the server stores the information in the database.

[1343] Specific operation: The user inputs and submits new strategy information from their device. The server analyzes this new information and stores it in the database. This allows the server to provide more accurate information when a similar question is asked in the future.

[1344] Input: New strategy information provided by the user

[1345] Output: New strategy information analyzed and stored in the database

[1346] Through the above steps, the system provides users with efficient and accurate game strategy information and promotes knowledge sharing among users.

[1347] (Application example 1)

[1348] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1349] With the large number of games available on the market today, players need a way to quickly obtain game strategy information for each game and improve their gameplay based on that information. However, game strategy information is scattered, making it difficult to guarantee its reliability and up-to-dateness. Therefore, there is a need for a system that can provide game strategy information efficiently and reliably.

[1350] 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.

[1351] In this invention, the server includes means for receiving questions from users and generating optimal walkthrough information using a generative AI model, means for acquiring and integrating the latest information from an accumulated database and external sites, and means for presenting answers to users based on the accumulated walkthrough information and external information, thereby enabling users to quickly obtain the latest and most reliable walkthrough information.

[1352] "User terminal" refers to any device that a user uses to input game information.

[1353] A "server" is a device or system that receives input from a user and analyzes the information.

[1354] A "generative model" is a model that provides game strategy information based on analyzed information, and in particular makes use of natural language processing technology.

[1355] The "database" is an information repository for storing and managing strategy information provided by users.

[1356] A "means" is a method or apparatus for performing a particular function or role.

[1357] The "means for accepting a question" refers to a method or device by which the system accepts a question input from a user.

[1358] "Means for generating optimal strategy information using a generative AI model" refers to a method or device for generating optimal answers to user questions using AI technology.

[1359] The "means for acquiring and integrating the latest information" refers to a method or device for acquiring the latest strategy information from databases and external sites and integrating it.

[1360] The "means for presenting an answer to the user" is a method or device for providing an appropriate answer to the user based on the accumulated walkthrough information and the acquired external information.

[1361] The present invention provides a system that allows users to efficiently obtain game strategy information and store their own knowledge in a database. Below, each element of the system and the overall processing flow will be explained in detail.

[1362] User terminal

[1363] The system of the present invention includes a user terminal through which a user inputs game information. This user terminal can be any device, such as a smartphone, tablet, or personal computer. Using the terminal, a user accesses a dedicated chatbot app or web page and inputs questions and strategy information about the game.

[1364] server

[1365] The server receives input from users and analyzes the information. Specifically, it analyzes the text data from the user and extracts keywords related to the "game title" and "information the user wants to know." The server then uses a generative AI model to generate appropriate answers to the user's questions. This generative AI model uses natural language processing technology and utilizes large amounts of training data. For example, answers generated using OpenAI's API are utilized.

[1366] Database

[1367] The server has a database that stores strategy information provided by users. This database stores strategy information provided in the past and related information collected from other sources. The server also has a search function for its own database and an external Internet browsing function, and obtains the latest information from external strategy sites and forums and adds it to the database.

[1368] Generative AI Models

[1369] The generative AI model is an element that generates optimal strategy information based on the content of questions from users. The generative AI model aims to provide highly accurate and reliable information, and uses natural language processing technology to analyze the content of questions and generate appropriate answers. This model can use, for example, OpenAI's API.

[1370] Server processing flow

[1371] 1. When a user enters a specific question such as "Tell me the trick to defeating Monster X," the server receives this text data.

[1372] 2. Based on the received data, keywords such as "Monster X" and "tips for defeating it" are extracted.

[1373] 3. The generative AI model generates an initial answer using OpenAI's API.

[1374] 4. Query the database to retrieve relevant information provided by past users.

[1375] 5. Obtain the latest strategy information from external sites and integrate it as supplementary information.

[1376] 6. Provide the generated answer and additional information to the user.

[1377] Prompt Sentence Examples

[1378] If a user asks "What's the trick to defeating Monster X?", the server might send the following prompt to the generative AI model:

[1379] What are some tips for defeating Monster X?

[1380] By using this prompt, the server can generate optimal strategy information, allowing the user to efficiently acquire game strategy information and improve their gameplay.

[1381] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1382] Step 1:

[1383] A user enters game information into a chatbot app or web page.

[1384] Input: The question text from the user (e.g., "What's the trick to defeating Monster X?").

[1385] Output: User input data is sent to the server.

[1386] Step 2:

[1387] The server receives the data entered by the user.

[1388] Input: The user's question text arrives at the server.

[1389] Output: The received data is sent for analysis.

[1390] Step 3:

[1391] The server analyzes the text data from the user and extracts keywords for the "game title" and "information you want to know."

[1392] Input: The user's question text.

[1393] Data processing: Perform text analysis (natural language processing) and extract keywords.

[1394] Output: Extracted keywords (e.g. "Monster X", "Tips to defeat it").

[1395] Step 4:

[1396] The server passes the extracted keywords to the generative AI model, which then creates and sends prompt text to generate optimal strategy information.

[1397] Input: Extracted keywords.

[1398] Data calculation: Pass the prompt sentence to the generative AI model and generate an answer.

[1399] Output: Answer text from the generative AI model.

[1400] Specific operation: Generate a prompt sentence, "Please tell me some tips for defeating Monster X," and send it to the AI ​​model.

[1401] Step 5:

[1402] The server searches its own database to retrieve related strategy information that has been provided in the past.

[1403] Input: Extracted keywords.

[1404] Data processing: Perform database searches and obtain relevant strategy information.

[1405] Output: Past strategy information obtained.

[1406] Specific operation: Search for strategy information related to "Monster X" in the database and obtain the results.

[1407] Step 6:

[1408] The server uses external internet browsing functions to obtain the latest strategy information from external strategy sites and forums.

[1409] Input: Extracted keywords.

[1410] Data processing: Collect strategy information using web scraping and API access.

[1411] Output: The latest external exploit information obtained.

[1412] What it does: Perform a web search to get the latest information from trusted cheat sites.

[1413] Step 7:

[1414] The server integrates the generated answers, information from the database, and information from external sites to generate a final answer.

[1415] Input: Answer text from the generative AI model, past strategy information, and the latest external information.

[1416] Data arithmetic: content integration and organization.

[1417] Output: The final answer text.

[1418] What it does: Synthesizes all the information and creates a final answer in a user-friendly format.

[1419] Step 8:

[1420] The server sends the final consolidated answer to the user.

[1421] Input: Final answer text.

[1422] Output: The answer displayed on the user's terminal.

[1423] Specific operation: The final answer is sent to the user's device and displayed on the screen.

[1424] This allows the user to efficiently acquire game strategy information and improve their gameplay.

[1425] 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.

[1426] The present invention is a system that includes a user terminal for inputting game information, a server that receives input from the user and analyzes the information, a generative model that provides game strategy information based on the analyzed information, a database that stores strategy information provided by the user, a means for presenting answers to the user based on the stored strategy information and external information, and an emotion engine that recognizes the user's emotions. Each element of the system and the overall processing flow will be explained in detail below.

[1427] User device operation

[1428] The system starts when a user accesses a dedicated chatbot app or web page using a terminal. User terminals consist of various devices, including smartphones, tablets, and personal computers. Through the chatbot's UI, users can input the game title and strategy information they want to know. For example, they can make a request to the chatbot such as, "Tell me how to beat Boss B in Game A."

[1429] Server analysis and information generation

[1430] The server receives input data from the user and passes it to the analysis engine. The analysis engine extracts keywords related to the "game title" and "desired information" from the user's text. The server then uses a generative model to generate appropriate answers to the user's questions. The generative model uses natural language processing technology and utilizes large amounts of training data to generate walkthrough information with high accuracy.

[1431] Information Integration

[1432] In addition to the generated answers, the server searches its own database to retrieve walkthroughs and other data previously provided by users. The server also uses external internet browsing functions to gather the latest walkthrough information from walkthrough sites, forums, etc. This ensures that the most up-to-date and reliable information is provided to users.

[1433] The role of the emotional engine

[1434] Another feature of the present invention is the inclusion of an emotion engine, which uses text analysis and speech recognition technology to recognize the user's emotions. The server uses the emotion data obtained from the emotion engine to adjust the tone and content of the information provided by the generative model. For example, if the user expresses dissatisfaction, the server may add more detailed explanations or words of encouragement.

[1435] Displaying Information

[1436] The server sends the integrated strategy information and the content adjusted by the emotion engine to the user's device. The user receives a response through the chatbot, such as, "To efficiently defeat Boss B in Game A, it is recommended that you use specific skills and exploit the weaknesses that have been changed in the latest patch. Good luck!"

[1437] User input

[1438] When a user wants to provide new strategy information, they can enter it through the chatbot UI on their device. They can post new information in the form of, for example, "I tried out a newly discovered strategy and was able to easily defeat a specific boss."

[1439] Accumulation in the database

[1440] The server receives the new information, performs text analysis, and stores the analyzed information in its own database. The index is updated to reflect the new information and be used in future search results.

[1441] As a concrete example, if a user asks "How do I beat Mission D in Game C?", the server uses a generative model to generate the optimal strategy and adds an encouraging message based on the user's emotions (e.g., frustration) recognized by the emotion engine. As a result, the server provides an answer such as "Using a specific item is effective in beating Mission D. There may be some difficult parts, but don't give up! Try!"

[1442] The above is a detailed description of the embodiment of the present invention. This system allows users to efficiently obtain game strategy information and also provides appropriate support that takes into consideration the user's feelings.

[1443] The processing flow will be explained below.

[1444] Step 1:

[1445] The user launches a dedicated app or web page on their device, allowing them to view the chatbot's UI.

[1446] Step 2:

[1447] The user inputs the game title and the strategy information they want to know. For example, they input "Tell me how to beat boss B in game A."

[1448] Step 3:

[1449] The server receives input data from the user. The received text data is passed to an analysis engine, which extracts keywords (game title and strategy). The keywords obtained are "Game A" and "How to beat Boss B."

[1450] Step 4:

[1451] The server uses a generative model (using natural language processing technology) to generate an answer to the user's question. The generative model receives input such as "How to beat Boss B in Game A" and outputs the answer text.

[1452] Step 5:

[1453] The server searches its own database and retrieves relevant information about "Boss B in Game A" from the accumulated strategy information. For example, it can obtain information such as "To defeat Boss B, it is best to use a specific skill."

[1454] Step 6:

[1455] The server collects information from external internet sources, scraping strategy sites and forums to gather the latest strategy information. This is where it obtains information such as "Boss B's weakness has been changed in the latest patch."

[1456] Step 7:

[1457] The server aggregates generated answers, information from its own database, and information collected from external sources to check for consistency and reliability, and selects the most relevant information to provide to the user.

[1458] Step 8:

[1459] The server uses an emotion engine to analyze emotions from the text and voice data entered by the user, for example by analyzing the context of the input text and the tone of the voice to determine whether the user is annoyed or satisfied.

[1460] Step 9:

[1461] The server adjusts the tone and content of the response based on the user's emotional data analyzed by the emotion engine. For example, if the user is frustrated, the server adjusts the response by adding more detailed explanations or encouraging words.

[1462] Step 10:

[1463] The server then sends the final adjusted answer to the user's device. The user receives specific strategy information, such as, "To efficiently defeat Boss B in Game A, it is recommended that you use specific skills and exploit the weaknesses that were changed in the latest patch. Good luck!"

[1464] Step 11:

[1465] When a user wants to provide new strategy information, they input the information through the chatbot UI on their device. The chatbot posts, "I've discovered a new strategy for defeating Boss B."

[1466] Step 12:

[1467] The server receives new posts, performs text analysis, stores the analyzed information in its own database, and updates the index to reflect the new information and ensure it is reflected in future search results.

[1468] The above is the specific processing flow of this system, which allows users to efficiently obtain game strategy information and receive adaptive support based on the user's emotions.

[1469] Example 2

[1470] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1471] Conventional game strategy information providing systems may not always be able to provide the specific strategy information desired by the user, and may also lack consideration for the user's feelings. This raises concerns that the quality of the user experience may be reduced. The present invention aims to provide a system that efficiently provides users with game strategy information and provides appropriate support that takes the user's feelings into consideration.

[1472] 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.

[1473] In this invention, the server includes means for receiving input from a user and analyzing the information, a generative model means for providing game strategy information based on the analyzed information, and a means for presenting answers to the user based on the accumulated strategy information and external information. This makes it possible to quickly provide specific and up-to-date strategy information that the user desires. Furthermore, by using an emotion engine means, it is possible to respond according to the user's emotions, improving the quality of the user experience.

[1474] "Game information" refers to any data or information that a user has relating to a game, and specifically includes strategies for winning, strategies for defeating boss characters, details of missions, and the like.

[1475] "User terminal" refers to a device used by a user to search for and input game information, including smartphones, tablets, personal computers, etc.

[1476] "Server" refers to a computer system that receives information sent by a user, analyzes it, and provides the necessary data.

[1477] A "generative model" refers to an algorithm or program for generating game strategy information in response to a user's request, and generally uses natural language processing technology.

[1478] "Database" refers to a system that stores strategy information provided by users and information collected from external sources, and prepares it for later retrieval or access.

[1479] "External information" refers to new information that has not been stored in the database but has been collected from online strategy sites, forums, etc.

[1480] An "emotion engine" refers to technology or a program that analyzes user input data and voice data and identifies the emotion it conveys.

[1481] "Chatbot UI" refers to an interactive user interface that allows users to input questions or requests and receive responses from the system.

[1482] The present invention is a system that includes a user terminal for inputting game information, a server that receives input from users and analyzes the information, a generation model that provides game strategy information based on the analyzed information, a database that stores strategy information provided by users, a means for presenting answers to users based on the stored strategy information and external information, and an emotion engine that recognizes the user's emotions. Each element of the system and the overall processing flow will be explained in detail below.

[1483] User terminal operation

[1484] The system starts when a user accesses a dedicated chatbot app or web page using a terminal. User terminals consist of various devices, including smartphones, tablets, and personal computers. Through the chatbot's UI, users can input the game title and strategy information they want to know. For example, they can make a request to the chatbot such as, "Tell me how to beat Boss B in Game A."

[1485] Server analysis and information generation

[1486] The server receives input data from users and passes it to an analysis engine. The analysis engine extracts keywords related to the "game title" and "desired information" from the user's text. The server then uses a generative model to generate appropriate answers to the user's questions. The generative model uses natural language processing technology and utilizes large amounts of training data to generate walkthrough information with high accuracy.

[1487] Information Integration

[1488] In addition to the generated answers, the server searches its own database to retrieve walkthroughs and other data previously provided by users. The server also uses external internet browsing functions to gather the latest walkthrough information from walkthrough sites, forums, etc. This ensures that users are provided with the most up-to-date and reliable information.

[1489] The role of the emotional engine

[1490] Another feature of the present invention is the inclusion of an emotion engine. The emotion engine uses text analysis and speech recognition technology to recognize the user's emotions. The server uses the emotion data obtained from the emotion engine to adjust the tone and content of the information provided by the generative model. For example, if the user expresses dissatisfaction, the server may add more detailed explanations or words of encouragement.

[1491] Displaying Information

[1492] The server sends the integrated strategy information and content adjusted by the emotion engine to the user's device. The user receives a response through the chatbot, such as, "To efficiently defeat Boss B in Game A, it is recommended that you use specific skills and exploit the weaknesses that have been changed in the latest patch. Good luck!"

[1493] Information provided by users

[1494] When users want to provide new strategy information, they can enter it through the chatbot UI on their device. They can post new information in the form of, for example, "I tried out a newly discovered strategy and was able to easily defeat a particular boss."

[1495] Accumulation in the database

[1496] The server receives the new information, performs text analysis, and stores the analyzed information in its own database. The index is updated to reflect the new information and be used in future search results.

[1497] As a concrete example, if a user asks "How do I beat Mission D in Game C?", the server uses a generative model to generate the optimal strategy and adds an encouraging message based on the user's emotions (e.g., frustration) recognized by the emotion engine. As a result, the server provides an answer such as, "Using a specific item is effective in beating Mission D. There may be some difficult parts, but don't give up! Keep trying!"

[1498] Prompt Sentence Examples

[1499] For example, the prompt you would enter into a generative AI model might look like this:

[1500] A user asks, "Tell me how to complete Mission D in Game C." According to the emotion engine, the user seems to be frustrated. Please generate an answer that provides information on how to complete Mission D and encourages the user.

[1501] Using this prompt, the generative AI model generates an answer that takes into account the user's best information and emotions.

[1502] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1503] Step 1:

[1504] A user accesses a chatbot app or web page using a device

[1505] Input: The device starts up and opens a dedicated app or web page.

[1506] How it works: A user turns on a device such as a smartphone, tablet, or personal computer and accesses the dedicated app "GameHelper" or a web page.

[1507] Output: The home screen of the chatbot app or web page is displayed.

[1508] Step 2:

[1509] User requests walkthrough information from chatbot UI

[1510] Input: The user types a request for a specific game title or gameplay information into the chatbot's input field.

[1511] How it works: A user types a request into a chatbot, such as "Tell me how to beat boss B in game A."

[1512] Output: The user's request data is sent to the server.

[1513] Step 3:

[1514] The server receives user input and passes it to the analysis engine

[1515] Input: The server receives the request data from the user.

[1516] How it works: The server passes the request to the analysis engine, which extracts keywords for the "game title" and "information you want to know."

[1517] Output: Keywords such as "Game A" and "Boss B" are extracted.

[1518] Step 4:

[1519] The server generates and inputs prompts into the generative model.

[1520] Input: A prompt sentence based on the extracted keywords.

[1521] How it works: The server generates and inputs a prompt to a generative AI model (e.g., GPT-3). The prompt is "Tell me how to beat boss B in game A."

[1522] Output: A prompt for the generative model to answer.

[1523] Step 5:

[1524] The generative model generates the appropriate answer

[1525] Input: A generative model given a prompt sentence.

[1526] How it works: The generative model generates highly accurate strategy information based on the prompt sentence.

[1527] Output: An answer such as "Using a specific skill is effective in defeating Boss B."

[1528] Step 6:

[1529] The server integrates its own database with external information

[1530] Input: Answers from the generative model, proprietary databases, and information from external sites.

[1531] How it works: In addition to the generated answers, the server also integrates past walkthroughs from the database and the latest information from external sites.

[1532] Output: Consolidated and reliable strategy information.

[1533] Step 7:

[1534] The server analyzes the user's emotions using an emotion engine.

[1535] Input: User input data.

[1536] How it works: The emotion engine uses text and speech analysis to identify the user's emotions.

[1537] Output: User emotion (e.g., annoyance).

[1538] Step 8:

[1539] The server uses emotional data to tailor responses

[1540] Input: Generated strategy information and user emotion data.

[1541] How it works: The server uses the emotion data to adjust the tone and content of the generated responses.

[1542] Output: An answer such as "Using specific skills is effective in defeating Boss B. Also, don't give up, keep trying!"

[1543] Step 9:

[1544] The server sends the generated answer to the user's device.

[1545] Input: Adjusted response data.

[1546] Operation: The server sends the optimal strategy information to the user's device.

[1547] Output: Walkthrough information displayed on the user's device.

[1548] Step 10:

[1549] The user receives the information on the device

[1550] Input: The response data sent from the server.

[1551] How it works: The user receives the answer through the chatbot UI on their device.

[1552] Output: Strategy information displayed in the chat window.

[1553] Step 11:

[1554] Users provide new strategy information

[1555] Input: User data to input new walkthrough information.

[1556] How it works: A user posts a new strategy through the chatbot UI.

[1557] Output: New cheats from the user.

[1558] Step 12:

[1559] The server receives and analyzes the new information.

[1560] Input: New cheats provided by the user.

[1561] How it works: The server receives new exploit information and analyzes it using the analysis engine.

[1562] Output: New parsed exploit data.

[1563] Step 13:

[1564] The server stores the analyzed information in a database

[1565] Input: Parsed new exploit data.

[1566] How it works: The server stores the new information in its own database and updates the index so that it is reflected in future searches.

[1567] Output: An updated database with the latest cheats.

[1568] (Application example 2)

[1569] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1570] A problem with conventional user information provision systems is that they provide uniform information without considering the user's emotions. This can lead to lower user satisfaction and the inability to receive optimal support. Furthermore, in virtual stores, customer support that does not respond to the customer's emotions can lead to a decrease in purchasing motivation.

[1571] 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.

[1572] In this invention, the server includes an emotion engine that analyzes the user's emotional state, means for adjusting responses based on the analysis results of the emotion engine, and means for analyzing user input and generating optimal information using a generative model, thereby making it possible to provide optimal information and support according to the user's emotions.

[1573] A "user terminal" is a device used by a user to input information, and includes a smartphone, tablet, personal computer, etc.

[1574] A "server" is a computer whose role is to receive input from users and analyze the information.

[1575] A "generative model" is an artificial intelligence model that uses natural language processing technology to provide appropriate answers and information based on analyzed information.

[1576] A "database" is an information management system that stores user-provided information and makes it available for later searches and queries.

[1577] The "emotion engine" is an analysis engine that has the function of analyzing the user's emotional state and adjusting the response content based on the results.

[1578] "Means of presenting information" refers to the functions and interfaces that allow the server to provide optimal information to users based on accumulated data and external information.

[1579] The "means for adjusting the response" is a function for appropriately adjusting the content of the response to the user based on the analysis results of the emotion engine, and providing optimal support and information.

[1580] "Natural language processing technology" is an artificial intelligence technology for understanding input text from users and generating appropriate responses.

[1581] A "proprietary database" is a database owned by the system that stores information obtained from users and external information.

[1582] The "external Internet browsing function" is a function that allows the server to collect external data and information via the Internet.

[1583] The present invention realizes the provision of information that takes into consideration the user's emotions by constructing a customer support system that includes a user terminal, a server, a generative model, a database, an emotion engine, and an information presentation means.

[1584] System configuration

[1585] User device operation

[1586] Users access a dedicated application or web page using a user device such as a smartphone, tablet, or personal computer and enter information. For example, the system starts by sending a request such as, "Please tell me more about the newly released smartphone."

[1587] Server Features

[1588] The server receives input data from the user and passes it to the analysis engine. The analysis engine extracts keywords related to the "question" and "related information" from the user's text. The generative model then generates the most appropriate answer. This generative model uses natural language processing technology to provide answers to the user's questions with high accuracy.

[1589] The role of the emotional engine

[1590] The emotion engine uses text analysis and speech recognition technology to recognize the user's emotional state. The server then adjusts the tone and content of the information generated by the generative model based on the emotional data obtained from the emotion engine. For example, if the user is excited, the server adds a positive response with detailed information.

[1591] Information Integration

[1592] In addition to the generated answers, the server searches its own database to retrieve information previously provided by users and external information, and also uses external internet browsing functions to gather the latest relevant information, allowing it to provide the most reliable and up-to-date information.

[1593] Displaying Information

[1594] The server sends the integrated information to the user's device, and the user can receive the information through the chatbot. For example, a response such as "The newly released smartphone has advanced camera functions and is in stock. We are also running a special campaign!" is provided.

[1595] About program processing

[1596] The server operates using the following processing means:

[1597] Emotion engine that analyzes emotional states: Analyzes emotions from user text using OpenAI's API.

[1598] How responses are tailored: Generative AI models are used based on the analysis results to generate optimal responses for the user.

[1599] Means of analyzing input and generating information: A live AI model using natural language processing techniques is used to generate appropriate answers to user questions.

[1600] Examples of concrete examples and prompts

[1601] For example, if a user sends a request such as "Tell me more about the new smartphone," the server analyzes the user's emotional state and uses a generative model to generate the best answer. Here is an example prompt:

[1602] A customer asked the following question: Can you tell me more about the newly launched smartphone?

[1603] The customer's emotion is excitement. Generate an appropriate response.

[1604] This results in a response like, "Our new smartphone has advanced camera features and is in stock. We're also running a special promotion!"

[1605] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1606] Step 1:

[1607] A user accesses a dedicated application or web page using a user terminal such as a smartphone or personal computer and inputs information. An example of input data is a request such as "Please tell me more about the newly released smartphone." This request is sent to the server.

[1608] Step 2:

[1609] The server receives input data from the user. It passes this received data to the analysis engine and begins analysis. The analysis engine extracts keywords for the "question content" and "related information" from the user's text. For example, the keyword "newly released smartphone" is extracted from the input data "Please tell me more about the newly released smartphone."

[1610] Step 3:

[1611] The server uses a generative model based on the analysis results to generate the optimal answer. The generative AI model uses natural language processing technology to generate the optimal answer from the extracted keywords. The prompt sentence in this case will be in the form below.

[1612] A customer asked the following question: Can you tell me more about the newly launched smartphone?

[1613] The customer's emotion is excitement. Generate an appropriate response.

[1614] Step 4:

[1615] In addition to the generated answer, the server searches its own database to retrieve information previously provided by the user and the latest related information. It also uses an external internet browsing function to collect the latest related information. This process retrieves data related to "new smartphones" from its own database and the latest information from external websites.

[1616] Step 5:

[1617] The emotion engine analyzes user input data to recognize their emotional state. For example, a request like "Tell me more about the new smartphone" might be interpreted as indicating that the user is excited.

[1618] Step 6:

[1619] The server then adjusts the responses generated by the generative model based on the analysis results of the emotion engine. For example, if the user is excited, a proactive and detailed response will be generated. This process is performed to adapt the content of the response to the user's emotional state.

[1620] Step 7:

[1621] The server sends the integrated information to the user's device, where it is displayed on the screen, and the user receives a response from the chatbot: "The newly released smartphone has advanced camera functions, is in stock, and we're also running a special campaign!"

[1622] In this way, a system is realized that takes into consideration the user's feelings and can provide appropriate and timely information.

[1623] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice 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 voice data.

[1624] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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.

[1625] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1626] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1627] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1628] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1629] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1630] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1631] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1632] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1633] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1634] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1635] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1636] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1637] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1638] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1639] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1640] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1641] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1642] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1643] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1644] The following is further disclosed regarding the above embodiment.

[1645] (Claim 1)

[1646] a user terminal for inputting game information;

[1647] a server that receives input from a user and analyzes the information;

[1648] A generative model that provides game strategy information based on the analyzed information;

[1649] a database that stores strategy information provided by users;

[1650] A means for presenting answers to users based on accumulated strategy information and external information;

[1651] A system including:

[1652] (Claim 2)

[1653] 2. The system according to claim 1, wherein the generative model is a generative model using natural language processing technology.

[1654] (Claim 3)

[1655] 2. The system of claim 1, wherein the server has a proprietary database search and external internet browsing capability.

[1656] "Example 1"

[1657] (Claim 1)

[1658] an information terminal for inputting game information;

[1659] a processing unit that receives input from a user and analyzes the information;

[1660] A data generation model that provides game strategy information based on the analyzed information;

[1661] a data storage device that stores strategy information provided by users;

[1662] A means for presenting answers to users based on accumulated strategy information and information collected from external sources;

[1663] A system including:

[1664] (Claim 2)

[1665] The system of claim 1, wherein the data generation model is a generative AI model using natural language processing technology.

[1666] (Claim 3)

[1667] 2. The system according to claim 1, wherein the processing device has the function of searching its own data storage device and collecting data using an external information network.

[1668] "Application Example 1"

[1669] (Claim 1)

[1670] a user terminal for inputting game information;

[1671] a server that receives input from a user and analyzes the information;

[1672] A generative model that provides game strategy information based on the analyzed information;

[1673] a database that stores strategy information provided by users;

[1674] A means for presenting answers to users based on accumulated strategy information and external information;

[1675] A means for receiving questions from users and generating optimal strategy information using a generative AI model;

[1676] A means of retrieving and integrating up-to-date information from the cumulative database and external sites;

[1677] A system including:

[1678] (Claim 2)

[1679] 2. The system according to claim 1, wherein the generative model is a generative model using natural language processing technology.

[1680] (Claim 3)

[1681] 2. The system of claim 1, wherein the server has a proprietary database search and external internet browsing capability.

[1682] "Example 2: Combining Emotion Engines"

[1683] (Claim 1)

[1684] A user terminal for inputting game information;

[1685] a server that receives input from users and analyzes the information;

[1686] A generative model that provides game strategy information based on the analyzed information;

[1687] A database that stores strategy information provided by users;

[1688] A means for presenting answers to users based on accumulated strategy information and external information;

[1689] An emotion engine that recognizes the user's emotions and adjusts the tone and content of the information provided by the generative model; and

[1690] A system including:

[1691] (Claim 2)

[1692] 2. The system according to claim 1, wherein the generative model is a generative model using natural language processing technology.

[1693] (Claim 3)

[1694] 2. The system of claim 1, wherein the server has a proprietary database search and external internet browsing capability.

[1695] "Application example 2 when combining emotion engines"

[1696] (Claim 1)

[1697] a user terminal for inputting game information;

[1698] a server that receives input from a user and analyzes the information;

[1699] A generative model that provides game strategy information based on the analyzed information;

[1700] a database that stores strategy information provided by users;

[1701] A means for presenting answers to users based on accumulated strategy information and external information;

[1702] an emotion engine that analyzes the user's emotional state;

[1703] a means for adjusting a response based on the analysis of the emotion engine;

[1704] A system including:

[1705] (Claim 2)

[1706] 2. The system according to claim 1, wherein the generative model is a generative model using natural language processing technology.

[1707] (Claim 3)

[1708] 2. The system of claim 1, wherein the server has a proprietary database search and external internet browsing capability. [Explanation of symbols]

[1709] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a user terminal for inputting game information; a server that receives input from a user and analyzes the information; A generative model that provides game strategy information based on the analyzed information; a database that stores strategy information provided by users; A means for presenting answers to users based on accumulated strategy information and external information; A system including:

2. The system according to claim 1 , wherein the generative model is a generative model using natural language processing technology.

3. 2. The system of claim 1, wherein the server has a proprietary database search and external internet browsing capability.

Images