system

Abstract

Provide a system. [Solution] means for a user to read the information identification code; a means for transmitting a request to a server based on information acquired by the terminal from the information identification code; means for retrieving source text from a database based on a request received by the server; A means for the server to call an external translation resource and translate the source text into the user's language; A means for the server to return the translation result to the terminal; A means for the terminal to display the translation result; A system including:

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] In conventional art museums and tourist attractions, providing multilingual explanations for all artworks and tourist attractions requires a great deal of effort. Furthermore, it is difficult to provide multilingual explanations in limited space, resulting in information being limited to specific languages, which creates the problem of insufficient information being provided to tourists who speak a variety of languages. Furthermore, there is a lack of systems that can provide accurate translations in real time. This situation prevents visitors from accurately understanding and enjoying the information. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing an information provision system that uses a specific information identification code. Specifically, the system includes a means for a user to read an information identification code (QR Code (registered trademark)) with a terminal, and a means for the terminal to send a request to a server based on the information read. The server further includes a means for receiving the request, retrieving the corresponding original text from a database, and translating the retrieved original text into the user's language using an external translation resource. The server then includes a means for returning the translation result to the user's terminal, which then displays the translation result. In this way, it is possible to provide multilingual information in real time to users who speak various languages.

[0006] An "information identification code" is a code that contains specific information, such as a QR code or barcode.

[0007] A "terminal" is an electronic device operated by a user, such as a smartphone, tablet, or PC.

[0008] A "server" is a computer system that receives and sends data over a network and interacts with databases and external resources in response to requests.

[0009] A "database" is a system for organizing and managing information, making it easy to search and extract.

[0010] "Original text" means a document written in the original language before translation.

[0011] "External translation resources" are machine translation services provided on the Internet and other resources with translation capabilities.

[0012] A "translation result" is a document that has been converted from its original language to the user's preferred language by an external translation resource.

[0013] A "user" is someone who uses the system to obtain information.

[0014] A "request" is data indicating a request or instruction sent from a terminal to a server.

[0015] An "information provision system" is a technical configuration that includes a series of means for providing necessary information to users in multiple languages. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The present invention relates to a system that enables a user to obtain information in multiple languages ​​in real time at museums, tourist attractions, etc., by reading an information identification code.

[0038] System Overview

[0039] The system begins when a user reads an information identification code (QR code) using a device such as a smartphone or tablet. Based on the code read, the device sends a request to the server, which retrieves the corresponding source text from a database. The retrieved source text is translated into the user's language using an external translation resource, and the result is sent back to the user's device and displayed.

[0040] The program processing is described in detail below.

[0041] Program processing

[0042] 1. User scans the QR code

[0043] Users scan QR codes installed at museums and tourist attractions using their device's camera app or a dedicated QR code reader app. The QR code contains specific information (e.g., a URL).

[0044] 2. The device sends a request to the server

[0045] The device sends a request to the server, including the URL obtained from the scanned QR code and the user's language setting.

[0046] 3. The server retrieves the source text from the database

[0047] The server receives the request and retrieves the corresponding original text, such as English, from the database based on the identification information contained in the URL.

[0048] 4. The server calls the external translation resource

[0049] The server sends the retrieved source text to an external translation resource (e.g., a machine translation service) to translate it into the user's preferred language.

[0050] 5. The server sends the translation results back to the device

[0051] The server receives the translation result and returns it to the user's terminal.

[0052] 6. The device will display the translation results.

[0053] The user's device displays the translated explanation on the screen, allowing the user to understand information about the artwork or tourist spot in their own language.

[0054] Specific examples

[0055] Museum Situation

[0056] Users scan a QR code placed next to a painting on display in a museum.

[0057] The scanned QR code contains a specific URL (e.g. https: / / example.com / art123).

[0058] The device sends this URL and the user's language setting (e.g. French) to the server.

[0059] The server retrieves the original English text from the database based on the URL's identifier (e.g., "This is a famous painting by an artist from the 18th century.").

[0060] The server sends the original English text to a machine translation service, which translates it into French.

[0061] The translation result (e.g., "C'est une peinture célèbre d'un artiste du XVIIIe siècle.") is received and returned to the user's device.

[0062] The device displays the translation results on the screen, allowing the user to read the description of the work in French.

[0063] In this way, the system of the present invention can provide multilingual information in real time, thereby improving user satisfaction.

[0064] The processing flow will be explained below.

[0065] Step 1:

[0066] The user scans the QR code

[0067] Users scan QR codes placed at museums and tourist attractions with their smartphone's camera app or QR code reader app, which contains a specific URL.

[0068] Step 2:

[0069] The device sends a request to the server

[0070] The user's device sends a request to the server using the URL obtained from the scanned QR code, combined with the device's language setting (e.g., Spanish). For example, the request might look like this: GET https: / / example.com / art123?lang=es

[0071] Step 3:

[0072] The server receives the request

[0073] The server receives a request sent from the device, which includes a URL and the user's language settings.

[0074] Step 4:

[0075] The server retrieves the source text from the database.

[0076] The server accesses the database based on the URL identification information included in the request (e.g., "art123") and retrieves the corresponding English text. An example of the data retrieved is "This is a famous painting by an artist from the 18th century."

[0077] Step 5:

[0078] The server sends a request to an external translation resource

[0079] The server then sends the original English text to an external translation resource (e.g., a machine translation service) to translate it into the user's preferred language (e.g., Spanish). For example, the request might look like this: https: / / translate.example.com / translate?text=This%20is%20a%20famous%20painting%20by%20an%20artist%20from%20the%2018th%20century.&to=es

[0080] Step 6:

[0081] The server receives the translation results

[0082] Receives the translated result (e.g., "Esta es una pintura famosa de un artista del siglo XVIII.") from an external translation resource.

[0083] Step 7:

[0084] The server sends the translation results back to the device

[0085] The server then returns the translation result to the user's device. For example, the returned data might look like this: {"translation": "Esta es una pintura famosa de un artist del siglo XVIII."}

[0086] Step 8:

[0087] The device displays the translation results

[0088] The user's device displays the translation results on the screen, allowing the user to understand the descriptions of the artworks and tourist spots by reading the translation results.

[0089] Example 1

[0090] 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."

[0091] It is often difficult for visitors to obtain multilingual tourist information or museum exhibit information in real time due to language differences. Conventional guidance systems require the preparation of multilingual explanations, which makes management and updating cumbersome. There is also no guarantee that explanations in each language are always up to date. This can lead to problems such as visitors not receiving enough information and lowering satisfaction.

[0092] 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.

[0093] In this invention, the server includes means for a user to read an information identification code, means for a terminal to send a request to the server based on information acquired from the information identification code and the user's language setting, means for the server to acquire an original text from a database based on the request received, means for the server to call an external translation resource and translate the original text into the user's language, means for the server to return the translation result to the terminal, and means for the terminal to display the translation result, thereby enabling visitors to easily and efficiently acquire tourist information and exhibit information in their own language in real time.

[0094] "User" refers to a person who uses the system to obtain information.

[0095] "Information identification code" refers to a code that contains specific information in the form of a QR code or the like.

[0096] "Terminal" refers to a device used by a user, such as a smartphone or tablet.

[0097] "Server" refers to a computer system that receives requests, retrieves information from databases, and calls external translation resources.

[0098] "Database" refers to a data management system that stores and retrieves information.

[0099] "Original text" refers to the original language text before translation, obtained from the information identification code.

[0100] "Translation Resource" refers to an external automated translation service, such as a machine translation API.

[0101] "Translation result" refers to the text that results from translating the original text into the user's language.

[0102] "Two-dimensional code" refers to a code for recording information in two dimensions, such as a QR code.

[0103] "Automatic Translation Service" means a computer-based translation service provided over the Internet.

[0104] This invention relates to a system that allows a user to obtain multilingual information in real time at museums, tourist attractions, etc. by reading an information identification code. How the present invention can be put into practice will be described below.

[0105] First, a user uses a device such as a smartphone or tablet to read an information identification code, such as a QR code. The code contains specific information (e.g., a URL). At this stage, the smartphone or tablet uses a QR code reader app or camera app to scan the code.

[0106] Next, the device sends a request containing the retrieved URL and the user's language settings to the server over Wi-Fi or mobile data. The request is sent via a secure HTTP request (HTTPS).

[0107] The server analyzes the received request and retrieves the corresponding source text from a database based on the identification information included in the URL. The database management system used here can be, for example, MySQL (registered trademark). The server executes a query against the database to retrieve the required information.

[0108] The server then sends the retrieved source text to an external translation resource to translate it into the user's preferred language. This external translation resource can be an automatic translation service such as Google Translate API. The API is called using an HTTP POST request, and the transmitted content includes the source text and target language information.

[0109] The server receives the translation results and returns them to the user's device. This process is also carried out via a secure HTTP response (HTTPS). The returned results are displayed to the user on the device.

[0110] As a concrete example, consider the case where a user visiting an art museum scans a QR code next to a painting. The user uses their smartphone to scan the QR code, and the URL obtained from the scanned QR code (e.g., https: / / example.com / art123) and the user's language setting (e.g., French) are sent to the server. The server retrieves the original English text from a database based on the obtained URL, and sends it to the Google Translate API to translate it into French. The translated result is sent back from the server to the device, and a French explanation is displayed on the smartphone screen.

[0111] Below are some example prompts to input to the generative AI model:

[0112] Example:

[0113] Please explain how this system works. When a user scans a QR code at a museum or tourist attraction, the server retrieves the information, translates it into the user's preferred language, and displays it. Please also explain the specific hardware and software used, and the data processing and calculations that are performed. For example, please provide a comprehensive explanation of the process, starting from the user scanning the QR code, to the device communicating with the server to retrieve the data and display the translation results.

[0114] In this way, the system of the present invention can provide multilingual information in real time, improving user satisfaction.

[0115] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0116] Step 1:

[0117] Users use the camera app on their smartphone or tablet, or a dedicated QR code reader app, to scan QR codes installed at museums and tourist attractions. In this case, when the user holds the QR code up to the camera, the camera app reads the data in the two-dimensional code and obtains the URL. The input is an image of the QR code captured by the camera, and the output is the URL contained in the QR code (e.g., https: / / example.com / art123).

[0118] Step 2:

[0119] The device sends a request to the server that includes the URL obtained from the scanned QR code and the user's language preference. At this stage, the device sends this information to the server as an HTTPS request. Specifically, the input is the obtained URL and the user's language preference (e.g., French), and the output is an HTTPS request sent to the server.

[0120] Step 3:

[0121] Based on the received request, the server analyzes the identification information contained in the URL and retrieves the corresponding source text from the database. The input is the HTTPS request received by the server, and the database query "SELECT FROM documentation WHERE id="URL identifier"" is executed. The output is the English source text retrieved from the database (e.g., "This is a famous painting by an artist from the 18th century.").

[0122] Step 4:

[0123] The server sends the obtained source text to an external translation resource and translates it into the language selected by the user. Specifically, the input is the source text in English and the target language information (French), which are sent to a translation service such as Google Translate API. The output is the translated result (e.g., "C'est une peinture célèbre d'un artiste du XVIIIe siècle.").

[0124] Step 5:

[0125] The server receives the translation result and returns it to the user's device. The input is the translation result, which is sent to the user's device as an HTTPS response. The output is the translation result returned to the user's device.

[0126] Step 6:

[0127] The device displays the translated text on its screen, allowing users to understand the information in their own language. Specifically, the input is the translation returned from the server, and the output is the translation displayed on the smartphone screen (e.g., "C'est une peinture célèbre d'un artiste du XVIIIe siècle.").

[0128] The above processing steps enable users to easily and efficiently obtain and understand multilingual information in real time.

[0129] (Application example 1)

[0130] 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."

[0131] Despite the demand for multilingual customer service in modern brick-and-mortar stores, there is a lack of effective systems for providing multilingual information in real time. In particular, providing product and promotional information instantly in a user's native language is important for improving customer satisfaction and realizing global customer service. However, current technology makes it difficult to achieve this smoothly and efficiently.

[0132] 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.

[0133] In this invention, the server includes means for generating a prompt sentence using a translation result generation AI model and obtaining the translation result based on the prompt sentence, means for calling an external translation resource and translating the original sentence into the user's language, and means for obtaining the original sentence from a database based on a received request. This allows users to obtain in-store product information and guidance information as multilingual information in real time, significantly improving customer satisfaction.

[0134] An "information identification code" refers to a code that contains specific information, and when read by a user, serves to provide the information to the terminal.

[0135] A "terminal" is an electronic device used by a user, which has the functions of reading information identification codes, sending requests to a server, and displaying translation results.

[0136] A "server" is a computer system that retrieves original text from a database in response to a request from a terminal, translates it using external translation resources or generative AI models, and returns the results to the terminal.

[0137] A "database" is a storage system that the server references to obtain original text, and that stores the corresponding original text data.

[0138] "External translation resource" refers to an external translation service or tool that the server invokes and uses to translate source text into the user's language.

[0139] A "generative AI model" is an AI model for natural language processing, and refers to an algorithm or system that generates prompt sentences and provides translation results based on those prompt sentences.

[0140] A "prompt sentence" is an input sentence that a generative AI model uses to perform translation, and contains the information that will be the basis for the translation.

[0141] A "QR code" is a type of two-dimensional barcode that is used as an information identification code and allows users to obtain information by scanning it with a device such as a smartphone.

[0142] "Translation result" refers to a sentence that the server converts from the original text into the user's language setting using external translation resources or generative AI models.

[0143] To implement this invention, it is necessary to build a system using the following hardware and software: This system includes a user terminal, a server, a database, external translation resources, and a generative AI model.

[0144] 1. Hardware and software configuration

[0145] Device:

[0146] This refers to devices such as smartphones and tablets, and includes a camera that allows users to read information identification codes (QR codes) and a display that displays the translation results.

[0147] Use a QR code reader app or camera function.

[0148] server:

[0149] It is a cloud server (e.g., AWS (registered trademark), Google Cloud) that receives requests from the terminal, retrieves the original text from the database, and translates it using external translation resources and generative AI models.

[0150] Database:

[0151] Databases such as MySQL and PostgreSQL are used to store the original data corresponding to the information identification code.

[0152] External translation resources:

[0153] This refers to machine translation services such as Google Translate API and DeepL API, which are used by the server to translate source text into the user's specified language.

[0154] Generative AI models:

[0155] It uses generative AI models such as OpenAI® GPT-4® to adjust translation results and generate answers based on specific prompts.

[0156] 2. Program processing explanation

[0157] When the server receives a request based on the information identification code from the terminal, it retrieves the corresponding original text from the database. It then calls an external translation resource to translate the original text into the user's specified language, and inputs the translation result into a generative AI model to obtain a more natural translation result. The final translation result is then returned to the terminal based on the generated prompt text.

[0158] The terminal displays the received translation results to the user, allowing the user to view the information in their own language.

[0159] 3. Examples of concrete examples and prompts

[0160] Examples:

[0161] When a user scans a wine's QR code in a store, the URL contained in the QR code is sent to the server. The server then retrieves the appropriate information from the database using the URL and obtains the original English text, such as "This is a famous wine produced by a renowned winery in France." Using an external translation resource, this original text is translated into Japanese, and the results are passed to a generative AI model to obtain a more refined translation result.

[0162] Example prompt sentence:

[0163] Prompt: Please translate the following English sentence into Japanese.

[0164] -------------------------------------------------------------------------

[0165] This is a famous wine produced by a renowned winery in France. The red wine is known for its rich flavor and smooth finish.

[0166] This invention makes it possible to provide multilingual guides and product information in real time in physical stores, thereby improving user convenience and satisfaction.

[0167] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0168] Step 1:

[0169] The user scans the QR code.

[0170] Input: The user scans the QR code on a product or guide in the store with their smartphone camera.

[0171] How it works: A QR code reader app installed on the device reads the QR code and extracts the URL within the code.

[0172] Output: The extracted URL (e.g. https: / / example.com / product123) is displayed in the terminal.

[0173] Step 2:

[0174] The terminal sends the information to the server.

[0175] Input: The extracted URL and the user's preferred language (e.g. Japanese).

[0176] How it works: The device sends this information to the server as an HTTP request.

[0177] Output: The URL and language setting information are sent to the server.

[0178] Step 3:

[0179] The server receives the request and retrieves the original text from the database.

[0180] Input: The URL received by the server and the user's preferred language.

[0181] How it works: The server uses the URL to search the database and retrieve the corresponding English source text.

[0182] Output: The retrieved source text (e.g., "This is a famous wine produced by a renowned winery in France. The red wine is known for its rich flavor and smooth finish.").

[0183] Step 4:

[0184] The server calls an external translation resource and translates the source text.

[0185] Input: The source text retrieved by the server and the user's preferred language.

[0186] How it works: The server sends the source text to an external translation resource (e.g., Google Translate API) and translates it into the configured language (e.g., Japanese).

[0187] Output: Translation result (e.g., "This is a famous wine produced by a famous French winemaker. The red wine is characterized by its rich flavor and smooth finish.").

[0188] Step 5:

[0189] The server uses a generative AI model to generate prompt sentences and adjust the translation results.

[0190] Input: Translation result (Japanese).

[0191] How it works: The server uses a generative AI model to generate translation results based on the prompt sentence and further refines the translation results.

[0192] Output: The final adjusted translation result.

[0193] Step 6:

[0194] The server returns the final translation result to the terminal.

[0195] Input: The final adjusted translation result.

[0196] Operation: The server returns the translation result to the device as an HTTP response.

[0197] Output: The final translation result is sent to the device.

[0198] Step 7:

[0199] The device will display the translation results.

[0200] Input: The final translation result.

[0201] How it works: The device displays the final translation on the screen for the user to read.

[0202] Output: The user sees the information displayed in their preferred language.

[0203] Through this series of processes, users can obtain real-time product and guide information about physical stores in their own language simply by scanning a QR code.

[0204] 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.

[0205] The present invention is a system that allows users to obtain multilingual information in real time at museums, tourist attractions, etc. by reading information identification codes such as QR codes. In addition, by combining it with an emotion engine that recognizes the user's emotions, it has the function of adjusting the information presented according to the user's emotions. The following describes in detail the embodiments of the present invention.

[0206] System Overview

[0207] This system consists of a user's device, a server, a database, external translation resources, and an emotion engine. The user scans a QR code and sends a request to the server based on the information acquired by the device. The server retrieves the corresponding original text from the database and translates it into the user's preferred language using external translation resources. The emotion engine also recognizes the user's emotions and adjusts the display of the translation results to provide the user with more appropriate information.

[0208] Program processing

[0209] 1. User scans the QR code

[0210] Users scan QR codes placed at museums and tourist attractions with their device's camera app or QR code reader app, which contains a specific URL.

[0211] 2. The device sends a request to the server

[0212] The user's device sends a request to the server using the URL obtained from the scanned QR code, combined with the device's language setting (e.g., Spanish). For example, the request might look like this: GET https: / / example.com / art123?lang=es

[0213] 3. The server receives the request

[0214] The server receives a request sent from the device, which includes a URL and the user's language settings.

[0215] 4. The server retrieves the source text from the database

[0216] The server accesses the database based on the URL identification information included in the request (e.g., "art123") and retrieves the corresponding English text. An example of the data retrieved is "This is a famous painting by an artist from the 18th century."

[0217] 5. The server sends a request to the external translation resource

[0218] The server then sends the original English text to an external translation resource (e.g., a machine translation service) to translate it into the user's preferred language (e.g., Spanish). For example, the request might look like this: https: / / translate.example.com / translate?text=This%20is%20a%20famous%20painting%20by%20an%20artist%20from%20the%2018th%20century.&to=es

[0219] 6. The server receives the translation results

[0220] Receives the translated result (e.g., "Esta es una pintura famosa de un artista del siglo XVIII.") from an external translation resource.

[0221] 7. The server sends the translation results back to the device

[0222] The server then optimizes the translation results it receives based on the user's emotions. The emotion engine analyzes the user's emotions and adjusts the displayed translation results as needed. The final translation result is then sent back to the user's device. For example, the returned data might look like this: {"translation": "Esta es una pintura famosa de un artist del siglo XVIII."}

[0223] 8. The device will display the translation result and additional information based on the emotion.

[0224] The user's device displays the translation results and additional information based on the user's emotions on the screen, allowing the user to receive the most appropriate information that corresponds to their emotions.

[0225] Specific examples

[0226] Museum Situation

[0227] Users scan a QR code placed next to a painting on display in a museum.

[0228] The scanned QR code contains a specific URL (e.g. https: / / example.com / art123).

[0229] The device sends this URL and the user's language setting (e.g. French) to the server.

[0230] The server retrieves the original English text from the database based on the URL's identifier (e.g., "This is a famous painting by an artist from the 18th century.").

[0231] The server sends the original English text to a machine translation service, which translates it into French.

[0232] After receiving the translation result (e.g., "C'est une peinture célèbre d'un artiste du XVIIIe siècle."), the emotion engine analyzes the user's facial expressions to recognize their emotions. For example, if the user shows interest, the emotion engine adds a message such as "Click here to find out more about this artist."

[0233] The server returns the final translation result to the user's terminal.

[0234] The device displays the translation results and additional information on the screen, allowing users to read the work's description and details in French.

[0235] In this way, the system of the present invention not only provides multilingual information in real time, but also provides more comprehensive information by taking into account the user's feelings.

[0236] The processing flow will be explained below.

[0237] Step 1:

[0238] The user scans the QR code

[0239] Users scan QR codes placed at museums and tourist attractions with their smartphone camera or a dedicated QR code reader app, which contains a specific URL.

[0240] Step 2:

[0241] The device sends a request to the server

[0242] The user's device sends a request to the server using the URL obtained from the scanned QR code, combined with the device's language setting (e.g., Spanish). For example, the request might look like this: GET https: / / example.com / art123?lang=es

[0243] Step 3:

[0244] The server receives the request

[0245] The server receives a request sent from the device, which includes a URL and the user's language settings.

[0246] Step 4:

[0247] The server retrieves the source text from the database.

[0248] The server accesses the database based on the URL identification information included in the request (e.g., "art123") and retrieves the corresponding English text. An example of the data retrieved is "This is a famous painting by an artist from the 18th century."

[0249] Step 5:

[0250] The server sends a request to an external translation resource

[0251] The server then sends the original English text to an external translation resource (e.g., a machine translation service) to translate it into the user's preferred language (e.g., Spanish). For example, the request might look like this: https: / / translate.example.com / translate?text=This%20is%20a%20famous%20painting%20by%20an%20artist%20from%20the%2018th%20century.&to=es

[0252] Step 6:

[0253] The server receives the translation results

[0254] Receives the translated result (e.g., "Esta es una pintura famosa de un artista del siglo XVIII.") from an external translation resource.

[0255] Step 7:

[0256] The server calls the emotion engine and recognizes the user's emotion.

[0257] The server uses the emotion engine to analyze the user's facial expressions and voice to recognize their current emotional state. For example, if the user shows interest, the emotion engine identifies them as "interested."

[0258] Step 8:

[0259] The server adjusts the translation results based on the sentiment

[0260] The server will tailor the presentation of the translation based on the results from the emotion engine, for example including additional details or links in the translation if the user indicates interest.

[0261] Step 9:

[0262] The server sends the final translation result after adjustments back to the device.

[0263] The server then sends the final translation result back to the user's device, which may look something like this: {"translation": "This is a famous piece by an artist of the same name XVIII. For more details about the artist, click here."}

[0264] Step 10:

[0265] Your device will display the final translation result

[0266] The user's device displays the final translation results on the screen, which correspond to the user's emotions and provide more information to the user.

[0267] Example 2

[0268] 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."

[0269] In modern society, travelers and visitors often require guidance in multiple languages. However, current systems often lack translation accuracy, real-time information provision, and customization based on user preferences. As a result, users are left unsatisfied with the information they receive, reducing the value of tourist destinations and museums.

[0270] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring an original text from a database, means for calling an external translation resource and translating the original text into the user's language, and means for analyzing the user's emotions using an emotion recognition engine and optimizing the translation result based on the analysis result. This allows the user to acquire real-time information in multiple languages ​​and provides information customized according to the user's emotions.

[0271] "User" refers to the person who reads the information identification code or operates the terminal.

[0272] "Information identification code" refers to a code that contains specific information, such as a QR code.

[0273] "Terminal" refers to a computing device operated by a user, such as a smartphone or tablet.

[0274] "Server" refers to a computer system that receives requests, retrieves information from databases, and communicates with external translation resources.

[0275] A "request" refers to a request for information sent from a terminal to a server.

[0276] "Database" refers to a collection of data where information can be managed and searched.

[0277] "Source text" refers to the original text information before translation.

[0278] "External translation resources" refers to resources for performing translation, such as external machine translation services.

[0279] "Translation result" refers to the text after translating the source text into another language.

[0280] "Emotion recognition engine" refers to a combination of software and hardware for analyzing a user's emotions.

[0281] "Optimization" refers to the process of adjusting translation results based on the results of user sentiment analysis.

[0282] "Additional information" refers to supplementary information provided to the user in addition to the translation result.

[0283] "Display" refers to visualizing the translation results and additional information on the device screen.

[0284] This invention is a system that allows users to obtain multilingual information in real time at museums, tourist attractions, etc. by reading information identification codes such as QR codes. In addition, by combining it with an emotion engine that recognizes the user's emotions, it has the function of adjusting the information presented according to the user's emotions.

[0285] System configuration

[0286] This system consists of a user terminal, a server, a database, external translation resources, and an emotion engine.

[0287] 1. User's device

[0288] Smartphones, tablets, etc.

[0289] Install and use a camera app or QR code reader app

[0290] An internet connection is required

[0291] 2. Server

[0292] High-performance computer system

[0293] Receives and processes requests, performs database lookups, communicates with external translation resources, and analyzes them using the emotion engine

[0294] 3. Database

[0295] A repository of data where information can be managed and searched

[0296] Contains original text such as detailed descriptions of museums and tourist attractions

[0297] 4. External Translation Resources

[0298] Machine translation services (e.g., Google Translate API)

[0299] Highly accurate translation is possible

[0300] 5. Emotion Engine

[0301] Software for analyzing emotions from the user's facial expressions and voice

[0302] Optimize information content based on emotions

[0303] Implementation method

[0304] A user scans a QR code installed at a museum or tourist attraction, and the device sends a request to the server based on the information acquired. The server then retrieves the corresponding original text from a database and translates it into the user's preferred language using external translation resources. The emotion engine also recognizes the user's emotions and adjusts the displayed translation results to provide the user with more appropriate information. This series of operations allows users to obtain multilingual information tailored to their needs in real time.

[0305] Specific examples

[0306] In the case of an art museum

[0307] A user scans a QR code placed next to a painting on display in a museum. The scanned QR code contains a specific URL, and the device sends this URL and the user's language setting (e.g., French) to the server. The server retrieves the original English text from the database based on the URL's identification information and sends it to an external machine translation service for translation into French. The translation result (e.g., "C'est une peinture célèbre d'un artiste du XVIIIe siècle.") is received, and the emotion engine analyzes the user's facial expression to recognize their emotion. For example, if the user shows interest, the emotion engine adds an additional message such as "Click here to learn more about this painter." The server then sends the final translation result back to the user's device, which displays the translation result and additional information on the screen. This allows the user to read the description and detailed information of the work in French.

[0308] Prompt Sentence Examples

[0309] "How can users scan the QR code on their device and view exhibit information in French?"

[0310] In this way, the system of the present invention not only provides multilingual information in real time, but also provides more comprehensive information by taking into account the user's feelings.

[0311] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0312] Step 1:

[0313] A user finds a QR code installed at a museum or tourist attraction and launches their device's camera app or QR code reader app. When the user scans the QR code, the URL is loaded into the device. The input is the visual information of the QR code, and the device analyzes the QR code to obtain the URL. The output of this process is a URL (e.g., https: / / example.com / art123).

[0314] Step 2:

[0315] The device combines the URL retrieved from the QR code with the user's language preference (e.g., Spanish) and sends a request to the server. The input is the URL retrieved from the QR code and the device's language preference, which together form the request. The output of this process is the request that is sent (e.g., GET https: / / example.com / art123?lang=es).

[0316] Step 3:

[0317] The server receives a request sent from the terminal. The input is the request from the terminal. The server analyzes the URL and language settings in the request and prepares for database access. The output is database query information (e.g., "art123") based on the analysis results.

[0318] Step 4:

[0319] The server accesses the database based on the request and retrieves the corresponding English source text. The input is the database query information, and the output is the English source text retrieved from the database (e.g., "This is a famous painting by an artist from the 18th century."). The server temporarily stores this information.

[0320] Step 5:

[0321] The server takes the English source text and sends it to an external translation resource (e.g., a machine translation service) to translate it into the user's preferred language. The input is the English source text and the user's preferred language (e.g., Spanish), which are composed into the outgoing request. The output is the outgoing request (e.g., https: / / translate.example.com / translate?text=This%20is%20a%20famous%20painting%20by%20an%20artist%20from%20the%2018th%20century.&to=es).

[0322] Step 6:

[0323] The server receives the translation result from an external translation resource. The input is the response to the translation request, and the output is the translated result (e.g., "Esta es una pintura famosa de un artista del siglo XVIII."). The server temporarily stores the translation result.

[0324] Step 7:

[0325] The server analyzes the user's emotions using an emotion engine. The input is emotional data such as the user's facial expressions and voice, which the emotion recognition engine analyzes. The output of this process is the analysis result (e.g., "interested"). The server then optimizes the translation result.

[0326] Step 8:

[0327] The server returns the optimized translation to the device. The input is the optimized translation. The output is the data sent to the device (e.g., {"translation": "This is a famous piece of art by artist Siglo XVIII.", "additional_info": "To learn more about the artist, click here."}).

[0328] Step 9:

[0329] The device receives the translation results and displays additional information based on the emotion on the screen. The input is the response data from the server, which is displayed on the device's display. The output of this process is the information displayed to the user (e.g., "Esta es una pintura famosa de un artista del siglo XVIII." and "Click here for more information about this artist"). This allows the user to view optimized information in real time.

[0330] (Application example 2)

[0331] 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."

[0332] Multilingual tourist guide systems and information systems have become commonplace in recent years, but few systems provide information that takes the user's emotions into consideration. For example, when scanning a QR code at a museum or tourist attraction to obtain information, only general translations are provided, and information that does not reflect the user's emotions or interests is often not provided. As a result, information may not be properly conveyed to tired or disinterested users. Furthermore, a similar problem occurs in physical stores. When a user shows interest in a product or has a specific emotion, the information provided may not be appropriate.

[0333] 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.

[0334] In this invention, the server includes means for calling an external translation resource and translating the original text into the user's language, means for the server to return the translation result to the terminal, means for the terminal to recognize the user's emotion, and means for the server to adjust the translation result based on the user's emotion. This makes it possible for the user to read the information identification code not only to be provided with information translated in the user's language, but also to be provided with information optimal for the user's emotion.

[0335] An "information identification code" is an identifier that a user can read to obtain specific information, and generally includes QR codes and barcodes.

[0336] A "terminal" is a device used by a user to read an information identification code, and generally includes a smartphone, tablet, or PC.

[0337] A "server" is a computer system that receives requests from terminals and provides information in cooperation with databases and external translation resources.

[0338] A "database" is an information storage system that stores original text corresponding to information identification codes.

[0339] An "external translation resource" is a translation service that the server invokes and uses to translate source text into the user's language, including machine translation services.

[0340] An "emotion engine" is a software or hardware component that uses the device's camera and sensors to analyze the user's emotions.

[0341] "Translation result" is information obtained by converting an original text into a user's language using an external translation resource.

[0342] "Information adjustment" is the process of appropriately modifying and enhancing the content of translation results and additional information based on the user's feelings.

[0343] The system for implementing the present invention consists of a series of processes in which a user reads an information identification code, the terminal sends it to a server, the server performs translation and emotion recognition, and then returns optimized information to the terminal.

[0344] composition

[0345] The system includes the following elements and procedures:

[0346] User terminal: A device used to read information identification codes, such as a smartphone, tablet, or PC.

[0347] Information Identification Code: A QR code or other code used to retrieve specific information.

[0348] Server: Processes incoming requests and performs translation by accessing databases and external translation resources.

[0349] Database: An information storage system that stores corresponding source texts.

[0350] External translation resources: A service that uses a machine translation service to translate source text into the user's language.

[0351] Emotion engine: A software and hardware component that recognizes a user's emotions and optimizes information based on those emotions.

[0352] Translation and information provision process

[0353] 1. Scanning a QR code: When a user scans a QR code with their device's camera or a QR code reader app, a specific URL is retrieved on the device.

[0354] 2. Sending a request: The device sends a request to the server based on the URL and the device's language setting. For example, the request format is "GET https: / / example.com / info123?lang=ja".

[0355] 3. Retrieving information from a database: The server accesses the database based on the identifying information in the URL included in the request and retrieves the relevant original text.

[0356] 4. Send translation request: The server sends the retrieved source text to an external machine translation service to translate it into the specified language.

[0357] 5. Getting the translation: The server receives the translated information and prepares to serve it in the user's preferred language.

[0358] 6. Emotion Recognition and Optimization: The device camera is used to analyze the user's emotions, and the information is optimized based on this emotion recognition, for example, providing additional information if the user is interested.

[0359] 7. Return and display information: The final information is returned to the terminal, which displays it on the screen.

[0360] Technologies used and data processing

[0361] Camera API: Uses the user's device's camera to scan QR codes and analyze facial expressions.

[0362] Translation API: Translates the source text into a specified language using an external machine translation service (e.g., Google Translate API).

[0363] Emotion Recognition API: An API for recognizing the user's emotions (for example, Microsoft® Azure® emotion recognition service) is used.

[0364] Specific examples

[0365] For example, when a user scans a QR code placed next to an exhibit in a museum, the following process takes place:

[0366] The user scans the QR code with their smartphone to obtain the URL.

[0367] The obtained URL and the smartphone's language setting (e.g., Japanese) are sent to the server.

[0368] The server retrieves the original English text from the database and then translates it into Japanese using a machine translation service.

[0369] The server receives the translated information and optimizes the information by recognizing the user's emotions.

[0370] The final information is sent back to the user's terminal, and the user views the displayed information.

[0371] Prompt Sentence Examples

[0372] For example, you can use a generative AI model by entering a prompt such as, "Implement an application in JavaScript (registered trademark) that scans the QR code of an exhibited work and provides multilingual information based on the user's emotions."

[0373] According to this embodiment, the present invention can provide advanced information in multiple languages ​​and taking into consideration the user's feelings, thereby improving the user experience.

[0374] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0375] Step 1:

[0376] The user reads the information identification code (QR code).

[0377] A user scans the information identification code using a camera app or QR code reader app on their device (e.g., a smartphone). This action causes a specific URL to be read by the device. The input is the scanned QR code, and the output is the retrieved URL. Specifically, the camera reads the QR code and retrieves the data as a string.

[0378] Step 2:

[0379] The device sends the URL and the device's language setting to the server.

[0380] The device combines the obtained URL with the device's current language setting (e.g., Japanese) and sends a request to the server. The input is the scanned URL and the device's language setting, and the output is the request sent to the server. Specifically, the URL and language setting are formatted into an HTTP request and sent to the server.

[0381] Step 3:

[0382] The server retrieves the corresponding source text from the database.

[0383] The server analyzes the received request and retrieves the corresponding text from a database based on the URL identification information. The input is the request sent to the server, and the output is the text retrieved from the database. The specific operation is to execute a database query to extract the corresponding information.

[0384] Step 4:

[0385] The server calls an external translation resource to translate the source text.

[0386] The server uses the retrieved source text to send a request to an external machine translation service to translate it into the specified language. The input is the retrieved source text, and the output is the translation result returned by the external translation service. Specifically, it calls the translation API and receives the result.

[0387] Step 5:

[0388] The server receives the translation result and sends a request to the device for emotion recognition.

[0389] The server receives the translation result returned from the external translation resource and attaches it to a request sent to the user's device. In addition, the device receives an additional request to recognize the user's emotion. The input is the received translation result, and the output is a request to the device. Specific operations include preparing the received data and sending an HTTP request to the device.

[0390] Step 6:

[0391] The device recognizes the user's emotions.

[0392] The device uses a camera to analyze the user's face and recognizes the user's emotions using an emotion engine. The input is the user's face image taken with the device's camera, and the output is the recognized emotion data. Specifically, the emotion recognition API is used to extract the emotion data.

[0393] Step 7:

[0394] The server adjusts the translation results based on the user's emotions.

[0395] The server receives the emotion data sent from the device and adjusts the translation results based on it. For example, if the user shows interest, it inserts additional relevant information. The input is emotion data and the translation result, and the output is the final translation result that takes emotion into consideration. Specifically, it performs conditional judgments on the emotion data and modifies the information.

[0396] Step 8:

[0397] The server sends the final translation result back to the terminal, which displays it.

[0398] The server returns the adjusted final translation result to the terminal, which displays it on the screen. The input is the adjusted translation result, and the output is the information displayed on the terminal. The specific operation is to send the data to the terminal and process it to display it on the screen.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] [Second embodiment]

[0403] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0404] 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.

[0405] 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).

[0406] 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.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] In the smart glasses 214, 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.

[0414] 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."

[0415] The present invention relates to a system that enables a user to obtain information in multiple languages ​​in real time at museums, tourist attractions, etc., by reading an information identification code.

[0416] System Overview

[0417] The system begins when a user reads an information identification code (QR code) using a device such as a smartphone or tablet. Based on the code read, the device sends a request to the server, which retrieves the corresponding source text from a database. The retrieved source text is translated into the user's language using an external translation resource, and the result is sent back to the user's device and displayed.

[0418] The program processing is described in detail below.

[0419] Program processing

[0420] 1. User scans the QR code

[0421] Users scan QR codes installed at museums and tourist attractions using their device's camera app or a dedicated QR code reader app. The QR code contains specific information (e.g., a URL).

[0422] 2. The device sends a request to the server

[0423] The device sends a request to the server, including the URL obtained from the scanned QR code and the user's language setting.

[0424] 3. The server retrieves the source text from the database

[0425] The server receives the request and retrieves the corresponding original text, such as English, from the database based on the identification information contained in the URL.

[0426] 4. The server calls the external translation resource

[0427] The server sends the retrieved source text to an external translation resource (e.g., a machine translation service) to translate it into the user's preferred language.

[0428] 5. The server sends the translation results back to the device

[0429] The server receives the translation result and returns it to the user's terminal.

[0430] 6. The device will display the translation results.

[0431] The user's device displays the translated explanation on the screen, allowing the user to understand information about the artwork or tourist spot in their own language.

[0432] Specific examples

[0433] Museum Situation

[0434] Users scan a QR code placed next to a painting on display in a museum.

[0435] The scanned QR code contains a specific URL (e.g. https: / / example.com / art123).

[0436] The device sends this URL and the user's language setting (e.g. French) to the server.

[0437] The server retrieves the original English text from the database based on the URL's identifier (e.g., "This is a famous painting by an artist from the 18th century.").

[0438] The server sends the original English text to a machine translation service, which translates it into French.

[0439] The translation result (e.g., "C'est une peinture célèbre d'un artiste du XVIIIe siècle.") is received and returned to the user's device.

[0440] The device displays the translation results on the screen, allowing the user to read the description of the work in French.

[0441] In this way, the system of the present invention can provide multilingual information in real time, thereby improving user satisfaction.

[0442] The processing flow will be explained below.

[0443] Step 1:

[0444] The user scans the QR code

[0445] Users scan QR codes placed at museums and tourist attractions with their smartphone's camera app or QR code reader app, which contains a specific URL.

[0446] Step 2:

[0447] The device sends a request to the server

[0448] The user's device sends a request to the server using the URL obtained from the scanned QR code, combined with the device's language setting (e.g., Spanish). For example, the request might look like this: GET https: / / example.com / art123?lang=es

[0449] Step 3:

[0450] The server receives the request

[0451] The server receives a request sent from the device, which includes a URL and the user's language settings.

[0452] Step 4:

[0453] The server retrieves the source text from the database.

[0454] The server accesses the database based on the URL identification information included in the request (e.g., "art123") and retrieves the corresponding English text. An example of the data retrieved is "This is a famous painting by an artist from the 18th century."

[0455] Step 5:

[0456] The server sends a request to an external translation resource

[0457] The server then sends the original English text to an external translation resource (e.g., a machine translation service) to translate it into the user's preferred language (e.g., Spanish). For example, the request might look like this: https: / / translate.example.com / translate?text=This%20is%20a%20famous%20painting%20by%20an%20artist%20from%20the%2018th%20century.&to=es

[0458] Step 6:

[0459] The server receives the translation results

[0460] Receives the translated result (e.g., "Esta es una pintura famosa de un artista del siglo XVIII.") from an external translation resource.

[0461] Step 7:

[0462] The server sends the translation results back to the device

[0463] The server then returns the translation result to the user's device. For example, the returned data might look like this: {"translation": "Esta es una pintura famosa de un artist del siglo XVIII."}

[0464] Step 8:

[0465] The device displays the translation results

[0466] The user's device displays the translation results on the screen, allowing the user to understand the descriptions of the artworks and tourist spots by reading the translation results.

[0467] Example 1

[0468] 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."

[0469] It is often difficult for visitors to obtain multilingual tourist information or museum exhibit information in real time due to language differences. Conventional guidance systems require the preparation of multilingual explanations, which makes management and updating cumbersome. There is also no guarantee that explanations in each language are always up to date. This can lead to problems such as visitors not receiving enough information and lowering satisfaction.

[0470] 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.

[0471] In this invention, the server includes means for a user to read an information identification code, means for a terminal to send a request to the server based on information acquired from the information identification code and the user's language setting, means for the server to acquire an original text from a database based on the request received, means for the server to call an external translation resource and translate the original text into the user's language, means for the server to return the translation result to the terminal, and means for the terminal to display the translation result, thereby enabling visitors to easily and efficiently acquire tourist information and exhibit information in their own language in real time.

[0472] "User" refers to a person who uses the system to obtain information.

[0473] "Information identification code" refers to a code that contains specific information in the form of a QR code or the like.

[0474] "Terminal" refers to a device used by a user, such as a smartphone or tablet.

[0475] "Server" refers to a computer system that receives requests, retrieves information from databases, and calls external translation resources.

[0476] "Database" refers to a data management system that stores and retrieves information.

[0477] "Original text" refers to the original language text before translation, obtained from the information identification code.

[0478] "Translation Resource" refers to an external automated translation service, such as a machine translation API.

[0479] "Translation result" refers to the text that results from translating the original text into the user's language.

[0480] "Two-dimensional code" refers to a code for recording information in two dimensions, such as a QR code.

[0481] "Automatic Translation Service" means a computer-based translation service provided over the Internet.

[0482] This invention relates to a system that allows a user to obtain multilingual information in real time at museums, tourist attractions, etc. by reading an information identification code. How the present invention can be put into practice will be described below.

[0483] First, a user uses a device such as a smartphone or tablet to read an information identification code, such as a QR code. The code contains specific information (e.g., a URL). At this stage, the smartphone or tablet uses a QR code reader app or camera app to scan the code.

[0484] Next, the device sends a request containing the retrieved URL and the user's language settings to the server over Wi-Fi or mobile data. The request is sent via a secure HTTP request (HTTPS).

[0485] The server analyzes the received request and retrieves the corresponding source text from a database based on the identification information contained in the URL. The database management system used here is MySQL, for example. The server executes a query against the database to retrieve the required information.

[0486] The server then sends the source text to an external translation resource to translate it into the user's language. This external translation resource can be an automatic translation service such as Google Translate API. The API is called using an HTTP POST request, and the transmitted content includes the source text and the target language information.

[0487] The server receives the translation results and returns them to the user's device. This process is also carried out via a secure HTTP response (HTTPS). The returned results are displayed to the user on the device.

[0488] As a concrete example, consider the case where a user visiting an art museum scans a QR code next to a painting. The user uses their smartphone to scan the QR code, and the URL obtained from the scanned QR code (e.g., https: / / example.com / art123) and the user's language setting (e.g., French) are sent to the server. The server retrieves the original English text from a database based on the obtained URL, and sends it to the Google Translate API to translate it into French. The translated result is sent back from the server to the device, and a French explanation is displayed on the smartphone screen.

[0489] Below are some example prompts to input to the generative AI model:

[0490] Example:

[0491] Please explain how this system works. When a user scans a QR code at a museum or tourist attraction, the server retrieves the information, translates it into the user's preferred language, and displays it. Please also explain the specific hardware and software used, and the data processing and calculations that are performed. For example, please provide a comprehensive explanation of the process, starting from the user scanning the QR code, to the device communicating with the server to retrieve the data and display the translation results.

[0492] In this way, the system of the present invention can provide multilingual information in real time, improving user satisfaction.

[0493] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0494] Step 1:

[0495] Users use the camera app on their smartphone or tablet, or a dedicated QR code reader app, to scan QR codes installed at museums and tourist attractions. In this case, when the user holds the QR code up to the camera, the camera app reads the data in the two-dimensional code and obtains the URL. The input is an image of the QR code captured by the camera, and the output is the URL contained in the QR code (e.g., https: / / example.com / art123).

[0496] Step 2:

[0497] The device sends a request to the server that includes the URL obtained from the scanned QR code and the user's language preference. At this stage, the device sends this information to the server as an HTTPS request. Specifically, the input is the obtained URL and the user's language preference (e.g., French), and the output is an HTTPS request sent to the server.

[0498] Step 3:

[0499] Based on the received request, the server analyzes the identification information contained in the URL and retrieves the corresponding source text from the database. The input is the HTTPS request received by the server, and the database query "SELECT FROM documentation WHERE id="URL identifier"" is executed. The output is the English source text retrieved from the database (e.g., "This is a famous painting by an artist from the 18th century.").

[0500] Step 4:

[0501] The server sends the obtained source text to an external translation resource and translates it into the language selected by the user. Specifically, the input is the source text in English and the target language information (French), which are sent to a translation service such as Google Translate API. The output is the translated result (e.g., "C'est une peinture célèbre d'un artiste du XVIIIe siècle.").

[0502] Step 5:

[0503] The server receives the translation result and returns it to the user's device. The input is the translation result, which is sent to the user's device as an HTTPS response. The output is the translation result returned to the user's device.

[0504] Step 6:

[0505] The device displays the translated text on its screen, allowing users to understand the information in their own language. Specifically, the input is the translation returned from the server, and the output is the translation displayed on the smartphone screen (e.g., "C'est une peinture célèbre d'un artiste du XVIIIe siècle.").

[0506] The above processing steps enable users to easily and efficiently obtain and understand multilingual information in real time.

[0507] (Application example 1)

[0508] 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."

[0509] Despite the demand for multilingual customer service in modern brick-and-mortar stores, there is a lack of effective systems for providing multilingual information in real time. In particular, providing product and promotional information instantly in a user's native language is important for improving customer satisfaction and realizing global customer service. However, current technology makes it difficult to achieve this smoothly and efficiently.

[0510] 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.

[0511] In this invention, the server includes means for generating a prompt sentence using a translation result generation AI model and obtaining the translation result based on the prompt sentence, means for calling an external translation resource and translating the original sentence into the user's language, and means for obtaining the original sentence from a database based on a received request. This allows users to obtain in-store product information and guidance information as multilingual information in real time, significantly improving customer satisfaction.

[0512] An "information identification code" refers to a code that contains specific information, and when read by a user, serves to provide the information to the terminal.

[0513] A "terminal" is an electronic device used by a user, which has the functions of reading information identification codes, sending requests to a server, and displaying translation results.

[0514] A "server" is a computer system that retrieves original text from a database in response to a request from a terminal, translates it using external translation resources or generative AI models, and returns the results to the terminal.

[0515] A "database" is a storage system that the server references to obtain original text, and that stores the corresponding original text data.

[0516] "External translation resource" refers to an external translation service or tool that the server invokes and uses to translate source text into the user's language.

[0517] A "generative AI model" is an AI model for natural language processing, and refers to an algorithm or system that generates prompt sentences and provides translation results based on those prompt sentences.

[0518] A "prompt sentence" is an input sentence that a generative AI model uses to perform translation, and contains the information that will be the basis for the translation.

[0519] A "QR code" is a type of two-dimensional barcode that is used as an information identification code and allows users to obtain information by scanning it with a device such as a smartphone.

[0520] "Translation result" refers to a sentence that the server converts from the original text into the user's language setting using external translation resources or generative AI models.

[0521] To implement this invention, it is necessary to build a system using the following hardware and software: This system includes a user terminal, a server, a database, external translation resources, and a generative AI model.

[0522] 1. Hardware and software configuration

[0523] Device:

[0524] This refers to devices such as smartphones and tablets, and includes a camera that allows users to read information identification codes (QR codes) and a display that displays the translation results.

[0525] Use a QR code reader app or camera function.

[0526] server:

[0527] It is a cloud server (e.g., AWS, Google Cloud) that receives requests from devices, retrieves source text from a database, and translates it using external translation resources and generative AI models.

[0528] Database:

[0529] Databases such as MySQL and PostgreSQL are used to store the original data corresponding to the information identification code.

[0530] External translation resources:

[0531] This refers to machine translation services such as Google Translate API and DeepL API, which are used by the server to translate source text into the user's specified language.

[0532] Generative AI models:

[0533] It uses generative AI models such as OpenAI GPT-4 to adjust translation results and generate answers based on specific prompts.

[0534] 2. Program processing explanation

[0535] When the server receives a request based on the information identification code from the terminal, it retrieves the corresponding original text from the database. It then calls an external translation resource to translate the original text into the user's specified language, and inputs the translation result into a generative AI model to obtain a more natural translation result. The final translation result is then returned to the terminal based on the generated prompt text.

[0536] The terminal displays the received translation results to the user, allowing the user to view the information in their own language.

[0537] 3. Examples of concrete examples and prompts

[0538] Examples:

[0539] When a user scans a wine's QR code in a store, the URL contained in the QR code is sent to the server. The server then retrieves the appropriate information from the database using the URL and obtains the original English text, such as "This is a famous wine produced by a renowned winery in France." Using an external translation resource, this original text is translated into Japanese, and the results are passed to a generative AI model to obtain a more refined translation result.

[0540] Example prompt sentence:

[0541] Prompt: Please translate the following English sentence into Japanese.

[0542] -------------------------------------------------------------------------

[0543] This is a famous wine produced by a renowned winery in France. The red wine is known for its rich flavor and smooth finish.

[0544] This invention makes it possible to provide multilingual guides and product information in real time in physical stores, thereby improving user convenience and satisfaction.

[0545] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0546] Step 1:

[0547] The user scans the QR code.

[0548] Input: The user scans the QR code on a product or guide in the store with their smartphone camera.

[0549] How it works: A QR code reader app installed on the device reads the QR code and extracts the URL within the code.

[0550] Output: The extracted URL (e.g. https: / / example.com / product123) is displayed in the terminal.

[0551] Step 2:

[0552] The terminal sends the information to the server.

[0553] Input: The extracted URL and the user's preferred language (e.g. Japanese).

[0554] How it works: The device sends this information to the server as an HTTP request.

[0555] Output: The URL and language setting information are sent to the server.

[0556] Step 3:

[0557] The server receives the request and retrieves the original text from the database.

[0558] Input: The URL received by the server and the user's preferred language.

[0559] How it works: The server uses the URL to search the database and retrieve the corresponding English source text.

[0560] Output: The retrieved source text (e.g., "This is a famous wine produced by a renowned winery in France. The red wine is known for its rich flavor and smooth finish.").

[0561] Step 4:

[0562] The server calls an external translation resource and translates the source text.

[0563] Input: The source text retrieved by the server and the user's preferred language.

[0564] How it works: The server sends the source text to an external translation resource (e.g., Google Translate API) and translates it into the configured language (e.g., Japanese).

[0565] Output: Translation result (e.g., "This is a famous wine produced by a famous French winemaker. The red wine is characterized by its rich flavor and smooth finish.").

[0566] Step 5:

[0567] The server uses a generative AI model to generate prompt sentences and adjust the translation results.

[0568] Input: Translation result (Japanese).

[0569] How it works: The server uses a generative AI model to generate translation results based on the prompt sentence and further refines the translation results.

[0570] Output: The final adjusted translation result.

[0571] Step 6:

[0572] The server returns the final translation result to the terminal.

[0573] Input: The final adjusted translation result.

[0574] Operation: The server returns the translation result to the device as an HTTP response.

[0575] Output: The final translation result is sent to the device.

[0576] Step 7:

[0577] The device will display the translation results.

[0578] Input: The final translation result.

[0579] How it works: The device displays the final translation on the screen for the user to read.

[0580] Output: The user sees the information displayed in their preferred language.

[0581] Through this series of processes, users can obtain real-time product and guide information about physical stores in their own language simply by scanning a QR code.

[0582] 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.

[0583] The present invention is a system that allows users to obtain multilingual information in real time at museums, tourist attractions, etc. by reading information identification codes such as QR codes. In addition, by combining it with an emotion engine that recognizes the user's emotions, it has the function of adjusting the information presented according to the user's emotions. The following describes in detail the embodiments of the present invention.

[0584] System Overview

[0585] This system consists of a user's device, a server, a database, external translation resources, and an emotion engine. The user scans a QR code and sends a request to the server based on the information acquired by the device. The server retrieves the corresponding original text from the database and translates it into the user's preferred language using external translation resources. The emotion engine also recognizes the user's emotions and adjusts the display of the translation results to provide the user with more appropriate information.

[0586] Program processing

[0587] 1. User scans the QR code

[0588] Users scan QR codes placed at museums and tourist attractions with their device's camera app or QR code reader app, which contains a specific URL.

[0589] 2. The device sends a request to the server

[0590] The user's device sends a request to the server using the URL obtained from the scanned QR code, combined with the device's language setting (e.g., Spanish). For example, the request might look like this: GET https: / / example.com / art123?lang=es

[0591] 3. The server receives the request

[0592] The server receives a request sent from the device, which includes a URL and the user's language settings.

[0593] 4. The server retrieves the source text from the database

[0594] The server accesses the database based on the URL identification information included in the request (e.g., "art123") and retrieves the corresponding English text. An example of the data retrieved is "This is a famous painting by an artist from the 18th century."

[0595] 5. The server sends a request to the external translation resource

[0596] The server then sends the original English text to an external translation resource (e.g., a machine translation service) to translate it into the user's preferred language (e.g., Spanish). For example, the request might look like this: https: / / translate.example.com / translate?text=This%20is%20a%20famous%20painting%20by%20an%20artist%20from%20the%2018th%20century.&to=es

[0597] 6. The server receives the translation results

[0598] Receives the translated result (e.g., "Esta es una pintura famosa de un artista del siglo XVIII.") from an external translation resource.

[0599] 7. The server sends the translation results back to the device

[0600] The server then optimizes the translation results it receives based on the user's emotions. The emotion engine analyzes the user's emotions and adjusts the displayed translation results as needed. The final translation result is then sent back to the user's device. For example, the returned data might look like this: {"translation": "Esta es una pintura famosa de un artist del siglo XVIII."}

[0601] 8. The device will display the translation result and additional information based on the emotion.

[0602] The user's device displays the translation results and additional information based on the user's emotions on the screen, allowing the user to receive the most appropriate information that corresponds to their emotions.

[0603] Specific examples

[0604] Museum Situation

[0605] Users scan a QR code placed next to a painting on display in a museum.

[0606] The scanned QR code contains a specific URL (e.g. https: / / example.com / art123).

[0607] The device sends this URL and the user's language setting (e.g. French) to the server.

[0608] The server retrieves the original English text from the database based on the URL's identifier (e.g., "This is a famous painting by an artist from the 18th century.").

[0609] The server sends the original English text to a machine translation service, which translates it into French.

[0610] After receiving the translation result (e.g., "C'est une peinture célèbre d'un artiste du XVIIIe siècle."), the emotion engine analyzes the user's facial expressions to recognize their emotions. For example, if the user shows interest, the emotion engine adds a message such as "Click here to find out more about this artist."

[0611] The server returns the final translation result to the user's terminal.

[0612] The device displays the translation results and additional information on the screen, allowing users to read the work's description and details in French.

[0613] In this way, the system of the present invention not only provides multilingual information in real time, but also provides more comprehensive information by taking into account the user's feelings.

[0614] The processing flow will be explained below.

[0615] Step 1:

[0616] The user scans the QR code

[0617] Users scan QR codes placed at museums and tourist attractions with their smartphone camera or a dedicated QR code reader app, which contains a specific URL.

[0618] Step 2:

[0619] The device sends a request to the server

[0620] The user's device sends a request to the server using the URL obtained from the scanned QR code, combined with the device's language setting (e.g., Spanish). For example, the request might look like this: GET https: / / example.com / art123?lang=es

[0621] Step 3:

[0622] The server receives the request

[0623] The server receives a request sent from the device, which includes a URL and the user's language settings.

[0624] Step 4:

[0625] The server retrieves the source text from the database.

[0626] The server accesses the database based on the URL identification information included in the request (e.g., "art123") and retrieves the corresponding English text. An example of the data retrieved is "This is a famous painting by an artist from the 18th century."

[0627] Step 5:

[0628] The server sends a request to an external translation resource

[0629] The server then sends the original English text to an external translation resource (e.g., a machine translation service) to translate it into the user's preferred language (e.g., Spanish). For example, the request might look like this: https: / / translate.example.com / translate?text=This%20is%20a%20famous%20painting%20by%20an%20artist%20from%20the%2018th%20century.&to=es

[0630] Step 6:

[0631] The server receives the translation results

[0632] Receives the translated result (e.g., "Esta es una pintura famosa de un artista del siglo XVIII.") from an external translation resource.

[0633] Step 7:

[0634] The server calls the emotion engine and recognizes the user's emotion.

[0635] The server uses the emotion engine to analyze the user's facial expressions and voice to recognize their current emotional state. For example, if the user shows interest, the emotion engine identifies them as "interested."

[0636] Step 8:

[0637] The server adjusts the translation results based on the sentiment

[0638] The server will tailor the presentation of the translation based on the results from the emotion engine, for example including additional details or links in the translation if the user indicates interest.

[0639] Step 9:

[0640] The server sends the final translation result after adjustments back to the device.

[0641] The server then sends the final translation result back to the user's device, which may look something like this: {"translation": "This is a famous piece by an artist of the same name XVIII. For more details about the artist, click here."}

[0642] Step 10:

[0643] Your device will display the final translation result

[0644] The user's device displays the final translation results on the screen, which correspond to the user's emotions and provide more information to the user.

[0645] Example 2

[0646] 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."

[0647] In modern society, travelers and visitors often require guidance in multiple languages. However, current systems often lack translation accuracy, real-time information provision, and customization based on user preferences. As a result, users are left unsatisfied with the information they receive, reducing the value of tourist destinations and museums.

[0648] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring an original text from a database, means for calling an external translation resource and translating the original text into the user's language, and means for analyzing the user's emotions using an emotion recognition engine and optimizing the translation result based on the analysis result. This allows the user to acquire real-time information in multiple languages ​​and provides information customized according to the user's emotions.

[0649] "User" refers to the person who reads the information identification code or operates the terminal.

[0650] "Information identification code" refers to a code that contains specific information, such as a QR code.

[0651] "Terminal" refers to a computing device operated by a user, such as a smartphone or tablet.

[0652] "Server" refers to a computer system that receives requests, retrieves information from databases, and communicates with external translation resources.

[0653] A "request" refers to a request for information sent from a terminal to a server.

[0654] "Database" refers to a collection of data where information can be managed and searched.

[0655] "Source text" refers to the original text information before translation.

[0656] "External translation resources" refers to resources for performing translation, such as external machine translation services.

[0657] "Translation result" refers to the text after translating the source text into another language.

[0658] "Emotion recognition engine" refers to a combination of software and hardware for analyzing a user's emotions.

[0659] "Optimization" refers to the process of adjusting translation results based on the results of user sentiment analysis.

[0660] "Additional information" refers to supplementary information provided to the user in addition to the translation result.

[0661] "Display" refers to visualizing the translation results and additional information on the device screen.

[0662] This invention is a system that allows users to obtain multilingual information in real time at museums, tourist attractions, etc. by reading information identification codes such as QR codes. In addition, by combining it with an emotion engine that recognizes the user's emotions, it has the function of adjusting the information presented according to the user's emotions.

[0663] System configuration

[0664] This system consists of a user terminal, a server, a database, external translation resources, and an emotion engine.

[0665] 1. User's device

[0666] Smartphones, tablets, etc.

[0667] Install and use a camera app or QR code reader app

[0668] An internet connection is required

[0669] 2. Server

[0670] High-performance computer system

[0671] Receives and processes requests, performs database lookups, communicates with external translation resources, and analyzes them using the emotion engine

[0672] 3. Database

[0673] A repository of data where information can be managed and searched

[0674] Contains original text such as detailed descriptions of museums and tourist attractions

[0675] 4. External Translation Resources

[0676] Machine translation services (e.g., Google Translate API)

[0677] Highly accurate translation is possible

[0678] 5. Emotion Engine

[0679] Software for analyzing emotions from the user's facial expressions and voice

[0680] Optimize information content based on emotions

[0681] Implementation method

[0682] A user scans a QR code installed at a museum or tourist attraction, and the device sends a request to the server based on the information acquired. The server then retrieves the corresponding original text from a database and translates it into the user's preferred language using external translation resources. The emotion engine also recognizes the user's emotions and adjusts the displayed translation results to provide the user with more appropriate information. This series of operations allows users to obtain multilingual information tailored to their needs in real time.

[0683] Specific examples

[0684] In the case of an art museum

[0685] A user scans a QR code placed next to a painting on display in a museum. The scanned QR code contains a specific URL, and the device sends this URL and the user's language setting (e.g., French) to the server. The server retrieves the original English text from the database based on the URL's identification information and sends it to an external machine translation service for translation into French. The translation result (e.g., "C'est une peinture célèbre d'un artiste du XVIIIe siècle.") is received, and the emotion engine analyzes the user's facial expression to recognize their emotion. For example, if the user shows interest, the emotion engine adds an additional message such as "Click here to learn more about this painter." The server then sends the final translation result back to the user's device, which displays the translation result and additional information on the screen. This allows the user to read the description and detailed information of the work in French.

[0686] Prompt Sentence Examples

[0687] "How can users scan the QR code on their device and view exhibit information in French?"

[0688] In this way, the system of the present invention not only provides multilingual information in real time, but also provides more comprehensive information by taking into account the user's feelings.

[0689] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0690] Step 1:

[0691] A user finds a QR code installed at a museum or tourist attraction and launches their device's camera app or QR code reader app. When the user scans the QR code, the URL is loaded into the device. The input is the visual information of the QR code, and the device analyzes the QR code to obtain the URL. The output of this process is a URL (e.g., https: / / example.com / art123).

[0692] Step 2:

[0693] The device combines the URL retrieved from the QR code with the user's language preference (e.g., Spanish) and sends a request to the server. The input is the URL retrieved from the QR code and the device's language preference, which together form the request. The output of this process is the request that is sent (e.g., GET https: / / example.com / art123?lang=es).

[0694] Step 3:

[0695] The server receives a request sent from the terminal. The input is the request from the terminal. The server analyzes the URL and language settings in the request and prepares for database access. The output is database query information (e.g., "art123") based on the analysis results.

[0696] Step 4:

[0697] The server accesses the database based on the request and retrieves the corresponding English source text. The input is the database query information, and the output is the English source text retrieved from the database (e.g., "This is a famous painting by an artist from the 18th century."). The server temporarily stores this information.

[0698] Step 5:

[0699] The server takes the English source text and sends it to an external translation resource (e.g., a machine translation service) to translate it into the user's preferred language. The input is the English source text and the user's preferred language (e.g., Spanish), which are composed into the outgoing request. The output is the outgoing request (e.g., https: / / translate.example.com / translate?text=This%20is%20a%20famous%20painting%20by%20an%20artist%20from%20the%2018th%20century.&to=es).

[0700] Step 6:

[0701] The server receives the translation result from an external translation resource. The input is the response to the translation request, and the output is the translated result (e.g., "Esta es una pintura famosa de un artista del siglo XVIII."). The server temporarily stores the translation result.

[0702] Step 7:

[0703] The server analyzes the user's emotions using an emotion engine. The input is emotional data such as the user's facial expressions and voice, which the emotion recognition engine analyzes. The output of this process is the analysis result (e.g., "interested"). The server then optimizes the translation result.

[0704] Step 8:

[0705] The server returns the optimized translation to the device. The input is the optimized translation. The output is the data sent to the device (e.g., {"translation": "This is a famous piece of art by artist Siglo XVIII.", "additional_info": "To learn more about the artist, click here."}).

[0706] Step 9:

[0707] The device receives the translation results and displays additional information based on the emotion on the screen. The input is the response data from the server, which is displayed on the device's display. The output of this process is the information displayed to the user (e.g., "Esta es una pintura famosa de un artista del siglo XVIII." and "Click here for more information about this artist"). This allows the user to view optimized information in real time.

[0708] (Application example 2)

[0709] 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."

[0710] Multilingual tourist guide systems and information systems have become commonplace in recent years, but few systems provide information that takes the user's emotions into consideration. For example, when scanning a QR code at a museum or tourist attraction to obtain information, only general translations are provided, and information that does not reflect the user's emotions or interests is often not provided. As a result, information may not be properly conveyed to tired or disinterested users. Furthermore, a similar problem occurs in physical stores. When a user shows interest in a product or has a specific emotion, the information provided may not be appropriate.

[0711] 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.

[0712] In this invention, the server includes means for calling an external translation resource and translating the original text into the user's language, means for the server to return the translation result to the terminal, means for the terminal to recognize the user's emotion, and means for the server to adjust the translation result based on the user's emotion. This makes it possible for the user to read the information identification code not only to be provided with information translated in the user's language, but also to be provided with information optimal for the user's emotion.

[0713] An "information identification code" is an identifier that a user can read to obtain specific information, and generally includes QR codes and barcodes.

[0714] A "terminal" is a device used by a user to read an information identification code, and generally includes a smartphone, tablet, or PC.

[0715] A "server" is a computer system that receives requests from terminals and provides information in cooperation with databases and external translation resources.

[0716] A "database" is an information storage system that stores original text corresponding to information identification codes.

[0717] An "external translation resource" is a translation service that the server invokes and uses to translate source text into the user's language, including machine translation services.

[0718] An "emotion engine" is a software or hardware component that uses the device's camera and sensors to analyze the user's emotions.

[0719] "Translation result" is information obtained by converting an original text into a user's language using an external translation resource.

[0720] "Information adjustment" is the process of appropriately modifying and enhancing the content of translation results and additional information based on the user's feelings.

[0721] The system for implementing the present invention consists of a series of processes in which a user reads an information identification code, the terminal sends it to a server, the server performs translation and emotion recognition, and then returns optimized information to the terminal.

[0722] composition

[0723] The system includes the following elements and procedures:

[0724] User terminal: A device used to read information identification codes, such as a smartphone, tablet, or PC.

[0725] Information Identification Code: A QR code or other code used to retrieve specific information.

[0726] Server: Processes incoming requests and performs translation by accessing databases and external translation resources.

[0727] Database: An information storage system that stores corresponding source texts.

[0728] External translation resources: A service that uses a machine translation service to translate source text into the user's language.

[0729] Emotion engine: A software and hardware component that recognizes a user's emotions and optimizes information based on those emotions.

[0730] Translation and information provision process

[0731] 1. Scanning a QR code: When a user scans a QR code with their device's camera or a QR code reader app, a specific URL is retrieved on the device.

[0732] 2. Sending a request: The device sends a request to the server based on the URL and the device's language setting. For example, the request format is "GET https: / / example.com / info123?lang=ja".

[0733] 3. Retrieving information from a database: The server accesses the database based on the identifying information in the URL included in the request and retrieves the relevant original text.

[0734] 4. Send translation request: The server sends the retrieved source text to an external machine translation service to translate it into the specified language.

[0735] 5. Getting the translation: The server receives the translated information and prepares to serve it in the user's preferred language.

[0736] 6. Emotion Recognition and Optimization: The device camera is used to analyze the user's emotions, and the information is optimized based on this emotion recognition, for example, providing additional information if the user is interested.

[0737] 7. Return and display information: The final information is returned to the terminal, which displays it on the screen.

[0738] Technologies used and data processing

[0739] Camera API: Uses the user's device's camera to scan QR codes and analyze facial expressions.

[0740] Translation API: Translates the source text into a specified language using an external machine translation service (e.g., Google Translate API).

[0741] Emotion Recognition API: Use an API to recognize user emotions (e.g., Microsoft Azure's emotion recognition service).

[0742] Specific examples

[0743] For example, when a user scans a QR code placed next to an exhibit in a museum, the following process takes place:

[0744] The user scans the QR code with their smartphone to obtain the URL.

[0745] The obtained URL and the smartphone's language setting (e.g., Japanese) are sent to the server.

[0746] The server retrieves the original English text from the database and then translates it into Japanese using a machine translation service.

[0747] The server receives the translated information and optimizes the information by recognizing the user's emotions.

[0748] The final information is sent back to the user's terminal, and the user views the displayed information.

[0749] Prompt Sentence Examples

[0750] For example, you can use a generative AI model by entering a prompt such as, "Implement an application in JavaScript that scans the QR code of an exhibited work and provides multilingual information based on the user's emotions."

[0751] According to this embodiment, the present invention can provide advanced information in multiple languages ​​and taking into consideration the user's feelings, thereby improving the user experience.

[0752] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0753] Step 1:

[0754] The user reads the information identification code (QR code).

[0755] A user scans the information identification code using a camera app or QR code reader app on their device (e.g., a smartphone). This action causes a specific URL to be read by the device. The input is the scanned QR code, and the output is the retrieved URL. Specifically, the camera reads the QR code and retrieves the data as a string.

[0756] Step 2:

[0757] The device sends the URL and the device's language setting to the server.

[0758] The device combines the obtained URL with the device's current language setting (e.g., Japanese) and sends a request to the server. The input is the scanned URL and the device's language setting, and the output is the request sent to the server. Specifically, the URL and language setting are formatted into an HTTP request and sent to the server.

[0759] Step 3:

[0760] The server retrieves the corresponding source text from the database.

[0761] The server analyzes the received request and retrieves the corresponding text from a database based on the URL identification information. The input is the request sent to the server, and the output is the text retrieved from the database. The specific operation is to execute a database query to extract the corresponding information.

[0762] Step 4:

[0763] The server calls an external translation resource to translate the source text.

[0764] The server uses the retrieved source text to send a request to an external machine translation service to translate it into the specified language. The input is the retrieved source text, and the output is the translation result returned by the external translation service. Specifically, it calls the translation API and receives the result.

[0765] Step 5:

[0766] The server receives the translation result and sends a request to the device for emotion recognition.

[0767] The server receives the translation result returned from the external translation resource and attaches it to a request sent to the user's device. In addition, the device receives an additional request to recognize the user's emotion. The input is the received translation result, and the output is a request to the device. Specific operations include preparing the received data and sending an HTTP request to the device.

[0768] Step 6:

[0769] The device recognizes the user's emotions.

[0770] The device uses a camera to analyze the user's face and recognizes the user's emotions using an emotion engine. The input is the user's face image taken with the device's camera, and the output is the recognized emotion data. Specifically, the emotion recognition API is used to extract the emotion data.

[0771] Step 7:

[0772] The server adjusts the translation results based on the user's emotions.

[0773] The server receives the emotion data sent from the device and adjusts the translation results based on it. For example, if the user shows interest, it inserts additional relevant information. The input is emotion data and the translation result, and the output is the final translation result that takes emotion into consideration. Specifically, it performs conditional judgments on the emotion data and modifies the information.

[0774] Step 8:

[0775] The server sends the final translation result back to the terminal, which displays it.

[0776] The server returns the adjusted final translation result to the terminal, which displays it on the screen. The input is the adjusted translation result, and the output is the information displayed on the terminal. The specific operation is to send the data to the terminal and process it to display it on the screen.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] [Third embodiment]

[0781] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0782] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0783] 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).

[0784] 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.

[0785] 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.

[0786] 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).

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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."

[0793] The present invention relates to a system that enables a user to obtain information in multiple languages ​​in real time at museums, tourist attractions, etc., by reading an information identification code.

[0794] System Overview

[0795] The system begins when a user reads an information identification code (QR code) using a device such as a smartphone or tablet. Based on the code read, the device sends a request to the server, which retrieves the corresponding source text from a database. The retrieved source text is translated into the user's language using an external translation resource, and the result is sent back to the user's device and displayed.

[0796] The program processing is described in detail below.

[0797] Program processing

[0798] 1. User scans the QR code

[0799] Users scan QR codes installed at museums and tourist attractions using their device's camera app or a dedicated QR code reader app. The QR code contains specific information (e.g., a URL).

[0800] 2. The device sends a request to the server

[0801] The device sends a request to the server, including the URL obtained from the scanned QR code and the user's language setting.

[0802] 3. The server retrieves the source text from the database

[0803] The server receives the request and retrieves the corresponding original text, such as English, from the database based on the identification information contained in the URL.

[0804] 4. The server calls the external translation resource

[0805] The server sends the retrieved source text to an external translation resource (e.g., a machine translation service) to translate it into the user's preferred language.

[0806] 5. The server sends the translation results back to the device

[0807] The server receives the translation result and returns it to the user's terminal.

[0808] 6. The device will display the translation results.

[0809] The user's device displays the translated explanation on the screen, allowing the user to understand information about the artwork or tourist spot in their own language.

[0810] Specific examples

[0811] Museum Situation

[0812] Users scan a QR code placed next to a painting on display in a museum.

[0813] The scanned QR code contains a specific URL (e.g. https: / / example.com / art123).

[0814] The device sends this URL and the user's language setting (e.g. French) to the server.

[0815] The server retrieves the original English text from the database based on the URL's identifier (e.g., "This is a famous painting by an artist from the 18th century.").

[0816] The server sends the original English text to a machine translation service, which translates it into French.

[0817] The translation result (e.g., "C'est une peinture célèbre d'un artiste du XVIIIe siècle.") is received and returned to the user's device.

[0818] The device displays the translation results on the screen, allowing the user to read the description of the work in French.

[0819] In this way, the system of the present invention can provide multilingual information in real time, thereby improving user satisfaction.

[0820] The processing flow will be explained below.

[0821] Step 1:

[0822] The user scans the QR code

[0823] Users scan QR codes placed at museums and tourist attractions with their smartphone's camera app or QR code reader app, which contains a specific URL.

[0824] Step 2:

[0825] The device sends a request to the server

[0826] The user's device sends a request to the server using the URL obtained from the scanned QR code, combined with the device's language setting (e.g., Spanish). For example, the request might look like this: GET https: / / example.com / art123?lang=es

[0827] Step 3:

[0828] The server receives the request

[0829] The server receives a request sent from the device, which includes a URL and the user's language settings.

[0830] Step 4:

[0831] The server retrieves the source text from the database.

[0832] The server accesses the database based on the URL identification information included in the request (e.g., "art123") and retrieves the corresponding English text. An example of the data retrieved is "This is a famous painting by an artist from the 18th century."

[0833] Step 5:

[0834] The server sends a request to an external translation resource

[0835] The server then sends the original English text to an external translation resource (e.g., a machine translation service) to translate it into the user's preferred language (e.g., Spanish). For example, the request might look like this: https: / / translate.example.com / translate?text=This%20is%20a%20famous%20painting%20by%20an%20artist%20from%20the%2018th%20century.&to=es

[0836] Step 6:

[0837] The server receives the translation results

[0838] Receives the translated result (e.g., "Esta es una pintura famosa de un artista del siglo XVIII.") from an external translation resource.

[0839] Step 7:

[0840] The server sends the translation results back to the device

[0841] The server then returns the translation result to the user's device. For example, the returned data might look like this: {"translation": "Esta es una pintura famosa de un artist del siglo XVIII."}

[0842] Step 8:

[0843] The device displays the translation results

[0844] The user's device displays the translation results on the screen, allowing the user to understand the descriptions of the artworks and tourist spots by reading the translation results.

[0845] Example 1

[0846] 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."

[0847] It is often difficult for visitors to obtain multilingual tourist information or museum exhibit information in real time due to language differences. Conventional guidance systems require the preparation of multilingual explanations, which makes management and updating cumbersome. There is also no guarantee that explanations in each language are always up to date. This can lead to problems such as visitors not receiving enough information and lowering satisfaction.

[0848] 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.

[0849] In this invention, the server includes means for a user to read an information identification code, means for a terminal to send a request to the server based on information acquired from the information identification code and the user's language setting, means for the server to acquire an original text from a database based on the request received, means for the server to call an external translation resource and translate the original text into the user's language, means for the server to return the translation result to the terminal, and means for the terminal to display the translation result, thereby enabling visitors to easily and efficiently acquire tourist information and exhibit information in their own language in real time.

[0850] "User" refers to a person who uses the system to obtain information.

[0851] "Information identification code" refers to a code that contains specific information in the form of a QR code or the like.

[0852] "Terminal" refers to a device used by a user, such as a smartphone or tablet.

[0853] "Server" refers to a computer system that receives requests, retrieves information from databases, and calls external translation resources.

[0854] "Database" refers to a data management system that stores and retrieves information.

[0855] "Original text" refers to the original language text before translation, obtained from the information identification code.

[0856] "Translation Resource" refers to an external automated translation service, such as a machine translation API.

[0857] "Translation result" refers to the text that results from translating the original text into the user's language.

[0858] "Two-dimensional code" refers to a code for recording information in two dimensions, such as a QR code.

[0859] "Automatic Translation Service" means a computer-based translation service provided over the Internet.

[0860] This invention relates to a system that allows a user to obtain multilingual information in real time at museums, tourist attractions, etc. by reading an information identification code. How the present invention can be put into practice will be described below.

[0861] First, a user uses a device such as a smartphone or tablet to read an information identification code, such as a QR code. The code contains specific information (e.g., a URL). At this stage, the smartphone or tablet uses a QR code reader app or camera app to scan the code.

[0862] Next, the device sends a request containing the retrieved URL and the user's language settings to the server over Wi-Fi or mobile data. The request is sent via a secure HTTP request (HTTPS).

[0863] The server analyzes the received request and retrieves the corresponding source text from a database based on the identification information contained in the URL. The database management system used here is MySQL, for example. The server executes a query against the database to retrieve the required information.

[0864] The server then sends the source text to an external translation resource to translate it into the user's language. This external translation resource can be an automatic translation service such as Google Translate API. The API is called using an HTTP POST request, and the transmitted content includes the source text and the target language information.

[0865] The server receives the translation results and returns them to the user's device. This process is also carried out via a secure HTTP response (HTTPS). The returned results are displayed to the user on the device.

[0866] As a concrete example, consider the case where a user visiting an art museum scans a QR code next to a painting. The user uses their smartphone to scan the QR code, and the URL obtained from the scanned QR code (e.g., https: / / example.com / art123) and the user's language setting (e.g., French) are sent to the server. The server retrieves the original English text from a database based on the obtained URL, and sends it to the Google Translate API to translate it into French. The translated result is sent back from the server to the device, and a French explanation is displayed on the smartphone screen.

[0867] Below are some example prompts to input to the generative AI model:

[0868] Example:

[0869] Please explain how this system works. When a user scans a QR code at a museum or tourist attraction, the server retrieves the information, translates it into the user's preferred language, and displays it. Please also explain the specific hardware and software used, and the data processing and calculations that are performed. For example, please provide a comprehensive explanation of the process, starting from the user scanning the QR code, to the device communicating with the server to retrieve the data and display the translation results.

[0870] In this way, the system of the present invention can provide multilingual information in real time, improving user satisfaction.

[0871] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0872] Step 1:

[0873] Users use the camera app on their smartphone or tablet, or a dedicated QR code reader app, to scan QR codes installed at museums and tourist attractions. In this case, when the user holds the QR code up to the camera, the camera app reads the data in the two-dimensional code and obtains the URL. The input is an image of the QR code captured by the camera, and the output is the URL contained in the QR code (e.g., https: / / example.com / art123).

[0874] Step 2:

[0875] The device sends a request to the server that includes the URL obtained from the scanned QR code and the user's language preference. At this stage, the device sends this information to the server as an HTTPS request. Specifically, the input is the obtained URL and the user's language preference (e.g., French), and the output is an HTTPS request sent to the server.

[0876] Step 3:

[0877] Based on the received request, the server analyzes the identification information contained in the URL and retrieves the corresponding source text from the database. The input is the HTTPS request received by the server, and the database query "SELECT FROM documentation WHERE id="URL identifier"" is executed. The output is the English source text retrieved from the database (e.g., "This is a famous painting by an artist from the 18th century.").

[0878] Step 4:

[0879] The server sends the obtained source text to an external translation resource and translates it into the language selected by the user. Specifically, the input is the source text in English and the target language information (French), which are sent to a translation service such as Google Translate API. The output is the translated result (e.g., "C'est une peinture célèbre d'un artiste du XVIIIe siècle.").

[0880] Step 5:

[0881] The server receives the translation result and returns it to the user's device. The input is the translation result, which is sent to the user's device as an HTTPS response. The output is the translation result returned to the user's device.

[0882] Step 6:

[0883] The device displays the translated text on its screen, allowing users to understand the information in their own language. Specifically, the input is the translation returned from the server, and the output is the translation displayed on the smartphone screen (e.g., "C'est une peinture célèbre d'un artiste du XVIIIe siècle.").

[0884] The above processing steps enable users to easily and efficiently obtain and understand multilingual information in real time.

[0885] (Application example 1)

[0886] 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."

[0887] Despite the demand for multilingual customer service in modern brick-and-mortar stores, there is a lack of effective systems for providing multilingual information in real time. In particular, providing product and promotional information instantly in a user's native language is important for improving customer satisfaction and realizing global customer service. However, current technology makes it difficult to achieve this smoothly and efficiently.

[0888] 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.

[0889] In this invention, the server includes means for generating a prompt sentence using a translation result generation AI model and obtaining the translation result based on the prompt sentence, means for calling an external translation resource and translating the original sentence into the user's language, and means for obtaining the original sentence from a database based on a received request. This allows users to obtain in-store product information and guidance information as multilingual information in real time, significantly improving customer satisfaction.

[0890] An "information identification code" refers to a code that contains specific information, and when read by a user, serves to provide the information to the terminal.

[0891] A "terminal" is an electronic device used by a user, which has the functions of reading information identification codes, sending requests to a server, and displaying translation results.

[0892] A "server" is a computer system that retrieves original text from a database in response to a request from a terminal, translates it using external translation resources or generative AI models, and returns the results to the terminal.

[0893] A "database" is a storage system that the server references to obtain original text, and that stores the corresponding original text data.

[0894] "External translation resource" refers to an external translation service or tool that the server invokes and uses to translate source text into the user's language.

[0895] A "generative AI model" is an AI model for natural language processing, and refers to an algorithm or system that generates prompt sentences and provides translation results based on those prompt sentences.

[0896] A "prompt sentence" is an input sentence that a generative AI model uses to perform translation, and contains the information that will be the basis for the translation.

[0897] A "QR code" is a type of two-dimensional barcode that is used as an information identification code and allows users to obtain information by scanning it with a device such as a smartphone.

[0898] "Translation result" refers to a sentence that the server converts from the original text into the user's language setting using external translation resources or generative AI models.

[0899] To implement this invention, it is necessary to build a system using the following hardware and software: This system includes a user terminal, a server, a database, external translation resources, and a generative AI model.

[0900] 1. Hardware and software configuration

[0901] Device:

[0902] This refers to devices such as smartphones and tablets, and includes a camera that allows users to read information identification codes (QR codes) and a display that displays the translation results.

[0903] Use a QR code reader app or camera function.

[0904] server:

[0905] It is a cloud server (e.g., AWS, Google Cloud) that receives requests from devices, retrieves source text from a database, and translates it using external translation resources and generative AI models.

[0906] Database:

[0907] Databases such as MySQL and PostgreSQL are used to store the original data corresponding to the information identification code.

[0908] External translation resources:

[0909] This refers to machine translation services such as Google Translate API and DeepL API, which are used by the server to translate source text into the user's specified language.

[0910] Generative AI models:

[0911] It uses generative AI models such as OpenAI GPT-4 to adjust translation results and generate answers based on specific prompts.

[0912] 2. Program processing explanation

[0913] When the server receives a request based on the information identification code from the terminal, it retrieves the corresponding original text from the database. It then calls an external translation resource to translate the original text into the user's specified language, and inputs the translation result into a generative AI model to obtain a more natural translation result. The final translation result is then returned to the terminal based on the generated prompt text.

[0914] The terminal displays the received translation results to the user, allowing the user to view the information in their own language.

[0915] 3. Examples of concrete examples and prompts

[0916] Examples:

[0917] When a user scans a wine's QR code in a store, the URL contained in the QR code is sent to the server. The server then retrieves the appropriate information from the database using the URL and obtains the original English text, such as "This is a famous wine produced by a renowned winery in France." Using an external translation resource, this original text is translated into Japanese, and the results are passed to a generative AI model to obtain a more refined translation result.

[0918] Example prompt sentence:

[0919] Prompt: Please translate the following English sentence into Japanese.

[0920] -------------------------------------------------------------------------

[0921] This is a famous wine produced by a renowned winery in France. The red wine is known for its rich flavor and smooth finish.

[0922] This invention makes it possible to provide multilingual guides and product information in real time in physical stores, thereby improving user convenience and satisfaction.

[0923] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0924] Step 1:

[0925] The user scans the QR code.

[0926] Input: The user scans the QR code on a product or guide in the store with their smartphone camera.

[0927] How it works: A QR code reader app installed on the device reads the QR code and extracts the URL within the code.

[0928] Output: The extracted URL (e.g. https: / / example.com / product123) is displayed in the terminal.

[0929] Step 2:

[0930] The terminal sends the information to the server.

[0931] Input: The extracted URL and the user's preferred language (e.g. Japanese).

[0932] How it works: The device sends this information to the server as an HTTP request.

[0933] Output: The URL and language setting information are sent to the server.

[0934] Step 3:

[0935] The server receives the request and retrieves the original text from the database.

[0936] Input: The URL received by the server and the user's preferred language.

[0937] How it works: The server uses the URL to search the database and retrieve the corresponding English source text.

[0938] Output: The retrieved source text (e.g., "This is a famous wine produced by a renowned winery in France. The red wine is known for its rich flavor and smooth finish.").

[0939] Step 4:

[0940] The server calls an external translation resource and translates the source text.

[0941] Input: The source text retrieved by the server and the user's preferred language.

[0942] How it works: The server sends the source text to an external translation resource (e.g., Google Translate API) and translates it into the configured language (e.g., Japanese).

[0943] Output: Translation result (e.g., "This is a famous wine produced by a famous French winemaker. The red wine is characterized by its rich flavor and smooth finish.").

[0944] Step 5:

[0945] The server uses a generative AI model to generate prompt sentences and adjust the translation results.

[0946] Input: Translation result (Japanese).

[0947] How it works: The server uses a generative AI model to generate translation results based on the prompt sentence and further refines the translation results.

[0948] Output: The final adjusted translation result.

[0949] Step 6:

[0950] The server returns the final translation result to the terminal.

[0951] Input: The final adjusted translation result.

[0952] Operation: The server returns the translation result to the device as an HTTP response.

[0953] Output: The final translation result is sent to the device.

[0954] Step 7:

[0955] The device will display the translation results.

[0956] Input: The final translation result.

[0957] How it works: The device displays the final translation on the screen for the user to read.

[0958] Output: The user sees the information displayed in their preferred language.

[0959] Through this series of processes, users can obtain real-time product and guide information about physical stores in their own language simply by scanning a QR code.

[0960] 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.

[0961] The present invention is a system that allows users to obtain multilingual information in real time at museums, tourist attractions, etc. by reading information identification codes such as QR codes. In addition, by combining it with an emotion engine that recognizes the user's emotions, it has the function of adjusting the information presented according to the user's emotions. The following describes in detail the embodiments of the present invention.

[0962] System Overview

[0963] This system consists of a user's device, a server, a database, external translation resources, and an emotion engine. The user scans a QR code and sends a request to the server based on the information acquired by the device. The server retrieves the corresponding original text from the database and translates it into the user's preferred language using external translation resources. The emotion engine also recognizes the user's emotions and adjusts the display of the translation results to provide the user with more appropriate information.

[0964] Program processing

[0965] 1. User scans the QR code

[0966] Users scan QR codes placed at museums and tourist attractions with their device's camera app or QR code reader app, which contains a specific URL.

[0967] 2. The device sends a request to the server

[0968] The user's device sends a request to the server using the URL obtained from the scanned QR code, combined with the device's language setting (e.g., Spanish). For example, the request might look like this: GET https: / / example.com / art123?lang=es

[0969] 3. The server receives the request

[0970] The server receives a request sent from the device, which includes a URL and the user's language settings.

[0971] 4. The server retrieves the source text from the database

[0972] The server accesses the database based on the URL identification information included in the request (e.g., "art123") and retrieves the corresponding English text. An example of the data retrieved is "This is a famous painting by an artist from the 18th century."

[0973] 5. The server sends a request to the external translation resource

[0974] The server then sends the original English text to an external translation resource (e.g., a machine translation service) to translate it into the user's preferred language (e.g., Spanish). For example, the request might look like this: https: / / translate.example.com / translate?text=This%20is%20a%20famous%20painting%20by%20an%20artist%20from%20the%2018th%20century.&to=es

[0975] 6. The server receives the translation results

[0976] Receives the translated result (e.g., "Esta es una pintura famosa de un artista del siglo XVIII.") from an external translation resource.

[0977] 7. The server sends the translation results back to the device

[0978] The server then optimizes the translation results it receives based on the user's emotions. The emotion engine analyzes the user's emotions and adjusts the displayed translation results as needed. The final translation result is then sent back to the user's device. For example, the returned data might look like this: {"translation": "Esta es una pintura famosa de un artist del siglo XVIII."}

[0979] 8. The device will display the translation result and additional information based on the emotion.

[0980] The user's device displays the translation results and additional information based on the user's emotions on the screen, allowing the user to receive the most appropriate information that corresponds to their emotions.

[0981] Specific examples

[0982] Museum Situation

[0983] Users scan a QR code placed next to a painting on display in a museum.

[0984] The scanned QR code contains a specific URL (e.g. https: / / example.com / art123).

[0985] The device sends this URL and the user's language setting (e.g. French) to the server.

[0986] The server retrieves the original English text from the database based on the URL's identifier (e.g., "This is a famous painting by an artist from the 18th century.").

[0987] The server sends the original English text to a machine translation service, which translates it into French.

[0988] After receiving the translation result (e.g., "C'est une peinture célèbre d'un artiste du XVIIIe siècle."), the emotion engine analyzes the user's facial expressions to recognize their emotions. For example, if the user shows interest, the emotion engine adds a message such as "Click here to find out more about this artist."

[0989] The server returns the final translation result to the user's terminal.

[0990] The device displays the translation results and additional information on the screen, allowing users to read the work's description and details in French.

[0991] In this way, the system of the present invention not only provides multilingual information in real time, but also provides more comprehensive information by taking into account the user's feelings.

[0992] The processing flow will be explained below.

[0993] Step 1:

[0994] The user scans the QR code

[0995] Users scan QR codes placed at museums and tourist attractions with their smartphone camera or a dedicated QR code reader app, which contains a specific URL.

[0996] Step 2:

[0997] The device sends a request to the server

[0998] The user's device sends a request to the server using the URL obtained from the scanned QR code, combined with the device's language setting (e.g., Spanish). For example, the request might look like this: GET https: / / example.com / art123?lang=es

[0999] Step 3:

[1000] The server receives the request

[1001] The server receives a request sent from the device, which includes a URL and the user's language settings.

[1002] Step 4:

[1003] The server retrieves the source text from the database.

[1004] The server accesses the database based on the URL identification information included in the request (e.g., "art123") and retrieves the corresponding English text. An example of the data retrieved is "This is a famous painting by an artist from the 18th century."

[1005] Step 5:

[1006] The server sends a request to an external translation resource

[1007] The server then sends the original English text to an external translation resource (e.g., a machine translation service) to translate it into the user's preferred language (e.g., Spanish). For example, the request might look like this: https: / / translate.example.com / translate?text=This%20is%20a%20famous%20painting%20by%20an%20artist%20from%20the%2018th%20century.&to=es

[1008] Step 6:

[1009] The server receives the translation results

[1010] Receives the translated result (e.g., "Esta es una pintura famosa de un artista del siglo XVIII.") from an external translation resource.

[1011] Step 7:

[1012] The server calls the emotion engine and recognizes the user's emotion.

[1013] The server uses the emotion engine to analyze the user's facial expressions and voice to recognize their current emotional state. For example, if the user shows interest, the emotion engine identifies them as "interested."

[1014] Step 8:

[1015] The server adjusts the translation results based on the sentiment

[1016] The server will tailor the presentation of the translation based on the results from the emotion engine, for example including additional details or links in the translation if the user indicates interest.

[1017] Step 9:

[1018] The server sends the final translation result after adjustments back to the device.

[1019] The server then sends the final translation result back to the user's device, which may look something like this: {"translation": "This is a famous piece by an artist of the same name XVIII. For more details about the artist, click here."}

[1020] Step 10:

[1021] Your device will display the final translation result

[1022] The user's device displays the final translation results on the screen, which correspond to the user's emotions and provide more information to the user.

[1023] Example 2

[1024] 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."

[1025] In modern society, travelers and visitors often require guidance in multiple languages. However, current systems often lack translation accuracy, real-time information provision, and customization based on user preferences. As a result, users are left unsatisfied with the information they receive, reducing the value of tourist destinations and museums.

[1026] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring an original text from a database, means for calling an external translation resource and translating the original text into the user's language, and means for analyzing the user's emotions using an emotion recognition engine and optimizing the translation result based on the analysis result. This allows the user to acquire real-time information in multiple languages ​​and provides information customized according to the user's emotions.

[1027] "User" refers to the person who reads the information identification code or operates the terminal.

[1028] "Information identification code" refers to a code that contains specific information, such as a QR code.

[1029] "Terminal" refers to a computing device operated by a user, such as a smartphone or tablet.

[1030] "Server" refers to a computer system that receives requests, retrieves information from databases, and communicates with external translation resources.

[1031] A "request" refers to a request for information sent from a terminal to a server.

[1032] "Database" refers to a collection of data where information can be managed and searched.

[1033] "Source text" refers to the original text information before translation.

[1034] "External translation resources" refers to resources for performing translation, such as external machine translation services.

[1035] "Translation result" refers to the text after translating the source text into another language.

[1036] "Emotion recognition engine" refers to a combination of software and hardware for analyzing a user's emotions.

[1037] "Optimization" refers to the process of adjusting translation results based on the results of user sentiment analysis.

[1038] "Additional information" refers to supplementary information provided to the user in addition to the translation result.

[1039] "Display" refers to visualizing the translation results and additional information on the device screen.

[1040] This invention is a system that allows users to obtain multilingual information in real time at museums, tourist attractions, etc. by reading information identification codes such as QR codes. In addition, by combining it with an emotion engine that recognizes the user's emotions, it has the function of adjusting the information presented according to the user's emotions.

[1041] System configuration

[1042] This system consists of a user terminal, a server, a database, external translation resources, and an emotion engine.

[1043] 1. User's device

[1044] Smartphones, tablets, etc.

[1045] Install and use a camera app or QR code reader app

[1046] An internet connection is required

[1047] 2. Server

[1048] High-performance computer system

[1049] Receives and processes requests, performs database lookups, communicates with external translation resources, and analyzes them using the emotion engine

[1050] 3. Database

[1051] A repository of data where information can be managed and searched

[1052] Contains original text such as detailed descriptions of museums and tourist attractions

[1053] 4. External Translation Resources

[1054] Machine translation services (e.g., Google Translate API)

[1055] Highly accurate translation is possible

[1056] 5. Emotion Engine

[1057] Software for analyzing emotions from the user's facial expressions and voice

[1058] Optimize information content based on emotions

[1059] Implementation method

[1060] A user scans a QR code installed at a museum or tourist attraction, and the device sends a request to the server based on the information acquired. The server then retrieves the corresponding original text from a database and translates it into the user's preferred language using external translation resources. The emotion engine also recognizes the user's emotions and adjusts the displayed translation results to provide the user with more appropriate information. This series of operations allows users to obtain multilingual information tailored to their needs in real time.

[1061] Specific examples

[1062] In the case of an art museum

[1063] A user scans a QR code placed next to a painting on display in a museum. The scanned QR code contains a specific URL, and the device sends this URL and the user's language setting (e.g., French) to the server. The server retrieves the original English text from the database based on the URL's identification information and sends it to an external machine translation service for translation into French. The translation result (e.g., "C'est une peinture célèbre d'un artiste du XVIIIe siècle.") is received, and the emotion engine analyzes the user's facial expression to recognize their emotion. For example, if the user shows interest, the emotion engine adds an additional message such as "Click here to learn more about this painter." The server then sends the final translation result back to the user's device, which displays the translation result and additional information on the screen. This allows the user to read the description and detailed information of the work in French.

[1064] Prompt Sentence Examples

[1065] "How can users scan the QR code on their device and view exhibit information in French?"

[1066] In this way, the system of the present invention not only provides multilingual information in real time, but also provides more comprehensive information by taking into account the user's feelings.

[1067] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1068] Step 1:

[1069] A user finds a QR code installed at a museum or tourist attraction and launches their device's camera app or QR code reader app. When the user scans the QR code, the URL is loaded into the device. The input is the visual information of the QR code, and the device analyzes the QR code to obtain the URL. The output of this process is a URL (e.g., https: / / example.com / art123).

[1070] Step 2:

[1071] The device combines the URL retrieved from the QR code with the user's language preference (e.g., Spanish) and sends a request to the server. The input is the URL retrieved from the QR code and the device's language preference, which together form the request. The output of this process is the request that is sent (e.g., GET https: / / example.com / art123?lang=es).

[1072] Step 3:

[1073] The server receives a request sent from the terminal. The input is the request from the terminal. The server analyzes the URL and language settings in the request and prepares for database access. The output is database query information (e.g., "art123") based on the analysis results.

[1074] Step 4:

[1075] The server accesses the database based on the request and retrieves the corresponding English source text. The input is the database query information, and the output is the English source text retrieved from the database (e.g., "This is a famous painting by an artist from the 18th century."). The server temporarily stores this information.

[1076] Step 5:

[1077] The server takes the English source text and sends it to an external translation resource (e.g., a machine translation service) to translate it into the user's preferred language. The input is the English source text and the user's preferred language (e.g., Spanish), which are composed into the outgoing request. The output is the outgoing request (e.g., https: / / translate.example.com / translate?text=This%20is%20a%20famous%20painting%20by%20an%20artist%20from%20the%2018th%20century.&to=es).

[1078] Step 6:

[1079] The server receives the translation result from an external translation resource. The input is the response to the translation request, and the output is the translated result (e.g., "Esta es una pintura famosa de un artista del siglo XVIII."). The server temporarily stores the translation result.

[1080] Step 7:

[1081] The server analyzes the user's emotions using an emotion engine. The input is emotional data such as the user's facial expressions and voice, which the emotion recognition engine analyzes. The output of this process is the analysis result (e.g., "interested"). The server then optimizes the translation result.

[1082] Step 8:

[1083] The server returns the optimized translation to the device. The input is the optimized translation. The output is the data sent to the device (e.g., {"translation": "This is a famous piece of art by artist Siglo XVIII.", "additional_info": "To learn more about the artist, click here."}).

[1084] Step 9:

[1085] The device receives the translation results and displays additional information based on the emotion on the screen. The input is the response data from the server, which is displayed on the device's display. The output of this process is the information displayed to the user (e.g., "Esta es una pintura famosa de un artista del siglo XVIII." and "Click here for more information about this artist"). This allows the user to view optimized information in real time.

[1086] (Application example 2)

[1087] 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."

[1088] Multilingual tourist guide systems and information systems have become commonplace in recent years, but few systems provide information that takes the user's emotions into consideration. For example, when scanning a QR code at a museum or tourist attraction to obtain information, only general translations are provided, and information that does not reflect the user's emotions or interests is often not provided. As a result, information may not be properly conveyed to tired or disinterested users. Furthermore, a similar problem occurs in physical stores. When a user shows interest in a product or has a specific emotion, the information provided may not be appropriate.

[1089] 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.

[1090] In this invention, the server includes means for calling an external translation resource and translating the original text into the user's language, means for the server to return the translation result to the terminal, means for the terminal to recognize the user's emotion, and means for the server to adjust the translation result based on the user's emotion. This makes it possible for the user to read the information identification code not only to be provided with information translated in the user's language, but also to be provided with information optimal for the user's emotion.

[1091] An "information identification code" is an identifier that a user can read to obtain specific information, and generally includes QR codes and barcodes.

[1092] A "terminal" is a device used by a user to read an information identification code, and generally includes a smartphone, tablet, or PC.

[1093] A "server" is a computer system that receives requests from terminals and provides information in cooperation with databases and external translation resources.

[1094] A "database" is an information storage system that stores original text corresponding to information identification codes.

[1095] An "external translation resource" is a translation service that the server invokes and uses to translate source text into the user's language, including machine translation services.

[1096] An "emotion engine" is a software or hardware component that uses the device's camera and sensors to analyze the user's emotions.

[1097] "Translation result" is information obtained by converting an original text into a user's language using an external translation resource.

[1098] "Information adjustment" is the process of appropriately modifying and enhancing the content of translation results and additional information based on the user's feelings.

[1099] The system for implementing the present invention consists of a series of processes in which a user reads an information identification code, the terminal sends it to a server, the server performs translation and emotion recognition, and then returns optimized information to the terminal.

[1100] composition

[1101] The system includes the following elements and procedures:

[1102] User terminal: A device used to read information identification codes, such as a smartphone, tablet, or PC.

[1103] Information Identification Code: A QR code or other code used to retrieve specific information.

[1104] Server: Processes incoming requests and performs translation by accessing databases and external translation resources.

[1105] Database: An information storage system that stores corresponding source texts.

[1106] External translation resources: A service that uses a machine translation service to translate source text into the user's language.

[1107] Emotion engine: A software and hardware component that recognizes a user's emotions and optimizes information based on those emotions.

[1108] Translation and information provision process

[1109] 1. Scanning a QR code: When a user scans a QR code with their device's camera or a QR code reader app, a specific URL is retrieved on the device.

[1110] 2. Sending a request: The device sends a request to the server based on the URL and the device's language setting. For example, the request format is "GET https: / / example.com / info123?lang=ja".

[1111] 3. Retrieving information from a database: The server accesses the database based on the identifying information in the URL included in the request and retrieves the relevant original text.

[1112] 4. Send translation request: The server sends the retrieved source text to an external machine translation service to translate it into the specified language.

[1113] 5. Getting the translation: The server receives the translated information and prepares to serve it in the user's preferred language.

[1114] 6. Emotion Recognition and Optimization: The device camera is used to analyze the user's emotions, and the information is optimized based on this emotion recognition, for example, providing additional information if the user is interested.

[1115] 7. Return and display information: The final information is returned to the terminal, which displays it on the screen.

[1116] Technologies used and data processing

[1117] Camera API: Uses the user's device's camera to scan QR codes and analyze facial expressions.

[1118] Translation API: Translates the source text into a specified language using an external machine translation service (e.g., Google Translate API).

[1119] Emotion Recognition API: Use an API to recognize user emotions (e.g., Microsoft Azure's emotion recognition service).

[1120] Specific examples

[1121] For example, when a user scans a QR code placed next to an exhibit in a museum, the following process takes place:

[1122] The user scans the QR code with their smartphone to obtain the URL.

[1123] The obtained URL and the smartphone's language setting (e.g., Japanese) are sent to the server.

[1124] The server retrieves the original English text from the database and then translates it into Japanese using a machine translation service.

[1125] The server receives the translated information and optimizes the information by recognizing the user's emotions.

[1126] The final information is sent back to the user's terminal, and the user views the displayed information.

[1127] Prompt Sentence Examples

[1128] For example, you can use a generative AI model by entering a prompt such as, "Implement an application in JavaScript that scans the QR code of an exhibited work and provides multilingual information based on the user's emotions."

[1129] According to this embodiment, the present invention can provide advanced information in multiple languages ​​and taking into consideration the user's feelings, thereby improving the user experience.

[1130] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1131] Step 1:

[1132] The user reads the information identification code (QR code).

[1133] A user scans the information identification code using a camera app or QR code reader app on their device (e.g., a smartphone). This action causes a specific URL to be read by the device. The input is the scanned QR code, and the output is the retrieved URL. Specifically, the camera reads the QR code and retrieves the data as a string.

[1134] Step 2:

[1135] The device sends the URL and the device's language setting to the server.

[1136] The device combines the obtained URL with the device's current language setting (e.g., Japanese) and sends a request to the server. The input is the scanned URL and the device's language setting, and the output is the request sent to the server. Specifically, the URL and language setting are formatted into an HTTP request and sent to the server.

[1137] Step 3:

[1138] The server retrieves the corresponding source text from the database.

[1139] The server analyzes the received request and retrieves the corresponding text from a database based on the URL identification information. The input is the request sent to the server, and the output is the text retrieved from the database. The specific operation is to execute a database query to extract the corresponding information.

[1140] Step 4:

[1141] The server calls an external translation resource to translate the source text.

[1142] The server uses the retrieved source text to send a request to an external machine translation service to translate it into the specified language. The input is the retrieved source text, and the output is the translation result returned by the external translation service. Specifically, it calls the translation API and receives the result.

[1143] Step 5:

[1144] The server receives the translation result and sends a request to the device for emotion recognition.

[1145] The server receives the translation result returned from the external translation resource and attaches it to a request sent to the user's device. In addition, the device receives an additional request to recognize the user's emotion. The input is the received translation result, and the output is a request to the device. Specific operations include preparing the received data and sending an HTTP request to the device.

[1146] Step 6:

[1147] The device recognizes the user's emotions.

[1148] The device uses a camera to analyze the user's face and recognizes the user's emotions using an emotion engine. The input is the user's face image taken with the device's camera, and the output is the recognized emotion data. Specifically, the emotion recognition API is used to extract the emotion data.

[1149] Step 7:

[1150] The server adjusts the translation results based on the user's emotions.

[1151] The server receives the emotion data sent from the device and adjusts the translation results based on it. For example, if the user shows interest, it inserts additional relevant information. The input is emotion data and the translation result, and the output is the final translation result that takes emotion into consideration. Specifically, it performs conditional judgments on the emotion data and modifies the information.

[1152] Step 8:

[1153] The server sends the final translation result back to the terminal, which displays it.

[1154] The server returns the adjusted final translation result to the terminal, which displays it on the screen. The input is the adjusted translation result, and the output is the information displayed on the terminal. The specific operation is to send the data to the terminal and process it to display it on the screen.

[1155] 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.

[1156] 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.

[1157] 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.

[1158] [Fourth embodiment]

[1159] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1160] 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.

[1161] 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).

[1162] 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.

[1163] 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.

[1164] 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).

[1165] 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.

[1166] 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.

[1167] 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.

[1168] 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.

[1169] 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.

[1170] 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.

[1171] 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."

[1172] The present invention relates to a system that enables a user to obtain information in multiple languages ​​in real time at museums, tourist attractions, etc., by reading an information identification code.

[1173] System Overview

[1174] The system begins when a user reads an information identification code (QR code) using a device such as a smartphone or tablet. Based on the code read, the device sends a request to the server, which retrieves the corresponding source text from a database. The retrieved source text is translated into the user's language using an external translation resource, and the result is sent back to the user's device and displayed.

[1175] The program processing is described in detail below.

[1176] Program processing

[1177] 1. User scans the QR code

[1178] Users scan QR codes installed at museums and tourist attractions using their device's camera app or a dedicated QR code reader app. The QR code contains specific information (e.g., a URL).

[1179] 2. The device sends a request to the server

[1180] The device sends a request to the server, including the URL obtained from the scanned QR code and the user's language setting.

[1181] 3. The server retrieves the source text from the database

[1182] The server receives the request and retrieves the corresponding original text, such as English, from the database based on the identification information contained in the URL.

[1183] 4. The server calls the external translation resource

[1184] The server sends the retrieved source text to an external translation resource (e.g., a machine translation service) to translate it into the user's preferred language.

[1185] 5. The server sends the translation results back to the device

[1186] The server receives the translation result and returns it to the user's terminal.

[1187] 6. The device will display the translation results.

[1188] The user's device displays the translated explanation on the screen, allowing the user to understand information about the artwork or tourist spot in their own language.

[1189] Specific examples

[1190] Museum Situation

[1191] Users scan a QR code placed next to a painting on display in a museum.

[1192] The scanned QR code contains a specific URL (e.g. https: / / example.com / art123).

[1193] The device sends this URL and the user's language setting (e.g. French) to the server.

[1194] The server retrieves the original English text from the database based on the URL's identifier (e.g., "This is a famous painting by an artist from the 18th century.").

[1195] The server sends the original English text to a machine translation service, which translates it into French.

[1196] The translation result (e.g., "C'est une peinture célèbre d'un artiste du XVIIIe siècle.") is received and returned to the user's device.

[1197] The device displays the translation results on the screen, allowing the user to read the description of the work in French.

[1198] In this way, the system of the present invention can provide multilingual information in real time, thereby improving user satisfaction.

[1199] The processing flow will be explained below.

[1200] Step 1:

[1201] The user scans the QR code

[1202] Users scan QR codes placed at museums and tourist attractions with their smartphone's camera app or QR code reader app, which contains a specific URL.

[1203] Step 2:

[1204] The device sends a request to the server

[1205] The user's device sends a request to the server using the URL obtained from the scanned QR code, combined with the device's language setting (e.g., Spanish). For example, the request might look like this: GET https: / / example.com / art123?lang=es

[1206] Step 3:

[1207] The server receives the request

[1208] The server receives a request sent from the device, which includes a URL and the user's language settings.

[1209] Step 4:

[1210] The server retrieves the source text from the database.

[1211] The server accesses the database based on the URL identification information included in the request (e.g., "art123") and retrieves the corresponding English text. An example of the data retrieved is "This is a famous painting by an artist from the 18th century."

[1212] Step 5:

[1213] The server sends a request to an external translation resource

[1214] The server then sends the original English text to an external translation resource (e.g., a machine translation service) to translate it into the user's preferred language (e.g., Spanish). For example, the request might look like this: https: / / translate.example.com / translate?text=This%20is%20a%20famous%20painting%20by%20an%20artist%20from%20the%2018th%20century.&to=es

[1215] Step 6:

[1216] The server receives the translation results

[1217] Receives the translated result (e.g., "Esta es una pintura famosa de un artista del siglo XVIII.") from an external translation resource.

[1218] Step 7:

[1219] The server sends the translation results back to the device

[1220] The server then returns the translation result to the user's device. For example, the returned data might look like this: {"translation": "Esta es una pintura famosa de un artist del siglo XVIII."}

[1221] Step 8:

[1222] The device displays the translation results

[1223] The user's device displays the translation results on the screen, allowing the user to understand the descriptions of the artworks and tourist spots by reading the translation results.

[1224] Example 1

[1225] 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."

[1226] It is often difficult for visitors to obtain multilingual tourist information or museum exhibit information in real time due to language differences. Conventional guidance systems require the preparation of multilingual explanations, which makes management and updating cumbersome. There is also no guarantee that explanations in each language are always up to date. This can lead to problems such as visitors not receiving enough information and lowering satisfaction.

[1227] 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.

[1228] In this invention, the server includes means for a user to read an information identification code, means for a terminal to send a request to the server based on information acquired from the information identification code and the user's language setting, means for the server to acquire an original text from a database based on the request received, means for the server to call an external translation resource and translate the original text into the user's language, means for the server to return the translation result to the terminal, and means for the terminal to display the translation result, thereby enabling visitors to easily and efficiently acquire tourist information and exhibit information in their own language in real time.

[1229] "User" refers to a person who uses the system to obtain information.

[1230] "Information identification code" refers to a code that contains specific information in the form of a QR code or the like.

[1231] "Terminal" refers to a device used by a user, such as a smartphone or tablet.

[1232] "Server" refers to a computer system that receives requests, retrieves information from databases, and calls external translation resources.

[1233] "Database" refers to a data management system that stores and retrieves information.

[1234] "Original text" refers to the original language text before translation, obtained from the information identification code.

[1235] "Translation Resource" refers to an external automated translation service, such as a machine translation API.

[1236] "Translation result" refers to the text that results from translating the original text into the user's language.

[1237] "Two-dimensional code" refers to a code for recording information in two dimensions, such as a QR code.

[1238] "Automatic Translation Service" means a computer-based translation service provided over the Internet.

[1239] This invention relates to a system that allows a user to obtain multilingual information in real time at museums, tourist attractions, etc. by reading an information identification code. How the present invention can be put into practice will be described below.

[1240] First, a user uses a device such as a smartphone or tablet to read an information identification code, such as a QR code. The code contains specific information (e.g., a URL). At this stage, the smartphone or tablet uses a QR code reader app or camera app to scan the code.

[1241] Next, the device sends a request containing the retrieved URL and the user's language settings to the server over Wi-Fi or mobile data. The request is sent via a secure HTTP request (HTTPS).

[1242] The server analyzes the received request and retrieves the corresponding source text from a database based on the identification information contained in the URL. The database management system used here is MySQL, for example. The server executes a query against the database to retrieve the required information.

[1243] The server then sends the source text to an external translation resource to translate it into the user's language. This external translation resource can be an automatic translation service such as Google Translate API. The API is called using an HTTP POST request, and the transmitted content includes the source text and the target language information.

[1244] The server receives the translation results and returns them to the user's device. This process is also carried out via a secure HTTP response (HTTPS). The returned results are displayed to the user on the device.

[1245] As a concrete example, consider the case where a user visiting an art museum scans a QR code next to a painting. The user uses their smartphone to scan the QR code, and the URL obtained from the scanned QR code (e.g., https: / / example.com / art123) and the user's language setting (e.g., French) are sent to the server. The server retrieves the original English text from a database based on the obtained URL, and sends it to the Google Translate API to translate it into French. The translated result is sent back from the server to the device, and a French explanation is displayed on the smartphone screen.

[1246] Below are some example prompts to input to the generative AI model:

[1247] Example:

[1248] Please explain how this system works. When a user scans a QR code at a museum or tourist attraction, the server retrieves the information, translates it into the user's preferred language, and displays it. Please also explain the specific hardware and software used, and the data processing and calculations that are performed. For example, please provide a comprehensive explanation of the process, starting from the user scanning the QR code, to the device communicating with the server to retrieve the data and display the translation results.

[1249] In this way, the system of the present invention can provide multilingual information in real time, improving user satisfaction.

[1250] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1251] Step 1:

[1252] Users use the camera app on their smartphone or tablet, or a dedicated QR code reader app, to scan QR codes installed at museums and tourist attractions. In this case, when the user holds the QR code up to the camera, the camera app reads the data in the two-dimensional code and obtains the URL. The input is an image of the QR code captured by the camera, and the output is the URL contained in the QR code (e.g., https: / / example.com / art123).

[1253] Step 2:

[1254] The device sends a request to the server that includes the URL obtained from the scanned QR code and the user's language preference. At this stage, the device sends this information to the server as an HTTPS request. Specifically, the input is the obtained URL and the user's language preference (e.g., French), and the output is an HTTPS request sent to the server.

[1255] Step 3:

[1256] Based on the received request, the server analyzes the identification information contained in the URL and retrieves the corresponding source text from the database. The input is the HTTPS request received by the server, and the database query "SELECT FROM documentation WHERE id="URL identifier"" is executed. The output is the English source text retrieved from the database (e.g., "This is a famous painting by an artist from the 18th century.").

[1257] Step 4:

[1258] The server sends the obtained source text to an external translation resource and translates it into the language selected by the user. Specifically, the input is the source text in English and the target language information (French), which are sent to a translation service such as Google Translate API. The output is the translated result (e.g., "C'est une peinture célèbre d'un artiste du XVIIIe siècle.").

[1259] Step 5:

[1260] The server receives the translation result and returns it to the user's device. The input is the translation result, which is sent to the user's device as an HTTPS response. The output is the translation result returned to the user's device.

[1261] Step 6:

[1262] The device displays the translated text on its screen, allowing users to understand the information in their own language. Specifically, the input is the translation returned from the server, and the output is the translation displayed on the smartphone screen (e.g., "C'est une peinture célèbre d'un artiste du XVIIIe siècle.").

[1263] The above processing steps enable users to easily and efficiently obtain and understand multilingual information in real time.

[1264] (Application example 1)

[1265] 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."

[1266] Despite the demand for multilingual customer service in modern brick-and-mortar stores, there is a lack of effective systems for providing multilingual information in real time. In particular, providing product and promotional information instantly in a user's native language is important for improving customer satisfaction and realizing global customer service. However, current technology makes it difficult to achieve this smoothly and efficiently.

[1267] 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.

[1268] In this invention, the server includes means for generating a prompt sentence using a translation result generation AI model and obtaining the translation result based on the prompt sentence, means for calling an external translation resource and translating the original sentence into the user's language, and means for obtaining the original sentence from a database based on a received request. This allows users to obtain in-store product information and guidance information as multilingual information in real time, significantly improving customer satisfaction.

[1269] An "information identification code" refers to a code that contains specific information, and when read by a user, serves to provide the information to the terminal.

[1270] A "terminal" is an electronic device used by a user, which has the functions of reading information identification codes, sending requests to a server, and displaying translation results.

[1271] A "server" is a computer system that retrieves original text from a database in response to a request from a terminal, translates it using external translation resources or generative AI models, and returns the results to the terminal.

[1272] A "database" is a storage system that the server references to obtain original text, and that stores the corresponding original text data.

[1273] "External translation resource" refers to an external translation service or tool that the server invokes and uses to translate source text into the user's language.

[1274] A "generative AI model" is an AI model for natural language processing, and refers to an algorithm or system that generates prompt sentences and provides translation results based on those prompt sentences.

[1275] A "prompt sentence" is an input sentence that a generative AI model uses to perform translation, and contains the information that will be the basis for the translation.

[1276] A "QR code" is a type of two-dimensional barcode that is used as an information identification code and allows users to obtain information by scanning it with a device such as a smartphone.

[1277] "Translation result" refers to a sentence that the server converts from the original text into the user's language setting using external translation resources or generative AI models.

[1278] To implement this invention, it is necessary to build a system using the following hardware and software: This system includes a user terminal, a server, a database, external translation resources, and a generative AI model.

[1279] 1. Hardware and software configuration

[1280] Device:

[1281] This refers to devices such as smartphones and tablets, and includes a camera that allows users to read information identification codes (QR codes) and a display that displays the translation results.

[1282] Use a QR code reader app or camera function.

[1283] server:

[1284] It is a cloud server (e.g., AWS, Google Cloud) that receives requests from devices, retrieves source text from a database, and translates it using external translation resources and generative AI models.

[1285] Database:

[1286] Databases such as MySQL and PostgreSQL are used to store the original data corresponding to the information identification code.

[1287] External translation resources:

[1288] This refers to machine translation services such as Google Translate API and DeepL API, which are used by the server to translate source text into the user's specified language.

[1289] Generative AI models:

[1290] It uses generative AI models such as OpenAI GPT-4 to adjust translation results and generate answers based on specific prompts.

[1291] 2. Program processing explanation

[1292] When the server receives a request based on the information identification code from the terminal, it retrieves the corresponding original text from the database. It then calls an external translation resource to translate the original text into the user's specified language, and inputs the translation result into a generative AI model to obtain a more natural translation result. The final translation result is then returned to the terminal based on the generated prompt text.

[1293] The terminal displays the received translation results to the user, allowing the user to view the information in their own language.

[1294] 3. Examples of concrete examples and prompts

[1295] Examples:

[1296] When a user scans a wine's QR code in a store, the URL contained in the QR code is sent to the server. The server then retrieves the appropriate information from the database using the URL and obtains the original English text, such as "This is a famous wine produced by a renowned winery in France." Using an external translation resource, this original text is translated into Japanese, and the results are passed to a generative AI model to obtain a more refined translation result.

[1297] Example prompt sentence:

[1298] Prompt: Please translate the following English sentence into Japanese.

[1299] -------------------------------------------------------------------------

[1300] This is a famous wine produced by a renowned winery in France. The red wine is known for its rich flavor and smooth finish.

[1301] This invention makes it possible to provide multilingual guides and product information in real time in physical stores, thereby improving user convenience and satisfaction.

[1302] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1303] Step 1:

[1304] The user scans the QR code.

[1305] Input: The user scans the QR code on a product or guide in the store with their smartphone camera.

[1306] How it works: A QR code reader app installed on the device reads the QR code and extracts the URL within the code.

[1307] Output: The extracted URL (e.g. https: / / example.com / product123) is displayed in the terminal.

[1308] Step 2:

[1309] The terminal sends the information to the server.

[1310] Input: The extracted URL and the user's preferred language (e.g. Japanese).

[1311] How it works: The device sends this information to the server as an HTTP request.

[1312] Output: The URL and language setting information are sent to the server.

[1313] Step 3:

[1314] The server receives the request and retrieves the original text from the database.

[1315] Input: The URL received by the server and the user's preferred language.

[1316] How it works: The server uses the URL to search the database and retrieve the corresponding English source text.

[1317] Output: The retrieved source text (e.g., "This is a famous wine produced by a renowned winery in France. The red wine is known for its rich flavor and smooth finish.").

[1318] Step 4:

[1319] The server calls an external translation resource and translates the source text.

[1320] Input: The source text retrieved by the server and the user's preferred language.

[1321] How it works: The server sends the source text to an external translation resource (e.g., Google Translate API) and translates it into the configured language (e.g., Japanese).

[1322] Output: Translation result (e.g., "This is a famous wine produced by a famous French winemaker. The red wine is characterized by its rich flavor and smooth finish.").

[1323] Step 5:

[1324] The server uses a generative AI model to generate prompt sentences and adjust the translation results.

[1325] Input: Translation result (Japanese).

[1326] How it works: The server uses a generative AI model to generate translation results based on the prompt sentence and further refines the translation results.

[1327] Output: The final adjusted translation result.

[1328] Step 6:

[1329] The server returns the final translation result to the terminal.

[1330] Input: The final adjusted translation result.

[1331] Operation: The server returns the translation result to the device as an HTTP response.

[1332] Output: The final translation result is sent to the device.

[1333] Step 7:

[1334] The device will display the translation results.

[1335] Input: The final translation result.

[1336] How it works: The device displays the final translation on the screen for the user to read.

[1337] Output: The user sees the information displayed in their preferred language.

[1338] Through this series of processes, users can obtain real-time product and guide information about physical stores in their own language simply by scanning a QR code.

[1339] 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.

[1340] The present invention is a system that allows users to obtain multilingual information in real time at museums, tourist attractions, etc. by reading information identification codes such as QR codes. In addition, by combining it with an emotion engine that recognizes the user's emotions, it has the function of adjusting the information presented according to the user's emotions. The following describes in detail the embodiments of the present invention.

[1341] System Overview

[1342] This system consists of a user's device, a server, a database, external translation resources, and an emotion engine. The user scans a QR code and sends a request to the server based on the information acquired by the device. The server retrieves the corresponding original text from the database and translates it into the user's preferred language using external translation resources. The emotion engine also recognizes the user's emotions and adjusts the display of the translation results to provide the user with more appropriate information.

[1343] Program processing

[1344] 1. User scans the QR code

[1345] Users scan QR codes placed at museums and tourist attractions with their device's camera app or QR code reader app, which contains a specific URL.

[1346] 2. The device sends a request to the server

[1347] The user's device sends a request to the server using the URL obtained from the scanned QR code, combined with the device's language setting (e.g., Spanish). For example, the request might look like this: GET https: / / example.com / art123?lang=es

[1348] 3. The server receives the request

[1349] The server receives a request sent from the device, which includes a URL and the user's language settings.

[1350] 4. The server retrieves the source text from the database

[1351] The server accesses the database based on the URL identification information included in the request (e.g., "art123") and retrieves the corresponding English text. An example of the data retrieved is "This is a famous painting by an artist from the 18th century."

[1352] 5. The server sends a request to the external translation resource

[1353] The server then sends the original English text to an external translation resource (e.g., a machine translation service) to translate it into the user's preferred language (e.g., Spanish). For example, the request might look like this: https: / / translate.example.com / translate?text=This%20is%20a%20famous%20painting%20by%20an%20artist%20from%20the%2018th%20century.&to=es

[1354] 6. The server receives the translation results

[1355] Receives the translated result (e.g., "Esta es una pintura famosa de un artista del siglo XVIII.") from an external translation resource.

[1356] 7. The server sends the translation results back to the device

[1357] The server then optimizes the translation results it receives based on the user's emotions. The emotion engine analyzes the user's emotions and adjusts the displayed translation results as needed. The final translation result is then sent back to the user's device. For example, the returned data might look like this: {"translation": "Esta es una pintura famosa de un artist del siglo XVIII."}

[1358] 8. The device will display the translation result and additional information based on the emotion.

[1359] The user's device displays the translation results and additional information based on the user's emotions on the screen, allowing the user to receive the most appropriate information that corresponds to their emotions.

[1360] Specific examples

[1361] Museum Situation

[1362] Users scan a QR code placed next to a painting on display in a museum.

[1363] The scanned QR code contains a specific URL (e.g. https: / / example.com / art123).

[1364] The device sends this URL and the user's language setting (e.g. French) to the server.

[1365] The server retrieves the original English text from the database based on the URL's identifier (e.g., "This is a famous painting by an artist from the 18th century.").

[1366] The server sends the original English text to a machine translation service, which translates it into French.

[1367] After receiving the translation result (e.g., "C'est une peinture célèbre d'un artiste du XVIIIe siècle."), the emotion engine analyzes the user's facial expressions to recognize their emotions. For example, if the user shows interest, the emotion engine adds a message such as "Click here to find out more about this artist."

[1368] The server returns the final translation result to the user's terminal.

[1369] The device displays the translation results and additional information on the screen, allowing users to read the work's description and details in French.

[1370] In this way, the system of the present invention not only provides multilingual information in real time, but also provides more comprehensive information by taking into account the user's feelings.

[1371] The processing flow will be explained below.

[1372] Step 1:

[1373] The user scans the QR code

[1374] Users scan QR codes placed at museums and tourist attractions with their smartphone camera or a dedicated QR code reader app, which contains a specific URL.

[1375] Step 2:

[1376] The device sends a request to the server

[1377] The user's device sends a request to the server using the URL obtained from the scanned QR code, combined with the device's language setting (e.g., Spanish). For example, the request might look like this: GET https: / / example.com / art123?lang=es

[1378] Step 3:

[1379] The server receives the request

[1380] The server receives a request sent from the device, which includes a URL and the user's language settings.

[1381] Step 4:

[1382] The server retrieves the source text from the database.

[1383] The server accesses the database based on the URL identification information included in the request (e.g., "art123") and retrieves the corresponding English text. An example of the data retrieved is "This is a famous painting by an artist from the 18th century."

[1384] Step 5:

[1385] The server sends a request to an external translation resource

[1386] The server then sends the original English text to an external translation resource (e.g., a machine translation service) to translate it into the user's preferred language (e.g., Spanish). For example, the request might look like this: https: / / translate.example.com / translate?text=This%20is%20a%20famous%20painting%20by%20an%20artist%20from%20the%2018th%20century.&to=es

[1387] Step 6:

[1388] The server receives the translation results

[1389] Receives the translated result (e.g., "Esta es una pintura famosa de un artista del siglo XVIII.") from an external translation resource.

[1390] Step 7:

[1391] The server calls the emotion engine and recognizes the user's emotion.

[1392] The server uses the emotion engine to analyze the user's facial expressions and voice to recognize their current emotional state. For example, if the user shows interest, the emotion engine identifies them as "interested."

[1393] Step 8:

[1394] The server adjusts the translation results based on the sentiment

[1395] The server will tailor the presentation of the translation based on the results from the emotion engine, for example including additional details or links in the translation if the user indicates interest.

[1396] Step 9:

[1397] The server sends the final translation result after adjustments back to the device.

[1398] The server then sends the final translation result back to the user's device, which may look something like this: {"translation": "This is a famous piece by an artist of the same name XVIII. For more details about the artist, click here."}

[1399] Step 10:

[1400] Your device will display the final translation result

[1401] The user's device displays the final translation results on the screen, which correspond to the user's emotions and provide more information to the user.

[1402] Example 2

[1403] 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."

[1404] In modern society, travelers and visitors often require guidance in multiple languages. However, current systems often lack translation accuracy, real-time information provision, and customization based on user preferences. As a result, users are left unsatisfied with the information they receive, reducing the value of tourist destinations and museums.

[1405] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring an original text from a database, means for calling an external translation resource and translating the original text into the user's language, and means for analyzing the user's emotions using an emotion recognition engine and optimizing the translation result based on the analysis result. This allows the user to acquire real-time information in multiple languages ​​and provides information customized according to the user's emotions.

[1406] "User" refers to the person who reads the information identification code or operates the terminal.

[1407] "Information identification code" refers to a code that contains specific information, such as a QR code.

[1408] "Terminal" refers to a computing device operated by a user, such as a smartphone or tablet.

[1409] "Server" refers to a computer system that receives requests, retrieves information from databases, and communicates with external translation resources.

[1410] A "request" refers to a request for information sent from a terminal to a server.

[1411] "Database" refers to a collection of data where information can be managed and searched.

[1412] "Source text" refers to the original text information before translation.

[1413] "External translation resources" refers to resources for performing translation, such as external machine translation services.

[1414] "Translation result" refers to the text after translating the source text into another language.

[1415] "Emotion recognition engine" refers to a combination of software and hardware for analyzing a user's emotions.

[1416] "Optimization" refers to the process of adjusting translation results based on the results of user sentiment analysis.

[1417] "Additional information" refers to supplementary information provided to the user in addition to the translation result.

[1418] "Display" refers to visualizing the translation results and additional information on the device screen.

[1419] This invention is a system that allows users to obtain multilingual information in real time at museums, tourist attractions, etc. by reading information identification codes such as QR codes. In addition, by combining it with an emotion engine that recognizes the user's emotions, it has the function of adjusting the information presented according to the user's emotions.

[1420] System configuration

[1421] This system consists of a user terminal, a server, a database, external translation resources, and an emotion engine.

[1422] 1. User's device

[1423] Smartphones, tablets, etc.

[1424] Install and use a camera app or QR code reader app

[1425] An internet connection is required

[1426] 2. Server

[1427] High-performance computer system

[1428] Receives and processes requests, performs database lookups, communicates with external translation resources, and analyzes them using the emotion engine

[1429] 3. Database

[1430] A repository of data where information can be managed and searched

[1431] Contains original text such as detailed descriptions of museums and tourist attractions

[1432] 4. External Translation Resources

[1433] Machine translation services (e.g., Google Translate API)

[1434] Highly accurate translation is possible

[1435] 5. Emotion Engine

[1436] Software for analyzing emotions from the user's facial expressions and voice

[1437] Optimize information content based on emotions

[1438] Implementation method

[1439] A user scans a QR code installed at a museum or tourist attraction, and the device sends a request to the server based on the information acquired. The server then retrieves the corresponding original text from a database and translates it into the user's preferred language using external translation resources. The emotion engine also recognizes the user's emotions and adjusts the displayed translation results to provide the user with more appropriate information. This series of operations allows users to obtain multilingual information tailored to their needs in real time.

[1440] Specific examples

[1441] In the case of an art museum

[1442] A user scans a QR code placed next to a painting on display in a museum. The scanned QR code contains a specific URL, and the device sends this URL and the user's language setting (e.g., French) to the server. The server retrieves the original English text from the database based on the URL's identification information and sends it to an external machine translation service for translation into French. The translation result (e.g., "C'est une peinture célèbre d'un artiste du XVIIIe siècle.") is received, and the emotion engine analyzes the user's facial expression to recognize their emotion. For example, if the user shows interest, the emotion engine adds an additional message such as "Click here to learn more about this painter." The server then sends the final translation result back to the user's device, which displays the translation result and additional information on the screen. This allows the user to read the description and detailed information of the work in French.

[1443] Prompt Sentence Examples

[1444] "How can users scan the QR code on their device and view exhibit information in French?"

[1445] In this way, the system of the present invention not only provides multilingual information in real time, but also provides more comprehensive information by taking into account the user's feelings.

[1446] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1447] Step 1:

[1448] A user finds a QR code installed at a museum or tourist attraction and launches their device's camera app or QR code reader app. When the user scans the QR code, the URL is loaded into the device. The input is the visual information of the QR code, and the device analyzes the QR code to obtain the URL. The output of this process is a URL (e.g., https: / / example.com / art123).

[1449] Step 2:

[1450] The device combines the URL retrieved from the QR code with the user's language preference (e.g., Spanish) and sends a request to the server. The input is the URL retrieved from the QR code and the device's language preference, which together form the request. The output of this process is the request that is sent (e.g., GET https: / / example.com / art123?lang=es).

[1451] Step 3:

[1452] The server receives a request sent from the terminal. The input is the request from the terminal. The server analyzes the URL and language settings in the request and prepares for database access. The output is database query information (e.g., "art123") based on the analysis results.

[1453] Step 4:

[1454] The server accesses the database based on the request and retrieves the corresponding English source text. The input is the database query information, and the output is the English source text retrieved from the database (e.g., "This is a famous painting by an artist from the 18th century."). The server temporarily stores this information.

[1455] Step 5:

[1456] The server takes the English source text and sends it to an external translation resource (e.g., a machine translation service) to translate it into the user's preferred language. The input is the English source text and the user's preferred language (e.g., Spanish), which are composed into the outgoing request. The output is the outgoing request (e.g., https: / / translate.example.com / translate?text=This%20is%20a%20famous%20painting%20by%20an%20artist%20from%20the%2018th%20century.&to=es).

[1457] Step 6:

[1458] The server receives the translation result from an external translation resource. The input is the response to the translation request, and the output is the translated result (e.g., "Esta es una pintura famosa de un artista del siglo XVIII."). The server temporarily stores the translation result.

[1459] Step 7:

[1460] The server analyzes the user's emotions using an emotion engine. The input is emotional data such as the user's facial expressions and voice, which the emotion recognition engine analyzes. The output of this process is the analysis result (e.g., "interested"). The server then optimizes the translation result.

[1461] Step 8:

[1462] The server returns the optimized translation to the device. The input is the optimized translation. The output is the data sent to the device (e.g., {"translation": "This is a famous piece of art by artist Siglo XVIII.", "additional_info": "To learn more about the artist, click here."}).

[1463] Step 9:

[1464] The device receives the translation results and displays additional information based on the emotion on the screen. The input is the response data from the server, which is displayed on the device's display. The output of this process is the information displayed to the user (e.g., "Esta es una pintura famosa de un artista del siglo XVIII." and "Click here for more information about this artist"). This allows the user to view optimized information in real time.

[1465] (Application example 2)

[1466] 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."

[1467] Multilingual tourist guide systems and information systems have become commonplace in recent years, but few systems provide information that takes the user's emotions into consideration. For example, when scanning a QR code at a museum or tourist attraction to obtain information, only general translations are provided, and information that does not reflect the user's emotions or interests is often not provided. As a result, information may not be properly conveyed to tired or disinterested users. Furthermore, a similar problem occurs in physical stores. When a user shows interest in a product or has a specific emotion, the information provided may not be appropriate.

[1468] 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.

[1469] In this invention, the server includes means for calling an external translation resource and translating the original text into the user's language, means for the server to return the translation result to the terminal, means for the terminal to recognize the user's emotion, and means for the server to adjust the translation result based on the user's emotion. This makes it possible for the user to read the information identification code not only to be provided with information translated in the user's language, but also to be provided with information optimal for the user's emotion.

[1470] An "information identification code" is an identifier that a user can read to obtain specific information, and generally includes QR codes and barcodes.

[1471] A "terminal" is a device used by a user to read an information identification code, and generally includes a smartphone, tablet, or PC.

[1472] A "server" is a computer system that receives requests from terminals and provides information in cooperation with databases and external translation resources.

[1473] A "database" is an information storage system that stores original text corresponding to information identification codes.

[1474] An "external translation resource" is a translation service that the server invokes and uses to translate source text into the user's language, including machine translation services.

[1475] An "emotion engine" is a software or hardware component that uses the device's camera and sensors to analyze the user's emotions.

[1476] "Translation result" is information obtained by converting an original text into a user's language using an external translation resource.

[1477] "Information adjustment" is the process of appropriately modifying and enhancing the content of translation results and additional information based on the user's feelings.

[1478] The system for implementing the present invention consists of a series of processes in which a user reads an information identification code, the terminal sends it to a server, the server performs translation and emotion recognition, and then returns optimized information to the terminal.

[1479] composition

[1480] The system includes the following elements and procedures:

[1481] User terminal: A device used to read information identification codes, such as a smartphone, tablet, or PC.

[1482] Information Identification Code: A QR code or other code used to retrieve specific information.

[1483] Server: Processes incoming requests and performs translation by accessing databases and external translation resources.

[1484] Database: An information storage system that stores corresponding source texts.

[1485] External translation resources: A service that uses a machine translation service to translate source text into the user's language.

[1486] Emotion engine: A software and hardware component that recognizes a user's emotions and optimizes information based on those emotions.

[1487] Translation and information provision process

[1488] 1. Scanning a QR code: When a user scans a QR code with their device's camera or a QR code reader app, a specific URL is retrieved on the device.

[1489] 2. Sending a request: The device sends a request to the server based on the URL and the device's language setting. For example, the request format is "GET https: / / example.com / info123?lang=ja".

[1490] 3. Retrieving information from a database: The server accesses the database based on the identifying information in the URL included in the request and retrieves the relevant original text.

[1491] 4. Send translation request: The server sends the retrieved source text to an external machine translation service to translate it into the specified language.

[1492] 5. Getting the translation: The server receives the translated information and prepares to serve it in the user's preferred language.

[1493] 6. Emotion Recognition and Optimization: The device camera is used to analyze the user's emotions, and the information is optimized based on this emotion recognition, for example, providing additional information if the user is interested.

[1494] 7. Return and display information: The final information is returned to the terminal, which displays it on the screen.

[1495] Technologies used and data processing

[1496] Camera API: Uses the user's device's camera to scan QR codes and analyze facial expressions.

[1497] Translation API: Translates the source text into a specified language using an external machine translation service (e.g., Google Translate API).

[1498] Emotion Recognition API: Use an API to recognize user emotions (e.g., Microsoft Azure's emotion recognition service).

[1499] Specific examples

[1500] For example, when a user scans a QR code placed next to an exhibit in a museum, the following process takes place:

[1501] The user scans the QR code with their smartphone to obtain the URL.

[1502] The obtained URL and the smartphone's language setting (e.g., Japanese) are sent to the server.

[1503] The server retrieves the original English text from the database and then translates it into Japanese using a machine translation service.

[1504] The server receives the translated information and optimizes the information by recognizing the user's emotions.

[1505] The final information is sent back to the user's terminal, and the user views the displayed information.

[1506] Prompt Sentence Examples

[1507] For example, you can use a generative AI model by entering a prompt such as, "Implement an application in JavaScript that scans the QR code of an exhibited work and provides multilingual information based on the user's emotions."

[1508] According to this embodiment, the present invention can provide advanced information in multiple languages ​​and taking into consideration the user's feelings, thereby improving the user experience.

[1509] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1510] Step 1:

[1511] The user reads the information identification code (QR code).

[1512] A user scans the information identification code using a camera app or QR code reader app on their device (e.g., a smartphone). This action causes a specific URL to be read by the device. The input is the scanned QR code, and the output is the retrieved URL. Specifically, the camera reads the QR code and retrieves the data as a string.

[1513] Step 2:

[1514] The device sends the URL and the device's language setting to the server.

[1515] The device combines the obtained URL with the device's current language setting (e.g., Japanese) and sends a request to the server. The input is the scanned URL and the device's language setting, and the output is the request sent to the server. Specifically, the URL and language setting are formatted into an HTTP request and sent to the server.

[1516] Step 3:

[1517] The server retrieves the corresponding source text from the database.

[1518] The server analyzes the received request and retrieves the corresponding text from a database based on the URL identification information. The input is the request sent to the server, and the output is the text retrieved from the database. The specific operation is to execute a database query to extract the corresponding information.

[1519] Step 4:

[1520] The server calls an external translation resource to translate the source text.

[1521] The server uses the retrieved source text to send a request to an external machine translation service to translate it into the specified language. The input is the retrieved source text, and the output is the translation result returned by the external translation service. Specifically, it calls the translation API and receives the result.

[1522] Step 5:

[1523] The server receives the translation result and sends a request to the device for emotion recognition.

[1524] The server receives the translation result returned from the external translation resource and attaches it to a request sent to the user's device. In addition, the device receives an additional request to recognize the user's emotion. The input is the received translation result, and the output is a request to the device. Specific operations include preparing the received data and sending an HTTP request to the device.

[1525] Step 6:

[1526] The device recognizes the user's emotions.

[1527] The device uses a camera to analyze the user's face and recognizes the user's emotions using an emotion engine. The input is the user's face image taken with the device's camera, and the output is the recognized emotion data. Specifically, the emotion recognition API is used to extract the emotion data.

[1528] Step 7:

[1529] The server adjusts the translation results based on the user's emotions.

[1530] The server receives the emotion data sent from the device and adjusts the translation results based on it. For example, if the user shows interest, it inserts additional relevant information. The input is emotion data and the translation result, and the output is the final translation result that takes emotion into consideration. Specifically, it performs conditional judgments on the emotion data and modifies the information.

[1531] Step 8:

[1532] The server sends the final translation result back to the terminal, which displays it.

[1533] The server returns the adjusted final translation result to the terminal, which displays it on the screen. The input is the adjusted translation result, and the output is the information displayed on the terminal. The specific operation is to send the data to the terminal and process it to display it on the screen.

[1534] 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.

[1535] 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.

[1536] 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 robot 414.

[1537] 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.

[1538] FIG. 9 illustrates 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 behaviors 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.

[1539] 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.

[1540] 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).

[1541] 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.

[1542] 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."

[1543] 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.

[1544] 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).

[1545] 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.

[1546] 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.

[1547] 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.

[1548] 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.

[1549] 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.

[1550] 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.

[1551] 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.

[1552] 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.

[1553] 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.

[1554] 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.

[1555] The following is further disclosed regarding the above embodiment.

[1556] (Claim 1)

[1557] means for a user to read the information identification code;

[1558] a means for transmitting a request to a server based on information acquired by the terminal from the information identification code;

[1559] means for retrieving source text from a database based on a request received by the server;

[1560] A means for the server to call an external translation resource and translate the source text into the user's language;

[1561] A means for the server to return the translation result to the terminal;

[1562] A means for the terminal to display the translation result;

[1563] A system including:

[1564] (Claim 2)

[1565] 10. The system of claim 1, wherein the information identification code is configured as a QR code.

[1566] (Claim 3)

[1567] 10. The system of claim 1, wherein the external translation resource is a machine translation service.

[1568] "Example 1"

[1569] (Claim 1)

[1570] means for a user to read the information identification code;

[1571] a means for transmitting a request to a server based on information acquired by the terminal from the information identification code and the user's language setting;

[1572] means for retrieving source text from a database based on a request received by the server;

[1573] A means for the server to call an external translation resource and translate the source text into the user's language;

[1574] A means for the server to return the translation result to the terminal;

[1575] A means for the terminal to display the translation result;

[1576] A system including:

[1577] (Claim 2)

[1578] 2. The system of claim 1, wherein the information identification code is configured as a two-dimensional code.

[1579] (Claim 3)

[1580] 10. The system of claim 1, wherein the external translation resource is an automated translation service.

[1581] "Application Example 1"

[1582] (Claim 1)

[1583] means for a user to read the information identification code;

[1584] a means for transmitting a request to a server based on information acquired by the terminal from the information identification code;

[1585] means for retrieving source text from a database based on a request received by the server;

[1586] A means for the server to call an external translation resource and translate the source text into the user's language;

[1587] A server generates a prompt sentence using an AI model for generating a translation result, and acquires the translation result based on the prompt sentence;

[1588] A means for the server to return the translation result to the terminal;

[1589] A means for the terminal to display the translation result;

[1590] A system including:

[1591] (Claim 2)

[1592] The system of claim 1, wherein the information identification code is configured as a QR code, and the translation result is obtained based on the generative AI model.

[1593] (Claim 3)

[1594] The system of claim 1, which uses a machine translation service as an external translation resource and performs translation based on prompt sentences using a generative AI model.

[1595] "Example 2: Combining Emotion Engines"

[1596] (Claim 1)

[1597] means for a user to read the information identification code;

[1598] a means for transmitting a request to a server based on information acquired by the terminal from the information identification code;

[1599] means for retrieving source text from a database based on a request received by the server;

[1600] A means for the server to call an external translation resource and translate the source text into the user's language;

[1601] A means for the server to analyze the user's emotions using an emotion recognition engine and optimize the translation result based on the analysis result;

[1602] A means for the server to return the optimized translation result to the terminal;

[1603] a means for the terminal to display the translation result and additional information;

[1604] A system including:

[1605] (Claim 2)

[1606] 10. The system of claim 1, wherein the information identification code is configured as a QR code.

[1607] (Claim 3)

[1608] 10. The system of claim 1, wherein the external translation resource is a machine translation service.

[1609] "Application example 2 when combining emotion engines"

[1610] (Claim 1)

[1611] means for a user to read the information identification code;

[1612] a means for transmitting a request to a server based on information acquired by the terminal from the information identification code;

[1613] means for retrieving source text from a database based on a request received by the server;

[1614] A means for the server to call an external translation resource and translate the source text into the user's language;

[1615] A means for the server to return the translation result to the terminal;

[1616] A means for the terminal to display the translation result;

[1617] A means for the terminal to recognize the user's emotion;

[1618] A means for the server to adjust the translation result based on the user's emotion;

[1619] A system including:

[1620] (Claim 2)

[1621] 10. The system of claim 1, wherein the information identification code is configured as a QR code.

[1622] (Claim 3)

[1623] 10. The system of claim 1, wherein the external translation resource is a machine translation service. [Explanation of symbols]

[1624] 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. means for a user to read the information identification code; a means for transmitting a request to a server based on information acquired by the terminal from the information identification code; means for retrieving source text from a database based on a request received by the server; A means for the server to call an external translation resource and translate the source text into the user's language; A means for the server to return the translation result to the terminal; A means for the terminal to display the translation result; A system including:

2. 2. The system of claim 1, wherein the information identification code is configured as a QR code.

3. The system of claim 1 , wherein the external translation resource is a machine translation service.

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