system

Abstract

Provide a system. The system includes: a means for receiving documents sent from a school or educational institution; means for translating the received document into a target language; A means for transmitting the translation result to the parent's device; A means for detecting specific words and phrases and obtaining their corresponding descriptions; A means for translating the acquired explanatory text into a target language and transmitting the translation result to the parent's device; A means of receiving messages from parents and translating them into the institution's language; a means for transmitting the translated message to the educational institution; a means for receiving reply messages from the educational institution and translating them into the parent's target language; The system includes a means for sending a translated reply message to the parent's device.

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 recent years, many foreign children and students have been enrolled in Japanese public schools. However, if their parents cannot understand Japanese, communication between the school or kindergarten and their parents can become problematic. In particular, when it comes to daily newsletters, communication notebooks, and emergency notices, the language barrier can make it difficult for information to be conveyed accurately, leading to a lack of understanding and anxiety among parents. Furthermore, unfamiliarity with Japan's unique events and preparations can sometimes make it difficult for parents to respond appropriately. There is a need for a system that can remove these linguistic and cultural barriers and enable smooth and accurate communication. [Means for solving the problem]

[0005] The present invention proposes a system for receiving documents sent from schools or educational institutions, translating them into a target language, and providing the translated documents to parents. Specifically, the system includes the following means:

[0006] First, it provides a means for receiving documents sent from a school or educational institution and includes a means for translating the documents into a target language. The translated documents are then sent to the parent's device.

[0007] Next, the device has a means for detecting specific words or phrases in the received document, and a means for retrieving explanatory text corresponding to these words or phrases from a database, translating the explanatory text into a target language, and providing the translated text to the guardian.

[0008] The system further includes means for receiving a message from a parent or guardian and translating the message into a language of the educational institution, then transmitting the translated message to the educational institution, and receiving a reply message from the educational institution and translating the reply message into the parent's target language, which is then transmitted to the parent's device.

[0009] By combining these methods, smooth and accurate communication can be achieved, transcending language barriers, and conventional problems can be resolved.

[0010] "Schools and educational institutions" refers to facilities that provide education, including elementary schools, junior high schools, high schools, and kindergartens in Japan.

[0011] "Documents" refers to text information such as letters, communication books, notices, and guides sent by schools and educational institutions to parents.

[0012] "Means for receiving" refers to a system in which a server, terminal, etc. has the function of receiving data from outside using a specific communication protocol.

[0013] "Means of translation" refers to machine translation technology or natural language processing technology for converting text written in a particular language into another language.

[0014] "Parent's device" refers to electronic devices such as smartphones, tablets, and computers used by parents.

[0015] "Specific words and phrases" refer to vocabulary and expressions in Japanese contained in the document that are considered difficult for foreigners to understand.

[0016] "Explanation" refers to a brief explanation of the meaning of a particular word or phrase and related information.

[0017] A "database" refers to an information system that stores, manages, and allows searching and retrieval of explanatory text corresponding to specific words or phrases.

[0018] "Message" refers to text information such as questions, inquiries, and requests sent by parents to schools or educational institutions.

[0019] "Reply message" refers to text information containing replies or instructions sent by a school or educational institution to parents. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] The present invention is a translation system for appropriately conveying documents from schools and educational institutions to parents, and realizes smooth communication beyond language barriers. The system of the present invention is specifically implemented as follows.

[0042] First, in terms of the system configuration, documents created by users (schools and educational institutions) are sent to a server via the Internet. The server then analyzes the contents of the document and translates it as necessary.

[0043] 1. Automatic translation of letters and contacts

[0044] The user (school or educational institution) creates a document in Japanese and sends it to the server. The server analyzes the received document and translates it into the target language using natural language processing technology. The translated document is then sent from the server to the parent's device, who can receive the document in a language they can understand.

[0045] Examples:

[0046] A user (school) creates and sends a notification saying, "There will be a sports day next Monday. Please bring your sportswear and water bottles."

[0047] The server receives this document and translates it into "Next Monday, we will have a sports day. Please bring sportswear and a water bottle."

[0048] The translated document will be sent to the parent's device.

[0049] 2. Explanation of specific words and phrases

[0050] When analyzing the content of the document, the server detects certain words and phrases. For these words and phrases, the server retrieves corresponding explanations from a database and provides them to the parent. The explanations are then translated into the target language and sent as an attachment to the original document.

[0051] Examples:

[0052] The server detects the word "sports day" and retrieves the description "sports day is a sporting event held at school" from the database.

[0053] The server adds the explanation "Sports day is a school event where students participate in various sports activities" to the document and sends it along with the translated document.

[0054] 3. Communication between both parties

[0055] When a parent sends a message in their native language, such as English, the message is received by the server. The server translates the received message into Japanese and sends it to the educational institution. The educational institution's reply is also sent to the server, where it is translated into the target language and then sent to the parent's device.

[0056] Examples:

[0057] A parent sends a message saying, "My child has a doctor's appointment on Friday. Will there be any important activities that day?"

[0058] The server translates this to "My child has a hospital appointment on Friday. Do you have any important activities on that day?" and sends it to the institution.

[0059] The educational institution replies, "There are no important activities on that day."

[0060] The server translates this as "There are no particularly important activities on that day" and sends it to the parent's device.

[0061] This system will ensure smooth communication between schools and educational institutions and foreign parents, and will ensure that necessary information is accurately conveyed to parents who do not understand Japanese.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] A user (such as a school or educational institution) creates a document and sends it to the server. The document can include, for example, a letter or a contact list.

[0065] Step 2:

[0066] The server analyzes the Japanese document received from the user, taking in the document's contents in text format and extracting specific information from it.

[0067] Step 3:

[0068] The server translates the document content into the target language. The server uses natural language processing (NLP) technology to analyze the entire document and translate it appropriately. For example, it calls a translation API to translate "There will be a sports day next Monday." into "Next Monday, we will have a sports day."

[0069] Step 4:

[0070] The server detects specific words and phrases within a document, for example, event names such as "athletic meet" or "athletic festival" or special words.

[0071] Step 5:

[0072] The server retrieves from the database the description that corresponds to the detected word or phrase, for example, "Undokai is a sports event held at Japanese schools."

[0073] Step 6:

[0074] The server translates the retrieved description into the target language. For example, the description could be translated as "Sports day is an event held at Japanese schools where students participate in various sports activities."

[0075] Step 7:

[0076] The server adds an explanatory text to the translated document, for example, "Next Monday, we will have a sports day. (Sports day is an event held at Japanese schools where students participate in various sports activities)."

[0077] Step 8:

[0078] The server sends the translated document to the parent's device, allowing the parent to receive information in their own language.

[0079] Step 9:

[0080] The user (parent) enters questions or comments in their native language and sends them to the server from their device. For example, they send a message like, "My child has a doctor's appointment on Friday. Will there be any important activities that day?"

[0081] Step 10:

[0082] The server receives the message from the parent and translates it into Japanese, saying, "My child has a hospital appointment on Friday. Do you have any important activities on that day?"

[0083] Step 11:

[0084] The server sends the translated message to the educational institution, which can then understand the parent's question in Japanese.

[0085] Step 12:

[0086] The user (educational institution) enters a reply message in Japanese and sends it to the server. For example, the reply may say, "There are no particularly important activities on that day."

[0087] Step 13:

[0088] The server receives the reply message from the educational institution and translates it into the parent's native language, stating, "There are no particularly important activities on that day."

[0089] Step 14:

[0090] The server then sends the translated reply message to the parent's device, allowing the parent to receive a reply in their own language and respond appropriately.

[0091] As described above, each step of the process is carried out sequentially, and smooth and accurate communication between users is achieved through this series of processes.

[0092] Example 1

[0093] 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."

[0094] Traditionally, communication between schools and educational institutions and parents has often been hindered by language barriers. Important notices and event notifications may not be conveyed accurately to parents, leading to misunderstandings and inconvenience. Furthermore, questions and messages from parents may not be translated properly, making smooth communication with educational institutions difficult.

[0095] 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.

[0096] In this invention, the server includes means for receiving documents sent from a school or educational institution, means for analyzing the received documents, means for translating the received documents into a target language, means for sending the translation results to the parent's terminal, means for detecting specific words or phrases and obtaining corresponding explanatory text, means for translating the obtained explanatory text into the target language and sending the translation results together with the explanatory text to the parent's terminal, means for receiving messages from the parent and translating them into the language of the educational institution, means for sending the translated messages to the educational institution, means for receiving reply messages from the educational institution and translating them into the parent's target language, means for sending the translated reply messages to the parent's terminal, means for presenting received documents in a form that is easy for users to understand, and means for translating messages sent by users into the original language of the documents, thereby enabling smooth and accurate communication that transcends language barriers.

[0097] "Schools and educational institutions" are organizations and facilities established for the purpose of education, including daycare centers, kindergartens, elementary schools, junior high schools, high schools, universities, and vocational schools.

[0098] A "document" is written information or instructions in text form that is produced for the purpose of communication or record-keeping.

[0099] "Receiving" is the act of receiving information or data sent from another party.

[0100] A "server" is a central computer that processes data and provides services to other computers and devices over a network.

[0101] "Analysis" is the act of investigating and evaluating data and information in detail to clarify their meaning and structure.

[0102] "Target language" is the language into which the original document is to be translated in a translation task.

[0103] "Translation" is the act of converting content written in one language into another language.

[0104] A "terminal" is a device connected to a computer system that can be operated by a user.

[0105] "Specific words or phrases" refers to certain keywords or expressions that are important or frequently appear in a document or message.

[0106] An "explanatory text" is a piece of text that explains the meaning and usage of a particular word or phrase.

[0107] A "message" is a document or word sent between users to convey information.

[0108] A "database" is a collection of information that has been organized and stored so that the data can be efficiently searched and used.

[0109] "Machine learning" is a technology that uses algorithms and statistical models to allow computers to learn from experience and perform data analysis and predictions.

[0110] MODE FOR CARRYING OUT THE INVENTION

[0111] This invention is a translation system for properly conveying documents sent from schools and educational institutions to parents, realizing smooth communication beyond language barriers. This system is specifically implemented through the process of receiving, analyzing, translating, and transmitting data between a server, terminals, and users.

[0112] First, a user (school or educational institution) creates a document in Japanese and sends it to a server via the Internet from their terminal. The user can use a standard computer or mobile device to send the document using a web interface or a dedicated application.

[0113] The server parses the received document using a natural language processing library (e.g., spaCy or NLTK) to extract important keywords and phrases from the document.

[0114] The server then calls a translation service such as Google® Translate API or DeepL to translate the analyzed document into the target language. The translated document is then sent to the parent's device, allowing the parent to receive the document in a language they can understand.

[0115] For example, if a user creates a document stating, "We have a sports day next Monday. Please bring sportswear and a water bottle.", the server translates this into "Next Monday, we will have a sports day. Please bring sportswear and a water bottle." and sends it to the parent's device.

[0116] The server also detects specific words and phrases in the document and retrieves corresponding explanations from the database. The explanations are also translated into the target language and added to the original document before being sent to the parent's device. This process allows the parent to gain a deeper understanding of the document's contents.

[0117] For example, if the server detects the word "sports day," it retrieves the explanation "sports day is a school event where students participate in various sports activities" from the database, translates it into "sports day is a school event where students participate in various sports activities," and adds it to the document. The document with this additional explanation is then sent to the parent's device.

[0118] Furthermore, messages from parents are also exchanged via the server. When parents send messages in their native language, such as English, the server translates them into Japanese and sends them to the educational institution. Responses from the educational institution are also sent to the server, which translates them into the parent's target language. This ensures smooth two-way communication between the educational institution and parents.

[0119] Examples of specific prompts include:

[0120] "Please translate the Japanese sentence, 'There will be a sports day next Monday. Please bring your sportswear and water bottles.' into English."

[0121] "Write a sentence explaining the meaning of the word 'sports day'."

[0122] "Please translate the following English message into Japanese: 'My child has a doctor's appointment on Friday. Will there be any important activities that day?'"

[0123] This makes it possible to obtain appropriate translations and descriptions from generative AI models.

[0124] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0125] Step 1:

[0126] A user (school or educational institution) creates a document and sends it to the server from their device.

[0127] Specifically, a user uses word processing software or a dedicated application to create a document, such as "There will be a sports day next Monday. Please bring your sportswear and water bottle." The document is then uploaded to a server from the terminal via the Internet. The input is the document, and the output is the uploading of the document to the server.

[0128] Step 2:

[0129] The server parses the received document.

[0130] The server begins analyzing the received document using a natural language processing library (e.g., spaCy or NLTK). Specifically, it extracts important keywords and phrases (e.g., "sports day") from the document. The input is the received document, and the output is the extracted keywords and phrases.

[0131] Step 3:

[0132] The server translates the document into the target language.

[0133] The server calls a translation service such as Google Translate API or DeepL and translates the parsed document into the target language. For example, it translates "We will have a sports day next Monday. Please bring sportswear and a water bottle." into "Next Monday, we will have a sports day. Please bring sportswear and a water bottle." The input is the parsed document, and the output is the translated document.

[0134] Step 4:

[0135] The server sends the translation results to the parent's device.

[0136] The server sends the translated document to the designated parent's device. Specifically, the translated document is delivered to the parent via email or a dedicated application. The input is the translated document, and the output is the document sent to the parent's device.

[0137] Step 5:

[0138] The server detects specific words or phrases in the document and retrieves their corresponding descriptions from a database.

[0139] The server detects specific words or phrases (e.g., "sports day") from the parsed document and retrieves the description "sports day is a sporting event held at school" from the database. The input is the parsed document, and the output is the retrieved description.

[0140] Step 6:

[0141] The server translates the description into the target language and adds it to the original document.

[0142] The server translates the retrieved description into the target language using the Google Translate API or DeepL. Specifically, it translates "Sports day is a school event where students participate in various sports activities" into "Sports day is a school event where students participate in various sports activities" and adds it to the translated document. The input is the retrieved description, and the output is the translated document with the description added.

[0143] Step 7:

[0144] The server sends the translated document with explanatory text to the parent's device.

[0145] The server adds explanatory text to the translated document and sends it to the parent's device. Specifically, it presents it in a format that is easy for the parent to understand. The input is the translated document with explanatory text added, and the output is the translated document with explanatory text sent to the parent's device.

[0146] Step 8:

[0147] The server receives messages from parents and translates them into the institution's language.

[0148] For example, a parent sends a message from their device to the server, such as "My child has a doctor's appointment on Friday. Will there be any important activities that day?" The server translates this to "My child has a doctor's appointment on Friday. Will there be any important activities that day?" The input is the message from the parent, and the output is the message translated into the language of the educational institution.

[0149] Step 9:

[0150] The server sends the translated message to the institution.

[0151] The server sends the translated message to the educational institution's terminal. The specific operation is to provide information in a form that the educational institution can easily understand. The input is the translated message, and the output is the message sent to the educational institution's terminal.

[0152] Step 10:

[0153] The server receives the reply message from the educational institution and translates it into the parent's target language.

[0154] The institution sends a reply to the server, for example, "There are no particularly important activities on that day." The server translates this to "There are no particularly important activities on that day." The input is the reply message from the institution, and the output is the translated reply message.

[0155] Step 11:

[0156] The server sends the translated reply message to the parent's device.

[0157] The server sends the translated reply message to the parent's device. Specifically, the server provides the translated reply message in a format that the parent can easily check. The input is the translated reply message, and the output is the message sent to the parent's device.

[0158] (Application example 1)

[0159] 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."

[0160] The present invention relates to a system for accurately and quickly transmitting order information between customers who speak different languages ​​and restaurants, thereby eliminating language barriers in food delivery services.

[0161] Specifically, there is a need for a means that allows customers to place orders in their own language, ensures that the restaurant accurately understands the content of the orders, and also allows the restaurant to properly convey order confirmation messages to customers.

[0162] 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.

[0163] In this invention, the server includes means for receiving a document sent from a school or educational institution, means for translating the received document into a target language, means for transmitting the translation result to the parent's terminal, means for detecting specific words or phrases and obtaining corresponding explanatory text, means for translating the obtained explanatory text into the target language and transmitting the translation result together with the explanatory text to the parent's terminal, means for receiving a message from the parent and translating it into the language of the educational institution, means for transmitting the translated message to the educational institution, means for receiving a reply message from the educational institution and translating it into the parent's target language, means for transmitting the translated reply message to the parent's terminal, means for receiving order information from a customer and translating it into the restaurant's language, means for transmitting the translated order information to the restaurant, means for receiving order confirmation information from the restaurant and translating it into the customer's target language, and means for transmitting the translated order confirmation information to the customer's terminal. This enables order information to be communicated accurately and quickly between customers and restaurants, eliminating language barriers.

[0164] "Schools and educational institutions" refers to organizations and facilities for carrying out educational activities, such as elementary schools, junior high schools, high schools, universities, and vocational schools.

[0165] "Document" means any paper or digital file containing textual information, including notices, correspondence, reports, etc.

[0166] "Translation means" refers to the methods and techniques used to convert a document written in one language into another language.

[0167] "Parental devices" refers to electronic devices such as computers, smartphones, and tablets used by parents of children or students.

[0168] "Specific words and phrases" refer to specific keywords and expressions in a document, and are words and phrases that contain important information that requires explanation.

[0169] An "explanation" is text information that provides detailed explanations of the meaning and background of specific words or phrases.

[0170] "Customer" means the person or entity placing an order to purchase or use a service or product.

[0171] "Order Information" means the requests and details provided by Customer when purchasing goods or services.

[0172] "Food and beverage establishments" refers to establishments and restaurants that serve food and beverages.

[0173] "Order confirmation information" is information that a restaurant sends back after confirming the order details received from a customer.

[0174] System Configuration

[0175] The present invention is a translation system for accurately and quickly communicating order information between customers and restaurants, and includes the following components: a server, a customer terminal, and a restaurant terminal, all of which work in conjunction with one another.

[0176] Hardware and Software

[0177] The hardware used is as follows:

[0178] Server (e.g., Amazon Web Services EC2 instance)

[0179] Customer and restaurant devices (e.g., smartphones, tablets, computers)

[0180] The software used is as follows:

[0181] Server-side framework: Flask (a Python micro web framework)

[0182] Translation engine: Google Translate API

[0183] Natural language processing techniques and machine learning algorithms: Machine learning models (e.g., BERT, GPT-3 (registered trademark))

[0184] Data processing and calculation flow

[0185] The server processes the data according to the following procedure.

[0186] 1. Document Receipt and Translation:

[0187] The server receives the order information entered on the customer's device. For example, the sentence "I would like to order a large pizza and a Coke" is sent.

[0188] The server uses the Google Translate API to translate this order information from the customer's native language into the restaurant's language.

[0189] 2. Sending Order Information:

[0190] When the translation result is "I would like to order a large pizza and a Coke," it is sent to the restaurant's terminal.

[0191] 3. Receiving and translating order confirmation messages:

[0192] Receive order confirmation from the restaurant terminal, such as "We have received your order for a large pizza and a Coke."

[0193] The server uses the Google Translate API to translate this confirmation information from the restaurant's language into the customer's native language.

[0194] 4. Sending confirmation information:

[0195] Once the translation result is "Your order for a large pizza and a Coke has been accepted," it is sent to the customer's terminal.

[0196] Specific examples

[0197] For example, if an English-speaking customer places an order by saying, "I would like to order a large pizza and a Coke," this order information is translated and transmitted as follows:

[0198] "I would like to order a large pizza and a Coke."

[0199] "I'd like to order a large pizza and a Coke."

[0200] Similarly, a confirmation message from a restaurant saying "We have received your order for a large pizza and a Coke" would be translated and sent as follows:

[0201] "Your order for a large pizza and a Coke has been accepted."

[0202] Translated into:English "We have received your order for a large pizza and a Coke."

[0203] Prompt Sentence Examples

[0204] English prompt:

[0205] "I would like to order a large pizza and a Coke."

[0206] Translated Japanese:

[0207] "I'd like to order a large pizza and a Coke."

[0208] In this way, the system of the present invention allows customers to input orders in their native language and accurately translates them into the restaurant's language, enabling smooth communication of order information across language barriers. By using the Google Translate API as the translation engine and performing detailed data processing on the server side, accurate and prompt communication between customers and restaurants is achieved.

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1:

[0211] A user inputs order information into a terminal. The input information is text data such as "I would like to order a large pizza and a Coke." This information is in the customer's native language and is sent from the terminal to the server.

[0212] Step 2:

[0213] The server receives the order information from the customer's device. The input is in the customer's native language (e.g., "I would like to order a large pizza and a Coke."), and this information is converted for processing within the server.

[0214] Step 3:

[0215] The server translates the order information using the Google Translate API. The input is the order information in English, the customer's native language, and the output is the order information translated into the restaurant's language (e.g., "I'd like to order a large pizza and a Coke."). The server accesses the API, converts the text data, and obtains the translation results.

[0216] Step 4:

[0217] The server sends the translated order information to the restaurant's terminal. The input is the translated order information (e.g., "I'd like to order a large pizza and a Coke."), and the output is the data sent to the restaurant's terminal. The server sends the information using a network protocol.

[0218] Step 5:

[0219] The restaurant terminal receives the order information and generates a confirmation message. The input is the translated order information received from the server, and the output is a confirmation message written in the restaurant's language (e.g., "We have received your order for a large pizza and a Coke."). The restaurant terminal may automatically generate this message, or a staff member may type it in.

[0220] Step 6:

[0221] The server receives an order confirmation message from the restaurant terminal. The input is the confirmation message in the restaurant's language (e.g., "We have received your order for a large pizza and a Coke."), and the output is retained for processing within the server.

[0222] Step 7:

[0223] The server translates the confirmation message using the Google Translate API. The input is the confirmation message in the restaurant's language, and the output is the confirmation message translated into the customer's native language (e.g., "Your order for a large pizza and a Coke has been accepted."). The server accesses the API, converts the text data, and obtains the translation result.

[0224] Step 8:

[0225] The server sends a translated confirmation message to the customer's device. The input is the translated confirmation message (e.g., "Your order for a large pizza and a Coke has been accepted.") and the output is the data sent to the customer's device. The server sends the information using a network protocol.

[0226] This series of processes enables smooth communication between customers who speak different languages ​​and restaurants, allowing for accurate and prompt ordering and confirmation.

[0227] 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.

[0228] This invention is a system for facilitating communication between schools and educational institutions and parents, and in particular provides technology for accurately conveying information across language barriers. A key feature of this invention is that it recognizes the user's emotions by combining an emotion engine and applies a translation tone accordingly. The processing flow of this system will now be described in detail.

[0229] 1. Automatic translation of letters and contacts

[0230] A user (school or educational institution) creates a document in Japanese and sends it to the server. The server analyzes the received document and uses natural language processing technology to translate the content into the target language. The translated document is then sent from the server to the parent's device, who can receive the document in a language they can understand. The emotion engine can also analyze the emotion contained in the document and adjust the translation tone accordingly.

[0231] Examples:

[0232] A user (school) creates and sends a notification saying, "There will be a sports day next Monday. Please bring your sportswear and water bottles."

[0233] The server receives this document and translates it into "Next Monday, we will have a sports day. Please bring sportswear and a water bottle."

[0234] The emotion engine recognizes the tone of the document as "cheerful" and reflects this in the translation.

[0235] The translated document will be sent to the parent's device.

[0236] 2. Explanation of specific words and phrases

[0237] When analyzing the content of a document, the server detects certain words and phrases. For these words and phrases, the server retrieves corresponding explanations from a database and provides them to parents as part of the translation. After the explanations are translated into the target language, they are added to the original document.

[0238] Examples:

[0239] The server detects the word "sports day" and retrieves the description "sports day is a sports event held at Japanese schools" from the database.

[0240] The server translates the description into "Sports day is an event held at Japanese schools where students participate in various sports activities."

[0241] The server responds to the document with the following: "Next Monday, we will have a sports day. Please bring sportswear and a water bottle. (Sports day is an event held at Japanese schools where students participate in various sports activities)."

[0242] 3. Communication between both parties

[0243] When a parent sends a message in their native language, the message is received by the server. The server translates the received message into Japanese and sends it to the educational institution. Reply messages are similarly translated and sent to the parent's device in their native language. The server uses an emotion engine to recognize the emotions contained in the parent's and educational institution's messages and applies the corresponding translation tone.

[0244] Examples:

[0245] A parent sends a message saying, "My child has a doctor's appointment on Friday. Will there be any important activities that day?"

[0246] The server translates this to "My child has a hospital appointment on Friday. Do you have any important activities on that day?" and sends it to the institution.

[0247] The emotion engine recognizes the emotion of the question as "concern" and adjusts the tone of the reply appropriately.

[0248] The educational institution replies, "There are no important activities on that day."

[0249] The server translates this as "There are no particularly important activities on that day" and sends it to the parent's device.

[0250] This invention enables smooth and accurate communication that transcends language barriers, deepening understanding between parents and educational institutions. By utilizing an emotion engine, the content of messages is conveyed appropriately, making dialogue even smoother.

[0251] The processing flow will be explained below.

[0252] Step 1:

[0253] A user (school or educational institution) creates a document and sends it to the server. For example, a user creates a notification saying, "We have a sports day next Monday. Please bring your sports clothes and water bottles."

[0254] Step 2:

[0255] The server analyzes the Japanese document received from the user, captures the document content in text format, and prepares it for translation.

[0256] Step 3:

[0257] The server uses natural language processing technology to translate the content of the document into the target language. For example, when translating a Japanese document into English, the server converts it into "Next Monday, we will have a sports day. Please bring sportswear and a water bottle."

[0258] Step 4:

[0259] The server detects specific words and phrases within a document, for example recognizing words like "sports day" and "sportswear."

[0260] Step 5:

[0261] The server retrieves from the database the description that corresponds to the detected word or phrase, for example, "Undokai is a sports event held at Japanese schools."

[0262] Step 6:

[0263] The server translates the obtained description into the target language. For example, "Sports day is an event held at Japanese schools where students participate in various sports activities."

[0264] Step 7:

[0265] The server generates a document with the translated explanation added. For example, the server adds an explanation to the translated document, such as "Next Monday, we will have a sports day. Please bring sportswear and a water bottle. (Sports day is an event held at Japanese schools where students participate in various sports activities)."

[0266] Step 8:

[0267] The server uses an emotion engine to analyze the emotion contained in the document, for example, recognizing the document's tone as "cheerful."

[0268] Step 9:

[0269] The server adjusts the translation tone based on the emotion recognition results. For example, it sets an appropriate translation tone and provides the document to the parent as a cheerful document.

[0270] Step 10:

[0271] The server sends the translated document to the parent's device, where the parent can receive the document in a language they understand.

[0272] Step 11:

[0273] The user (parent) creates a message in their native language and sends it to the server from their device. For example, they send a question such as, "My child has a doctor's appointment on Friday. Will there be any important activities that day?"

[0274] Step 12:

[0275] The server receives the message from the parent and uses natural language processing technology to translate it into Japanese, for example, "My child has a hospital appointment on Friday. Are there any important activities on that day?"

[0276] Step 13:

[0277] The server sends the translated message to the educational institution, which can then receive the parent's questions in Japanese.

[0278] Step 14:

[0279] The server uses an emotion engine to analyze the emotion contained in the parent's message. For example, the emotion engine recognizes "worry."

[0280] Step 15:

[0281] The user (educational institution) creates a reply message in Japanese and sends it to the server. For example, they reply, "There are no particularly important activities on that day."

[0282] Step 16:

[0283] The server receives the reply message from the educational institution and translates it into the target language, for example, "There are no particularly important activities on that day."

[0284] Step 17:

[0285] The server uses an emotion engine to analyze the emotion contained in the educational institution's reply message, for example recognizing "a sense of relief."

[0286] Step 18:

[0287] The server adjusts the translation tone based on the emotion recognition result, and applies an appropriate tone to provide the translated message to the parent.

[0288] Step 19:

[0289] The server sends the translated reply message to the parent's device, who can then receive the reply in their own language through the device.

[0290] As described above, processing is performed at each step, ensuring smooth communication between users.

[0291] Example 2

[0292] 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."

[0293] Language barriers can be an obstacle when communicating between schools and parents, making it difficult to convey information accurately. Furthermore, the quality of communication can decline if emotional nuances are not conveyed properly when documents are translated. Furthermore, replies and inquiries from parents cannot be handled quickly and accurately.

[0294] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0295] In this invention, the server includes a means for receiving a document sent from a school or educational institution, a means for translating the received document into a target language, and a means for analyzing the emotion of the document using an emotion recognition engine and adjusting the translation tone. This enables accurate communication that transcends language barriers and incorporates emotional nuances. The server also includes a means for retrieving specific words or phrases from a database and translating the corresponding explanatory text into the target language, a means for translating the retrieved explanatory text into the target language and sending the translation result to the parent's terminal, a means for receiving a message from the parent and translating it into the language of the educational institution, a means for sending the translated message to the educational institution, a means for receiving a reply message from the educational institution and translating it into the parent's target language, and a means for sending the translated reply message to the parent's terminal. This system enables rapid and accurate two-way communication.

[0296] "Schools and educational institutions" is a general term for organizations that provide education to children and students.

[0297] "Document" refers to documents or text data that express information using characters and diagrams.

[0298] A "server" refers to a computer system that provides services and data in response to client requests over a network.

[0299] "Target language" refers to a language other than Japanese that is designated as the target language for translation.

[0300] "Translation means" refers to a function that converts a document written in one language into another language.

[0301] An "emotion recognition engine" refers to a software system that analyzes the emotions and tone in a document and adjusts the appropriate translation tone based on the results.

[0302] A "terminal" refers to a device, such as a PC or smartphone, that allows a user to receive or send information over a network.

[0303] "Specific words and phrases" refer to words or expressions that are considered particularly important in the document to be translated.

[0304] A "database" refers to a system that organizes and stores specific data so that it can be easily searched and retrieved.

[0305] "Parent" refers to a parent or legal guardian of a child or student, or any other adult who is required to communicate with an educational institution.

[0306] A "machine learning algorithm" refers to a computational method that learns patterns from data and uses those patterns to predict or classify new data.

[0307] The present invention provides a system for facilitating communication between schools or educational institutions and parents, and in particular provides technology for accurately transmitting information across language barriers. Specific embodiments of this system are described in detail below.

[0308] Hardware and software used

[0309] Server: Use a high-performance network server, such as an Apache or Nginx server.

[0310] Device: A device used by users and parents. This primarily refers to devices that can connect to the internet, such as smartphones, tablets, and PCs.

[0311] Translation API: Used to automatically translate documents. Specifically, Google Translate API and Microsoft Translator are used.

[0312] Natural language processing libraries: Used for document analysis and sentiment recognition. Examples include the nltk library and spaCy.

[0313] Machine learning models, such as OpenAI's GPT model, are used to analyze the sentiment of a document and adjust the translation tone.

[0314] Explanation of system processing

[0315] 1. Sending documents

[0316] Users (schools and educational institutions) create letters and contacts in Japanese, and the documents are sent to the server via a specified API endpoint.

[0317] Example: A user creates a notification that says, "We have a sports day next Monday. Please bring your sports clothes and water bottle."

[0318] 2. Document analysis on the server

[0319] The server analyzes the received document, tokenizing it using a natural language processing library (e.g., nltk, spaCy) and performing syntax and sentiment analysis.

[0320] 3. Translation Processing

[0321] Based on the analysis results, the server identifies the target language and calls a translation API (e.g., Google Translate API) to translate the document into the target language, using an emotion recognition engine to analyze the sentiment of the document and adjust the appropriate translation tone.

[0322] Example: The server translates the sentence "There will be a sports day next Monday." into "Next Monday, we will have a sports day.", and the emotion engine determines that this sentence is "cheerful."

[0323] 4. Sending translation results

[0324] The server converts the translated text into packets and sends them to the parent's device, which interprets the received packets and displays them on the screen.

[0325] 5. Processing responses from parents

[0326] Parents can create a reply message in their own language and send it to the server. The server then translates the message and sends it to the educational institution. The educational institution's reply is also translated via the server and sent to the parent's device.

[0327] Examples of concrete examples and prompts

[0328] Example: A parent sends a message saying, "My child has a doctor's appointment on Friday. Will there be any important activities that day?" The server translates this to "My child has a doctor's appointment on Friday. Will there be any important activities that day?" and sends it to the educational institution. The educational institution replies, "There are no particularly important activities on that day," which the server translates to "There are no particularly important activities on that day" and sends it to the parent's device.

[0329] Example prompt sentence:

[0330] "We have a sports day next Monday. Please bring your sports clothes and water bottles."

[0331] "My child has a doctor's appointment on Friday. Will there be any important activities that day?"

[0332] In this way, the system of the present invention works in combination with users, servers, and terminals to provide accurate and efficient communication across language barriers. By utilizing an emotion recognition engine and machine learning algorithms, the tone and emotion of the message are also reflected appropriately.

[0333] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0334] Step 1:

[0335] A user creates a document and submits it to the server.

[0336] Specifically, the user (school or educational institution) creates a letter or contact list in Japanese. The created document is sent to the server via the API endpoint.

[0337] Input: Document written in Japanese

[0338] Output: The document sent to the server

[0339] Step 2:

[0340] The server receives and parses the document.

[0341] Specifically, the server analyzes the received document using a natural language processing library (e.g., nltk, spaCy), which includes tokenization, syntactic analysis, and sentiment analysis.

[0342] Input: Received Japanese document

[0343] Output: Analysis results (tokenized data, syntactic information, sentiment analysis results)

[0344] Step 3:

[0345] The server translates the received document as needed.

[0346] Specifically, the server calls a translation API (e.g., Google Translate API) based on the analysis results to translate the document into the target language, using an emotion recognition engine to analyze the emotion of the document and adjust the appropriate translation tone.

[0347] Input: Analysis results, Japanese document

[0348] Output: Document translated into target language

[0349] Step 4:

[0350] The server uses an emotion engine to analyze the sentiment of the document and adjust the translation tone.

[0351] Specifically, the server uses an emotion recognition engine (e.g., OpenAI GPT model) to analyze the emotions in the document and adjust the translation tone based on the results.

[0352] Input: translated document, sentiment analysis results

[0353] Output: A translated document with adjusted tone

[0354] Step 5:

[0355] The server sends the translation results to the parent's device.

[0356] Specifically, the server converts the translated document into packets and sends them to the parent's device, which then interprets the received packets and displays them on the screen.

[0357] Input: translated document

[0358] Output: Document sent to parent's device

[0359] Step 6:

[0360] Parents review the documents they receive.

[0361] Specifically, the parent's terminal receives packets from the server and displays the document.

[0362] Input: Document sent to parent's device

[0363] Output: Document that parents can view on the screen

[0364] Step 7:

[0365] The parent creates a reply message and sends it to the server.

[0366] Specifically, the parent composes a reply message on the device, types it in their native language, and sends the message to the server.

[0367] Input: A message in your native language created by the parent

[0368] Output: Reply message sent to the server

[0369] Step 8:

[0370] The server translates messages from parents and sends them to the educational institution.

[0371] Specifically, the server receives messages from parents, calls a translation API to translate the messages into Japanese, and then sends the translation results to the educational institution.

[0372] Input: Parental Message

[0373] Output: Japanese translated message

[0374] Step 9:

[0375] The educational institution will review and respond to the message.

[0376] Specifically, the educational institution checks the message received from the server, creates a reply message in Japanese if necessary, and sends the created message to the server.

[0377] Input: Japanese translated message

[0378] Output: Reply message written in Japanese

[0379] Step 10:

[0380] The server translates the response from the educational institution and sends it to the parent.

[0381] Specifically, the server receives the reply from the educational institution, translates the message into the parent's native language using a translation API, converts the translated result into a packet format, and sends it to the parent's device.

[0382] Input: Reply message from educational institution

[0383] Output: Reply message translated into the parent's native language

[0384] Through the above processing steps, the system can provide accurate and efficient communication across language barriers.

[0385] (Application example 2)

[0386] 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."

[0387] Language barriers exist in communication between schools or educational institutions and parents, or between security services and multinational clients, resulting in problems with information not being transmitted accurately and promptly. Misunderstandings and mistranslations are particularly likely in emergencies or when emotions are strong, posing a risk of reduced credibility and adversely affecting actual crisis response. The present invention aims to solve these problems and provide smooth and accurate communication across language barriers.

[0388] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving a document sent from a school or educational institution; means for translating the received document into a target language; means for transmitting the translation result to the parent's terminal; means for detecting specific words or phrases and obtaining corresponding explanatory text; means for translating the obtained explanatory text into the target language and transmitting the translation result together with the explanatory text to the parent's terminal; means for receiving a message from the parent and translating it into the language of the educational institution; means for transmitting the translated message to the educational institution; means for receiving a reply message from the educational institution and translating it into the parent's target language; and means for analyzing the emotion contained in the message during translation and adjusting the translation tone accordingly. This allows smooth and accurate communication between the two parties, and enables information to be communicated in an appropriate tone, especially in emergencies when emotions are strongly expressed.

[0389] "Document receiving means" refers to a means for receiving documents sent from a school, educational institution, or security service.

[0390] A "translation means" is a means for translating a received document into a target language.

[0391] "Transmission means" refers to the means for transmitting the translated document to the parent or client's terminal.

[0392] A "specific word detection means" is a means for detecting specific words or phrases within a document.

[0393] The "explanation acquisition means" is a means for acquiring an explanation corresponding to a detected specific word or phrase from a database.

[0394] "Sentiment analysis means" is a means for analyzing the emotions contained in a message and adjusting the translation tone according to the results.

[0395] The "means for receiving a parental message" is a means for receiving a message from a parent.

[0396] "Educational institution language translation means" refers to a means for translating messages received from parents into the language of the educational institution.

[0397] The "means for receiving a reply message" is a means for receiving a reply message from an educational institution.

[0398] "Machine learning algorithms" are algorithms used to improve translation accuracy.

[0399] The "translation tone adjustment means" is a means for adjusting the tone of a translated message based on the results of sentiment analysis.

[0400] To implement this invention, a system is constructed that links multiple terminals and a server owned by schools, educational institutions, parents, and security services. The program processing procedures of this system and the hardware and software used are described in detail below.

[0401] System Overview:

[0402] The system consists of various devices (educational institution devices, parent devices, and security service devices) and a server. The server is equipped with a document receiving means, a translation means, a sending means, a specific word detection means, a description acquisition means, a sentiment analysis means, a parent message receiving means, an educational institution language translation means, and a reply message receiving means.

[0403] Hardware and software used:

[0404] Hardware: smartphones, laptops, servers, etc.

[0405] Software: Python 3.x, Googletrans library (translation function), TextBlob (sentiment analysis), REST API (HTTP communication).

[0406] Internet connection: Required to enable system-wide communication.

[0407] Data processing and calculation details:

[0408] 1. Document Receipt Method:

[0409] The server receives documents sent by educational institutions and security services, including event notifications and emergency reports.

[0410] 2. Translation Methods:

[0411] Translates the received document into the target language using the Python GoogleTrans library. If the document is in Japanese, it will be translated into a specific language (e.g., English or Chinese).

[0412] 3. Sentiment analysis tools:

[0413] Using Natural Language Processing (NLP) techniques, the TextBlob library analyzes the sentiment contained in a document and adjusts the translation tone accordingly.

[0414] 4. Specific word detection method:

[0415] The server detects specific words and phrases and retrieves their corresponding descriptions from a database, in accordance with US standards.

[0416] 5. Description acquisition method:

[0417] For each detected word or phrase, an explanation is retrieved from the database and translated into the target language.

[0418] 6. Means of transmission:

[0419] The translated document and explanation are sent to the parent or client's device. Communication is carried out using a REST API.

[0420] 7. Parental Message Reception Method:

[0421] Receive messages from parents and clients, translate them into the language of the educational institution or security service, and send reply messages in the same way.

[0422] Examples and prompts:

[0423] For example, if a client sends you a message saying, "My security alarm went off unexpectedly," use the following prompt to apply the appropriate translation tone:

[0424] Translate the following English emergency message into Japanese with the appropriate tone: "My security alarm went off unexpectedly." Ensure the translated message reflects a serious tone appropriate for an emergency situation.

[0425] In this way, the server analyzes the sentiment of the received message and sends a translated message with the appropriate tone to educational institutions and security services, enabling accurate and prompt communication across language barriers.

[0426] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0427] Step 1:

[0428] The server receives documents sent from schools, educational institutions, or security services. As input, document data is sent to the server. As output, the received document data is stored within the server. Specifically, the server receives the document data over the network using the standard HTTP protocol.

[0429] Step 2:

[0430] The server translates the received document into the target language. As input, the received document data and target language information are provided to the server. As output, a document translated into the target language is generated. Specifically, the server translates the document using Python's Google Trans library.

[0431] Step 3:

[0432] The server analyzes the sentiment of the document and adjusts the translation tone accordingly. As input, the translated document is fed into the server. As output, a translated document with an adjusted tone is generated. Specifically, the server uses the TextBlob library to analyze the sentiment of the document and apply a tone adjustment algorithm.

[0433] Step 4:

[0434] The server detects specific words or phrases in a document and retrieves their corresponding descriptions from a database. The translated document is entered into the server as input. The corresponding descriptions are obtained as output. Specifically, the server uses Natural Language Processing (NLP) techniques to detect specific words and executes a database query.

[0435] Step 5:

[0436] The server translates the acquired description into the target language. The acquired description is input to the server as input. The translated description into the target language is generated as output. Specifically, the server again translates the description using the GoogleTrans library.

[0437] Step 6:

[0438] The server sends the translated document and explanatory text to the parent or client device. As input, the adjusted translated document and explanatory text are entered into the server. As output, the translated document and explanatory text are sent to the parent or client device. Specifically, the server sends data using a REST API.

[0439] Step 7:

[0440] The server receives messages from guardians and clients. As input, messages sent from guardians and clients are input to the server. As output, the received messages are stored in the server. In concrete terms, the server receives and stores messages via the network.

[0441] Step 8:

[0442] The server translates the received message into the language of the educational institution or security service. As input, the server is provided with the received message and information about the target language. As output, the translated message in the target language is generated. Specifically, the server uses the GoogleTrans library to translate the message.

[0443] Step 9:

[0444] The server sends the translated message to the educational institution or security service. As input, the translated message is entered into the server. As output, the translated message is sent to the educational institution or security service's terminal. In concrete terms, the server sends data using a REST API.

[0445] Step 10:

[0446] The server receives a reply message from the educational institution or security service. As input, the reply message sent from the educational institution or security service is input to the server. As output, the received reply message is stored in the server. In concrete terms, the server receives the reply message via the network.

[0447] Step 11:

[0448] The server translates the received reply message into the language of the parent or client. As input, the received reply message and target language information are provided to the server. As output, a reply message translated into the target language is generated. Specifically, the server translates the reply message using the GoogleTrans library.

[0449] Step 12:

[0450] The server sends the translated reply message to the parent or client's device. The translated reply message is entered into the server as input. The translated reply message is sent to the parent or client's device as output. Specifically, the server sends data using a REST API.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] [Second embodiment]

[0455] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0456] 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.

[0457] 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).

[0458] 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.

[0459] 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.

[0460] 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).

[0461] 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. 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.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0466] 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."

[0467] The present invention is a translation system for appropriately conveying documents from schools and educational institutions to parents, and realizes smooth communication beyond language barriers. The system of the present invention is specifically implemented as follows.

[0468] First, in terms of the system configuration, documents created by users (schools and educational institutions) are sent to a server via the Internet. The server then analyzes the contents of the document and translates it as necessary.

[0469] 1. Automatic translation of letters and contacts

[0470] The user (school or educational institution) creates a document in Japanese and sends it to the server. The server analyzes the received document and translates it into the target language using natural language processing technology. The translated document is then sent from the server to the parent's device, who can receive the document in a language they can understand.

[0471] Examples:

[0472] A user (school) creates and sends a notification saying, "There will be a sports day next Monday. Please bring your sportswear and water bottles."

[0473] The server receives this document and translates it into "Next Monday, we will have a sports day. Please bring sportswear and a water bottle."

[0474] The translated document will be sent to the parent's device.

[0475] 2. Explanation of specific words and phrases

[0476] When analyzing the content of the document, the server detects certain words and phrases. For these words and phrases, the server retrieves corresponding explanations from a database and provides them to the parent. The explanations are then translated into the target language and sent as an attachment to the original document.

[0477] Examples:

[0478] The server detects the word "sports day" and retrieves the description "sports day is a sporting event held at school" from the database.

[0479] The server adds the explanation "Sports day is a school event where students participate in various sports activities" to the document and sends it along with the translated document.

[0480] 3. Communication between both parties

[0481] When a parent sends a message in their native language, such as English, the message is received by the server. The server translates the received message into Japanese and sends it to the educational institution. The educational institution's reply is also sent to the server, where it is translated into the target language and then sent to the parent's device.

[0482] Examples:

[0483] A parent sends a message saying, "My child has a doctor's appointment on Friday. Will there be any important activities that day?"

[0484] The server translates this to "My child has a hospital appointment on Friday. Do you have any important activities on that day?" and sends it to the institution.

[0485] The educational institution replies, "There are no important activities on that day."

[0486] The server translates this as "There are no particularly important activities on that day" and sends it to the parent's device.

[0487] This system will ensure smooth communication between schools and educational institutions and foreign parents, and will ensure that necessary information is accurately conveyed to parents who do not understand Japanese.

[0488] The processing flow will be explained below.

[0489] Step 1:

[0490] A user (such as a school or educational institution) creates a document and sends it to the server. The document can include, for example, a letter or a contact list.

[0491] Step 2:

[0492] The server analyzes the Japanese document received from the user, taking in the document's contents in text format and extracting specific information from it.

[0493] Step 3:

[0494] The server translates the document content into the target language. The server uses natural language processing (NLP) technology to analyze the entire document and translate it appropriately. For example, it calls a translation API to translate "There will be a sports day next Monday." into "Next Monday, we will have a sports day."

[0495] Step 4:

[0496] The server detects specific words and phrases within a document, for example, event names such as "athletic meet" or "athletic festival" or special words.

[0497] Step 5:

[0498] The server retrieves from the database the description that corresponds to the detected word or phrase, for example, "Undokai is a sports event held at Japanese schools."

[0499] Step 6:

[0500] The server translates the retrieved description into the target language. For example, the description could be translated as "Sports day is an event held at Japanese schools where students participate in various sports activities."

[0501] Step 7:

[0502] The server adds an explanatory text to the translated document, for example, "Next Monday, we will have a sports day. (Sports day is an event held at Japanese schools where students participate in various sports activities)."

[0503] Step 8:

[0504] The server sends the translated document to the parent's device, allowing the parent to receive information in their own language.

[0505] Step 9:

[0506] The user (parent) enters questions or comments in their native language and sends them to the server from their device. For example, they send a message like, "My child has a doctor's appointment on Friday. Will there be any important activities that day?"

[0507] Step 10:

[0508] The server receives the message from the parent and translates it into Japanese, saying, "My child has a hospital appointment on Friday. Do you have any important activities on that day?"

[0509] Step 11:

[0510] The server sends the translated message to the educational institution, which can then understand the parent's question in Japanese.

[0511] Step 12:

[0512] The user (educational institution) enters a reply message in Japanese and sends it to the server. For example, the reply may say, "There are no particularly important activities on that day."

[0513] Step 13:

[0514] The server receives the reply message from the educational institution and translates it into the parent's native language, stating, "There are no particularly important activities on that day."

[0515] Step 14:

[0516] The server then sends the translated reply message to the parent's device, allowing the parent to receive a reply in their own language and respond appropriately.

[0517] As described above, each step of the process is carried out sequentially, and smooth and accurate communication between users is achieved through this series of processes.

[0518] Example 1

[0519] 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."

[0520] Traditionally, communication between schools and educational institutions and parents has often been hindered by language barriers. Important notices and event notifications may not be conveyed accurately to parents, leading to misunderstandings and inconvenience. Furthermore, questions and messages from parents may not be translated properly, making smooth communication with educational institutions difficult.

[0521] 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.

[0522] In this invention, the server includes means for receiving documents sent from a school or educational institution, means for analyzing the received documents, means for translating the received documents into a target language, means for sending the translation results to the parent's terminal, means for detecting specific words or phrases and obtaining corresponding explanatory text, means for translating the obtained explanatory text into the target language and sending the translation results together with the explanatory text to the parent's terminal, means for receiving messages from the parent and translating them into the language of the educational institution, means for sending the translated messages to the educational institution, means for receiving reply messages from the educational institution and translating them into the parent's target language, means for sending the translated reply messages to the parent's terminal, means for presenting received documents in a form that is easy for users to understand, and means for translating messages sent by users into the original language of the documents, thereby enabling smooth and accurate communication that transcends language barriers.

[0523] "Schools and educational institutions" are organizations and facilities established for the purpose of education, including daycare centers, kindergartens, elementary schools, junior high schools, high schools, universities, and vocational schools.

[0524] A "document" is written information or instructions in text form that is produced for the purpose of communication or record-keeping.

[0525] "Receiving" is the act of receiving information or data sent from another party.

[0526] A "server" is a central computer that processes data and provides services to other computers and devices over a network.

[0527] "Analysis" is the act of investigating and evaluating data and information in detail to clarify their meaning and structure.

[0528] "Target language" is the language into which the original document is to be translated in a translation task.

[0529] "Translation" is the act of converting content written in one language into another language.

[0530] A "terminal" is a device connected to a computer system that can be operated by a user.

[0531] "Specific words or phrases" refers to certain keywords or expressions that are important or frequently appear in a document or message.

[0532] An "explanatory text" is a piece of text that explains the meaning and usage of a particular word or phrase.

[0533] A "message" is a document or word sent between users to convey information.

[0534] A "database" is a collection of information that has been organized and stored so that the data can be efficiently searched and used.

[0535] "Machine learning" is a technology that uses algorithms and statistical models to allow computers to learn from experience and perform data analysis and predictions.

[0536] MODE FOR CARRYING OUT THE INVENTION

[0537] This invention is a translation system for properly conveying documents sent from schools and educational institutions to parents, realizing smooth communication beyond language barriers. This system is specifically implemented through the process of receiving, analyzing, translating, and transmitting data between a server, terminals, and users.

[0538] First, a user (school or educational institution) creates a document in Japanese and sends it to a server via the Internet from their terminal. The user can use a standard computer or mobile device to send the document using a web interface or a dedicated application.

[0539] The server parses the received document using a natural language processing library (e.g., spaCy or NLTK) to extract important keywords and phrases from the document.

[0540] The server then calls a translation service such as Google Translate API or DeepL to translate the analyzed document into the target language, and the translated document is sent to the parent's device, allowing the parent to receive the document in a language they understand.

[0541] For example, if a user creates a document stating, "We have a sports day next Monday. Please bring sportswear and a water bottle.", the server translates this into "Next Monday, we will have a sports day. Please bring sportswear and a water bottle." and sends it to the parent's device.

[0542] The server also detects specific words and phrases in the document and retrieves corresponding explanations from the database. The explanations are also translated into the target language and added to the original document before being sent to the parent's device. This process allows the parent to gain a deeper understanding of the document's contents.

[0543] For example, if the server detects the word "sports day," it retrieves the explanation "sports day is a school event where students participate in various sports activities" from the database, translates it into "sports day is a school event where students participate in various sports activities," and adds it to the document. The document with this additional explanation is then sent to the parent's device.

[0544] Furthermore, messages from parents are also exchanged via the server. When parents send messages in their native language, such as English, the server translates them into Japanese and sends them to the educational institution. Responses from the educational institution are also sent to the server, which translates them into the parent's target language. This ensures smooth two-way communication between the educational institution and parents.

[0545] Examples of specific prompts include:

[0546] "Please translate the Japanese sentence, 'There will be a sports day next Monday. Please bring your sportswear and water bottles.' into English."

[0547] "Write a sentence explaining the meaning of the word 'sports day'."

[0548] "Please translate the following English message into Japanese: 'My child has a doctor's appointment on Friday. Will there be any important activities that day?'"

[0549] This makes it possible to obtain appropriate translations and descriptions from generative AI models.

[0550] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0551] Step 1:

[0552] A user (school or educational institution) creates a document and sends it to the server from their device.

[0553] Specifically, a user uses word processing software or a dedicated application to create a document, such as "There will be a sports day next Monday. Please bring your sportswear and water bottle." The document is then uploaded to a server from the terminal via the Internet. The input is the document, and the output is the uploading of the document to the server.

[0554] Step 2:

[0555] The server parses the received document.

[0556] The server begins analyzing the received document using a natural language processing library (e.g., spaCy or NLTK). Specifically, it extracts important keywords and phrases (e.g., "sports day") from the document. The input is the received document, and the output is the extracted keywords and phrases.

[0557] Step 3:

[0558] The server translates the document into the target language.

[0559] The server calls a translation service such as Google Translate API or DeepL and translates the parsed document into the target language. For example, it translates "We will have a sports day next Monday. Please bring sportswear and a water bottle." into "Next Monday, we will have a sports day. Please bring sportswear and a water bottle." The input is the parsed document, and the output is the translated document.

[0560] Step 4:

[0561] The server sends the translation results to the parent's device.

[0562] The server sends the translated document to the designated parent's device. Specifically, the translated document is delivered to the parent via email or a dedicated application. The input is the translated document, and the output is the document sent to the parent's device.

[0563] Step 5:

[0564] The server detects specific words or phrases in the document and retrieves their corresponding descriptions from a database.

[0565] The server detects specific words or phrases (e.g., "sports day") from the parsed document and retrieves the description "sports day is a sporting event held at school" from the database. The input is the parsed document, and the output is the retrieved description.

[0566] Step 6:

[0567] The server translates the description into the target language and adds it to the original document.

[0568] The server translates the retrieved description into the target language using the Google Translate API or DeepL. Specifically, it translates "Sports day is a school event where students participate in various sports activities" into "Sports day is a school event where students participate in various sports activities" and adds it to the translated document. The input is the retrieved description, and the output is the translated document with the description added.

[0569] Step 7:

[0570] The server sends the translated document with explanatory text to the parent's device.

[0571] The server adds explanatory text to the translated document and sends it to the parent's device. Specifically, it presents it in a format that is easy for the parent to understand. The input is the translated document with explanatory text added, and the output is the translated document with explanatory text sent to the parent's device.

[0572] Step 8:

[0573] The server receives messages from parents and translates them into the institution's language.

[0574] For example, a parent sends a message from their device to the server, such as "My child has a doctor's appointment on Friday. Will there be any important activities that day?" The server translates this to "My child has a doctor's appointment on Friday. Will there be any important activities that day?" The input is the message from the parent, and the output is the message translated into the language of the educational institution.

[0575] Step 9:

[0576] The server sends the translated message to the institution.

[0577] The server sends the translated message to the educational institution's terminal. The specific operation is to provide information in a form that the educational institution can easily understand. The input is the translated message, and the output is the message sent to the educational institution's terminal.

[0578] Step 10:

[0579] The server receives the reply message from the educational institution and translates it into the parent's target language.

[0580] The institution sends a reply to the server, for example, "There are no particularly important activities on that day." The server translates this to "There are no particularly important activities on that day." The input is the reply message from the institution, and the output is the translated reply message.

[0581] Step 11:

[0582] The server sends the translated reply message to the parent's device.

[0583] The server sends the translated reply message to the parent's device. Specifically, the server provides the translated reply message in a format that the parent can easily check. The input is the translated reply message, and the output is the message sent to the parent's device.

[0584] (Application example 1)

[0585] 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."

[0586] The present invention relates to a system for accurately and quickly transmitting order information between customers who speak different languages ​​and restaurants, thereby eliminating language barriers in food delivery services.

[0587] Specifically, there is a need for a means that allows customers to place orders in their own language, ensures that the restaurant accurately understands the content of the orders, and also allows the restaurant to properly convey order confirmation messages to customers.

[0588] 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.

[0589] In this invention, the server includes means for receiving a document sent from a school or educational institution, means for translating the received document into a target language, means for transmitting the translation result to the parent's terminal, means for detecting specific words or phrases and obtaining corresponding explanatory text, means for translating the obtained explanatory text into the target language and transmitting the translation result together with the explanatory text to the parent's terminal, means for receiving a message from the parent and translating it into the language of the educational institution, means for transmitting the translated message to the educational institution, means for receiving a reply message from the educational institution and translating it into the parent's target language, means for transmitting the translated reply message to the parent's terminal, means for receiving order information from a customer and translating it into the restaurant's language, means for transmitting the translated order information to the restaurant, means for receiving order confirmation information from the restaurant and translating it into the customer's target language, and means for transmitting the translated order confirmation information to the customer's terminal. This enables order information to be communicated accurately and quickly between customers and restaurants, eliminating language barriers.

[0590] "Schools and educational institutions" refers to organizations and facilities for carrying out educational activities, such as elementary schools, junior high schools, high schools, universities, and vocational schools.

[0591] "Document" means any paper or digital file containing textual information, including notices, correspondence, reports, etc.

[0592] "Translation means" refers to the methods and techniques used to convert a document written in one language into another language.

[0593] "Parental devices" refers to electronic devices such as computers, smartphones, and tablets used by parents of children or students.

[0594] "Specific words and phrases" refer to specific keywords and expressions in a document, and are words and phrases that contain important information that requires explanation.

[0595] An "explanation" is text information that provides detailed explanations of the meaning and background of specific words or phrases.

[0596] "Customer" means the person or entity placing an order to purchase or use a service or product.

[0597] "Order Information" means the requests and details provided by Customer when purchasing goods or services.

[0598] "Food and beverage establishments" refers to establishments and restaurants that serve food and beverages.

[0599] "Order confirmation information" is information that a restaurant sends back after confirming the order details received from a customer.

[0600] System Configuration

[0601] The present invention is a translation system for accurately and quickly communicating order information between customers and restaurants, and includes the following components: a server, a customer terminal, and a restaurant terminal, all of which work in conjunction with one another.

[0602] Hardware and Software

[0603] The hardware used is as follows:

[0604] Server (e.g., Amazon Web Services EC2 instance)

[0605] Customer and restaurant devices (e.g., smartphones, tablets, computers)

[0606] The software used is as follows:

[0607] Server-side framework: Flask (a Python micro web framework)

[0608] Translation engine: Google Translate API

[0609] Natural language processing techniques and machine learning algorithms: Machine learning models (e.g., BERT, GPT-3)

[0610] Data processing and calculation flow

[0611] The server processes the data according to the following procedure.

[0612] 1. Document Receipt and Translation:

[0613] The server receives the order information entered on the customer's device. For example, the sentence "I would like to order a large pizza and a Coke" is sent.

[0614] The server uses the Google Translate API to translate this order information from the customer's native language into the restaurant's language.

[0615] 2. Sending Order Information:

[0616] When the translation result is "I would like to order a large pizza and a Coke," it is sent to the restaurant's terminal.

[0617] 3. Receiving and translating order confirmation messages:

[0618] Receive order confirmation from the restaurant terminal, such as "We have received your order for a large pizza and a Coke."

[0619] The server uses the Google Translate API to translate this confirmation information from the restaurant's language into the customer's native language.

[0620] 4. Sending confirmation information:

[0621] Once the translation result is "Your order for a large pizza and a Coke has been accepted," it is sent to the customer's terminal.

[0622] Specific examples

[0623] For example, if an English-speaking customer places an order by saying, "I would like to order a large pizza and a Coke," this order information is translated and transmitted as follows:

[0624] "I would like to order a large pizza and a Coke."

[0625] "I'd like to order a large pizza and a Coke."

[0626] Similarly, a confirmation message from a restaurant saying "We have received your order for a large pizza and a Coke" would be translated and sent as follows:

[0627] "Your order for a large pizza and a Coke has been accepted."

[0628] Translated into:English "We have received your order for a large pizza and a Coke."

[0629] Prompt Sentence Examples

[0630] English prompt:

[0631] "I would like to order a large pizza and a Coke."

[0632] Translated Japanese:

[0633] "I'd like to order a large pizza and a Coke."

[0634] In this way, the system of the present invention allows customers to input orders in their native language and accurately translates them into the restaurant's language, enabling smooth communication of order information across language barriers. By using the Google Translate API as the translation engine and performing detailed data processing on the server side, accurate and prompt communication between customers and restaurants is achieved.

[0635] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0636] Step 1:

[0637] A user inputs order information into a terminal. The input information is text data such as "I would like to order a large pizza and a Coke." This information is in the customer's native language and is sent from the terminal to the server.

[0638] Step 2:

[0639] The server receives the order information from the customer's device. The input is in the customer's native language (e.g., "I would like to order a large pizza and a Coke."), and this information is converted for processing within the server.

[0640] Step 3:

[0641] The server translates the order information using the Google Translate API. The input is the order information in English, the customer's native language, and the output is the order information translated into the restaurant's language (e.g., "I'd like to order a large pizza and a Coke."). The server accesses the API, converts the text data, and obtains the translation results.

[0642] Step 4:

[0643] The server sends the translated order information to the restaurant's terminal. The input is the translated order information (e.g., "I'd like to order a large pizza and a Coke."), and the output is the data sent to the restaurant's terminal. The server sends the information using a network protocol.

[0644] Step 5:

[0645] The restaurant terminal receives the order information and generates a confirmation message. The input is the translated order information received from the server, and the output is a confirmation message written in the restaurant's language (e.g., "We have received your order for a large pizza and a Coke."). The restaurant terminal may automatically generate this message, or a staff member may type it in.

[0646] Step 6:

[0647] The server receives an order confirmation message from the restaurant terminal. The input is the confirmation message in the restaurant's language (e.g., "We have received your order for a large pizza and a Coke."), and the output is retained for processing within the server.

[0648] Step 7:

[0649] The server translates the confirmation message using the Google Translate API. The input is the confirmation message in the restaurant's language, and the output is the confirmation message translated into the customer's native language (e.g., "Your order for a large pizza and a Coke has been accepted."). The server accesses the API, converts the text data, and obtains the translation result.

[0650] Step 8:

[0651] The server sends a translated confirmation message to the customer's device. The input is the translated confirmation message (e.g., "Your order for a large pizza and a Coke has been accepted.") and the output is the data sent to the customer's device. The server sends the information using a network protocol.

[0652] This series of processes enables smooth communication between customers who speak different languages ​​and restaurants, allowing for accurate and prompt ordering and confirmation.

[0653] 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.

[0654] This invention is a system for facilitating communication between schools and educational institutions and parents, and in particular provides technology for accurately conveying information across language barriers. A key feature of this invention is that it recognizes the user's emotions by combining an emotion engine and applies a translation tone accordingly. The processing flow of this system will now be described in detail.

[0655] 1. Automatic translation of letters and contacts

[0656] A user (school or educational institution) creates a document in Japanese and sends it to the server. The server analyzes the received document and uses natural language processing technology to translate the content into the target language. The translated document is then sent from the server to the parent's device, who can receive the document in a language they can understand. The emotion engine can also analyze the emotion contained in the document and adjust the translation tone accordingly.

[0657] Examples:

[0658] A user (school) creates and sends a notification saying, "There will be a sports day next Monday. Please bring your sportswear and water bottles."

[0659] The server receives this document and translates it into "Next Monday, we will have a sports day. Please bring sportswear and a water bottle."

[0660] The emotion engine recognizes the tone of the document as "cheerful" and reflects this in the translation.

[0661] The translated document will be sent to the parent's device.

[0662] 2. Explanation of specific words and phrases

[0663] When analyzing the content of a document, the server detects certain words and phrases. For these words and phrases, the server retrieves corresponding explanations from a database and provides them to parents as part of the translation. After the explanations are translated into the target language, they are added to the original document.

[0664] Examples:

[0665] The server detects the word "sports day" and retrieves the description "sports day is a sports event held at Japanese schools" from the database.

[0666] The server translates the description into "Sports day is an event held at Japanese schools where students participate in various sports activities."

[0667] The server responds to the document with the following: "Next Monday, we will have a sports day. Please bring sportswear and a water bottle. (Sports day is an event held at Japanese schools where students participate in various sports activities)."

[0668] 3. Communication between both parties

[0669] When a parent sends a message in their native language, the message is received by the server. The server translates the received message into Japanese and sends it to the educational institution. Reply messages are similarly translated and sent to the parent's device in their native language. The server uses an emotion engine to recognize the emotions contained in the parent's and educational institution's messages and applies the corresponding translation tone.

[0670] Examples:

[0671] A parent sends a message saying, "My child has a doctor's appointment on Friday. Will there be any important activities that day?"

[0672] The server translates this to "My child has a hospital appointment on Friday. Do you have any important activities on that day?" and sends it to the institution.

[0673] The emotion engine recognizes the emotion of the question as "concern" and adjusts the tone of the reply appropriately.

[0674] The educational institution replies, "There are no important activities on that day."

[0675] The server translates this as "There are no particularly important activities on that day" and sends it to the parent's device.

[0676] This invention enables smooth and accurate communication that transcends language barriers, deepening understanding between parents and educational institutions. By utilizing an emotion engine, the content of messages is conveyed appropriately, making dialogue even smoother.

[0677] The processing flow will be explained below.

[0678] Step 1:

[0679] A user (school or educational institution) creates a document and sends it to the server. For example, a user creates a notification saying, "We have a sports day next Monday. Please bring your sports clothes and water bottles."

[0680] Step 2:

[0681] The server analyzes the Japanese document received from the user, captures the document content in text format, and prepares it for translation.

[0682] Step 3:

[0683] The server uses natural language processing technology to translate the content of the document into the target language. For example, when translating a Japanese document into English, the server converts it into "Next Monday, we will have a sports day. Please bring sportswear and a water bottle."

[0684] Step 4:

[0685] The server detects specific words and phrases within a document, for example recognizing words like "sports day" and "sportswear."

[0686] Step 5:

[0687] The server retrieves from the database the description that corresponds to the detected word or phrase, for example, "Undokai is a sports event held at Japanese schools."

[0688] Step 6:

[0689] The server translates the obtained description into the target language. For example, "Sports day is an event held at Japanese schools where students participate in various sports activities."

[0690] Step 7:

[0691] The server generates a document with the translated explanation added. For example, the server adds an explanation to the translated document, such as "Next Monday, we will have a sports day. Please bring sportswear and a water bottle. (Sports day is an event held at Japanese schools where students participate in various sports activities)."

[0692] Step 8:

[0693] The server uses an emotion engine to analyze the emotion contained in the document, for example, recognizing the document's tone as "cheerful."

[0694] Step 9:

[0695] The server adjusts the translation tone based on the emotion recognition results. For example, it sets an appropriate translation tone and provides the document to the parent as a cheerful document.

[0696] Step 10:

[0697] The server sends the translated document to the parent's device, where the parent can receive the document in a language they understand.

[0698] Step 11:

[0699] The user (parent) creates a message in their native language and sends it to the server from their device. For example, they send a question such as, "My child has a doctor's appointment on Friday. Will there be any important activities that day?"

[0700] Step 12:

[0701] The server receives the message from the parent and uses natural language processing technology to translate it into Japanese, for example, "My child has a hospital appointment on Friday. Are there any important activities on that day?"

[0702] Step 13:

[0703] The server sends the translated message to the educational institution, which can then receive the parent's questions in Japanese.

[0704] Step 14:

[0705] The server uses an emotion engine to analyze the emotion contained in the parent's message. For example, the emotion engine recognizes "worry."

[0706] Step 15:

[0707] The user (educational institution) creates a reply message in Japanese and sends it to the server. For example, they reply, "There are no particularly important activities on that day."

[0708] Step 16:

[0709] The server receives the reply message from the educational institution and translates it into the target language, for example, "There are no particularly important activities on that day."

[0710] Step 17:

[0711] The server uses an emotion engine to analyze the emotion contained in the educational institution's reply message, for example recognizing "a sense of relief."

[0712] Step 18:

[0713] The server adjusts the translation tone based on the emotion recognition result, and applies an appropriate tone to provide the translated message to the parent.

[0714] Step 19:

[0715] The server sends the translated reply message to the parent's device, who can then receive the reply in their own language through the device.

[0716] As described above, processing is performed at each step, ensuring smooth communication between users.

[0717] Example 2

[0718] 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."

[0719] Language barriers can be an obstacle when communicating between schools and parents, making it difficult to convey information accurately. Furthermore, the quality of communication can decline if emotional nuances are not conveyed properly when documents are translated. Furthermore, replies and inquiries from parents cannot be handled quickly and accurately.

[0720] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0721] In this invention, the server includes a means for receiving a document sent from a school or educational institution, a means for translating the received document into a target language, and a means for analyzing the emotion of the document using an emotion recognition engine and adjusting the translation tone. This enables accurate communication that transcends language barriers and incorporates emotional nuances. The server also includes a means for retrieving specific words or phrases from a database and translating the corresponding explanatory text into the target language, a means for translating the retrieved explanatory text into the target language and sending the translation result to the parent's terminal, a means for receiving a message from the parent and translating it into the language of the educational institution, a means for sending the translated message to the educational institution, a means for receiving a reply message from the educational institution and translating it into the parent's target language, and a means for sending the translated reply message to the parent's terminal. This system enables rapid and accurate two-way communication.

[0722] "Schools and educational institutions" is a general term for organizations that provide education to children and students.

[0723] "Document" refers to documents or text data that express information using characters and diagrams.

[0724] A "server" refers to a computer system that provides services and data in response to client requests over a network.

[0725] "Target language" refers to a language other than Japanese that is designated as the target language for translation.

[0726] "Translation means" refers to a function that converts a document written in one language into another language.

[0727] An "emotion recognition engine" refers to a software system that analyzes the emotions and tone in a document and adjusts the appropriate translation tone based on the results.

[0728] A "terminal" refers to a device, such as a PC or smartphone, that allows a user to receive or send information over a network.

[0729] "Specific words and phrases" refer to words or expressions that are considered particularly important in the document to be translated.

[0730] A "database" refers to a system that organizes and stores specific data so that it can be easily searched and retrieved.

[0731] "Parent" refers to a parent or legal guardian of a child or student, or any other adult who is required to communicate with an educational institution.

[0732] A "machine learning algorithm" refers to a computational method that learns patterns from data and uses those patterns to predict or classify new data.

[0733] The present invention provides a system for facilitating communication between schools or educational institutions and parents, and in particular provides technology for accurately transmitting information across language barriers. Specific embodiments of this system are described in detail below.

[0734] Hardware and software used

[0735] Server: Use a high-performance network server, such as an Apache or Nginx server.

[0736] Device: A device used by users and parents. This primarily refers to devices that can connect to the internet, such as smartphones, tablets, and PCs.

[0737] Translation API: Used to automatically translate documents, specifically Google Translate API and Microsoft Translator.

[0738] Natural language processing libraries: Used for document analysis and sentiment recognition. Examples include the nltk library and spaCy.

[0739] Machine learning models, such as OpenAI's GPT model, are used to analyze the sentiment of a document and adjust the translation tone.

[0740] Explanation of system processing

[0741] 1. Sending documents

[0742] Users (schools and educational institutions) create letters and contacts in Japanese, and the documents are sent to the server via a specified API endpoint.

[0743] Example: A user creates a notification that says, "We have a sports day next Monday. Please bring your sports clothes and water bottle."

[0744] 2. Document analysis on the server

[0745] The server analyzes the received document, tokenizing it using a natural language processing library (e.g., nltk, spaCy) and performing syntax and sentiment analysis.

[0746] 3. Translation Processing

[0747] Based on the analysis results, the server identifies the target language and calls a translation API (e.g., Google Translate API) to translate the document into the target language, using an emotion recognition engine to analyze the sentiment of the document and adjust the appropriate translation tone.

[0748] Example: The server translates the sentence "There will be a sports day next Monday." into "Next Monday, we will have a sports day.", and the emotion engine determines that this sentence is "cheerful."

[0749] 4. Sending translation results

[0750] The server converts the translated text into packets and sends them to the parent's device, which interprets the received packets and displays them on the screen.

[0751] 5. Processing responses from parents

[0752] Parents can create a reply message in their own language and send it to the server. The server then translates the message and sends it to the educational institution. The educational institution's reply is also translated via the server and sent to the parent's device.

[0753] Examples of concrete examples and prompts

[0754] Example: A parent sends a message saying, "My child has a doctor's appointment on Friday. Will there be any important activities that day?" The server translates this to "My child has a doctor's appointment on Friday. Will there be any important activities that day?" and sends it to the educational institution. The educational institution replies, "There are no particularly important activities on that day," which the server translates to "There are no particularly important activities on that day" and sends it to the parent's device.

[0755] Example prompt sentence:

[0756] "We have a sports day next Monday. Please bring your sports clothes and water bottles."

[0757] "My child has a doctor's appointment on Friday. Will there be any important activities that day?"

[0758] In this way, the system of the present invention works in combination with users, servers, and terminals to provide accurate and efficient communication across language barriers. By utilizing an emotion recognition engine and machine learning algorithms, the tone and emotion of the message are also reflected appropriately.

[0759] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0760] Step 1:

[0761] A user creates a document and submits it to the server.

[0762] Specifically, the user (school or educational institution) creates a letter or contact list in Japanese. The created document is sent to the server via the API endpoint.

[0763] Input: Document written in Japanese

[0764] Output: The document sent to the server

[0765] Step 2:

[0766] The server receives and parses the document.

[0767] Specifically, the server analyzes the received document using a natural language processing library (e.g., nltk, spaCy), which includes tokenization, syntactic analysis, and sentiment analysis.

[0768] Input: Received Japanese document

[0769] Output: Analysis results (tokenized data, syntactic information, sentiment analysis results)

[0770] Step 3:

[0771] The server translates the received document as needed.

[0772] Specifically, the server calls a translation API (e.g., Google Translate API) based on the analysis results to translate the document into the target language, using an emotion recognition engine to analyze the emotion of the document and adjust the appropriate translation tone.

[0773] Input: Analysis results, Japanese document

[0774] Output: Document translated into target language

[0775] Step 4:

[0776] The server uses an emotion engine to analyze the sentiment of the document and adjust the translation tone.

[0777] Specifically, the server uses an emotion recognition engine (e.g., OpenAI GPT model) to analyze the emotions in the document and adjust the translation tone based on the results.

[0778] Input: translated document, sentiment analysis results

[0779] Output: A translated document with adjusted tone

[0780] Step 5:

[0781] The server sends the translation results to the parent's device.

[0782] Specifically, the server converts the translated document into packets and sends them to the parent's device, which then interprets the received packets and displays them on the screen.

[0783] Input: translated document

[0784] Output: Document sent to parent's device

[0785] Step 6:

[0786] Parents review the documents they receive.

[0787] Specifically, the parent's terminal receives packets from the server and displays the document.

[0788] Input: Document sent to parent's device

[0789] Output: Document that parents can view on the screen

[0790] Step 7:

[0791] The parent creates a reply message and sends it to the server.

[0792] Specifically, the parent composes a reply message on the device, types it in their native language, and sends the message to the server.

[0793] Input: A message in your native language created by the parent

[0794] Output: Reply message sent to the server

[0795] Step 8:

[0796] The server translates messages from parents and sends them to the educational institution.

[0797] Specifically, the server receives messages from parents, calls a translation API to translate the messages into Japanese, and then sends the translation results to the educational institution.

[0798] Input: Parental Message

[0799] Output: Japanese translated message

[0800] Step 9:

[0801] The educational institution will review and respond to the message.

[0802] Specifically, the educational institution checks the message received from the server, creates a reply message in Japanese if necessary, and sends the created message to the server.

[0803] Input: Japanese translated message

[0804] Output: Reply message written in Japanese

[0805] Step 10:

[0806] The server translates the response from the educational institution and sends it to the parent.

[0807] Specifically, the server receives the reply from the educational institution, translates the message into the parent's native language using a translation API, converts the translated result into a packet format, and sends it to the parent's device.

[0808] Input: Reply message from educational institution

[0809] Output: Reply message translated into the parent's native language

[0810] Through the above processing steps, the system can provide accurate and efficient communication across language barriers.

[0811] (Application example 2)

[0812] 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."

[0813] Language barriers exist in communication between schools or educational institutions and parents, or between security services and multinational clients, resulting in problems with information not being transmitted accurately and promptly. Misunderstandings and mistranslations are particularly likely in emergencies or when emotions are strong, posing a risk of reduced credibility and adversely affecting actual crisis response. The present invention aims to solve these problems and provide smooth and accurate communication across language barriers.

[0814] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving a document sent from a school or educational institution; means for translating the received document into a target language; means for transmitting the translation result to the parent's terminal; means for detecting specific words or phrases and obtaining corresponding explanatory text; means for translating the obtained explanatory text into the target language and transmitting the translation result together with the explanatory text to the parent's terminal; means for receiving a message from the parent and translating it into the language of the educational institution; means for transmitting the translated message to the educational institution; means for receiving a reply message from the educational institution and translating it into the parent's target language; and means for analyzing the emotion contained in the message during translation and adjusting the translation tone accordingly. This allows smooth and accurate communication between the two parties, and enables information to be communicated in an appropriate tone, especially in emergencies when emotions are strongly expressed.

[0815] "Document receiving means" refers to a means for receiving documents sent from a school, educational institution, or security service.

[0816] A "translation means" is a means for translating a received document into a target language.

[0817] "Transmission means" refers to the means for transmitting the translated document to the parent or client's terminal.

[0818] A "specific word detection means" is a means for detecting specific words or phrases within a document.

[0819] The "explanation acquisition means" is a means for acquiring an explanation corresponding to a detected specific word or phrase from a database.

[0820] "Sentiment analysis means" is a means for analyzing the emotions contained in a message and adjusting the translation tone according to the results.

[0821] The "means for receiving a parental message" is a means for receiving a message from a parent.

[0822] "Educational institution language translation means" refers to a means for translating messages received from parents into the language of the educational institution.

[0823] The "means for receiving a reply message" is a means for receiving a reply message from an educational institution.

[0824] "Machine learning algorithms" are algorithms used to improve translation accuracy.

[0825] The "translation tone adjustment means" is a means for adjusting the tone of a translated message based on the results of sentiment analysis.

[0826] To implement this invention, a system is constructed that links multiple terminals and a server owned by schools, educational institutions, parents, and security services. The program processing procedures of this system and the hardware and software used are described in detail below.

[0827] System Overview:

[0828] The system consists of various devices (educational institution devices, parent devices, and security service devices) and a server. The server is equipped with a document receiving means, a translation means, a sending means, a specific word detection means, a description acquisition means, a sentiment analysis means, a parent message receiving means, an educational institution language translation means, and a reply message receiving means.

[0829] Hardware and software used:

[0830] Hardware: smartphones, laptops, servers, etc.

[0831] Software: Python 3.x, Googletrans library (translation function), TextBlob (sentiment analysis), REST API (HTTP communication).

[0832] Internet connection: Required to enable system-wide communication.

[0833] Data processing and calculation details:

[0834] 1. Document Receipt Method:

[0835] The server receives documents sent by educational institutions and security services, including event notifications and emergency reports.

[0836] 2. Translation Methods:

[0837] Translates the received document into the target language using the Python GoogleTrans library. If the document is in Japanese, it will be translated into a specific language (e.g., English or Chinese).

[0838] 3. Sentiment analysis tools:

[0839] Using Natural Language Processing (NLP) techniques, the TextBlob library analyzes the sentiment contained in a document and adjusts the translation tone accordingly.

[0840] 4. Specific word detection method:

[0841] The server detects specific words and phrases and retrieves their corresponding descriptions from a database, in accordance with US standards.

[0842] 5. Description acquisition method:

[0843] For each detected word or phrase, an explanation is retrieved from the database and translated into the target language.

[0844] 6. Means of transmission:

[0845] The translated document and explanation are sent to the parent or client's device. Communication is carried out using a REST API.

[0846] 7. Parental Message Reception Method:

[0847] Receive messages from parents and clients, translate them into the language of the educational institution or security service, and send reply messages in the same way.

[0848] Examples and prompts:

[0849] For example, if a client sends you a message saying, "My security alarm went off unexpectedly," use the following prompt to apply the appropriate translation tone:

[0850] Translate the following English emergency message into Japanese with the appropriate tone: "My security alarm went off unexpectedly." Ensure the translated message reflects a serious tone appropriate for an emergency situation.

[0851] In this way, the server analyzes the sentiment of the received message and sends a translated message with the appropriate tone to educational institutions and security services, enabling accurate and prompt communication across language barriers.

[0852] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0853] Step 1:

[0854] The server receives documents sent from schools, educational institutions, or security services. As input, document data is sent to the server. As output, the received document data is stored within the server. Specifically, the server receives the document data over the network using the standard HTTP protocol.

[0855] Step 2:

[0856] The server translates the received document into the target language. As input, the received document data and target language information are provided to the server. As output, a document translated into the target language is generated. Specifically, the server translates the document using Python's Google Trans library.

[0857] Step 3:

[0858] The server analyzes the sentiment of the document and adjusts the translation tone accordingly. As input, the translated document is fed into the server. As output, a translated document with an adjusted tone is generated. Specifically, the server uses the TextBlob library to analyze the sentiment of the document and apply a tone adjustment algorithm.

[0859] Step 4:

[0860] The server detects specific words or phrases in a document and retrieves their corresponding descriptions from a database. The translated document is entered into the server as input. The corresponding descriptions are obtained as output. Specifically, the server uses Natural Language Processing (NLP) techniques to detect specific words and executes a database query.

[0861] Step 5:

[0862] The server translates the acquired description into the target language. The acquired description is input to the server as input. The translated description into the target language is generated as output. Specifically, the server again translates the description using the GoogleTrans library.

[0863] Step 6:

[0864] The server sends the translated document and explanatory text to the parent or client device. As input, the adjusted translated document and explanatory text are entered into the server. As output, the translated document and explanatory text are sent to the parent or client device. Specifically, the server sends data using a REST API.

[0865] Step 7:

[0866] The server receives messages from guardians and clients. As input, messages sent from guardians and clients are input to the server. As output, the received messages are stored in the server. In concrete terms, the server receives and stores messages via the network.

[0867] Step 8:

[0868] The server translates the received message into the language of the educational institution or security service. As input, the server is provided with the received message and information about the target language. As output, the translated message in the target language is generated. Specifically, the server uses the GoogleTrans library to translate the message.

[0869] Step 9:

[0870] The server sends the translated message to the educational institution or security service. As input, the translated message is entered into the server. As output, the translated message is sent to the educational institution or security service's terminal. In concrete terms, the server sends data using a REST API.

[0871] Step 10:

[0872] The server receives a reply message from the educational institution or security service. As input, the reply message sent from the educational institution or security service is input to the server. As output, the received reply message is stored in the server. In concrete terms, the server receives the reply message via the network.

[0873] Step 11:

[0874] The server translates the received reply message into the language of the parent or client. As input, the received reply message and target language information are provided to the server. As output, a reply message translated into the target language is generated. Specifically, the server translates the reply message using the GoogleTrans library.

[0875] Step 12:

[0876] The server sends the translated reply message to the parent or client's device. The translated reply message is entered into the server as input. The translated reply message is sent to the parent or client's device as output. Specifically, the server sends data using a REST API.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] [Third embodiment]

[0881] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0882] 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.

[0883] 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).

[0884] 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.

[0885] 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.

[0886] 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).

[0887] 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. 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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."

[0893] The present invention is a translation system for appropriately conveying documents from schools and educational institutions to parents, and realizes smooth communication beyond language barriers. The system of the present invention is specifically implemented as follows.

[0894] First, in terms of the system configuration, documents created by users (schools and educational institutions) are sent to a server via the Internet. The server then analyzes the contents of the document and translates it as necessary.

[0895] 1. Automatic translation of letters and contacts

[0896] The user (school or educational institution) creates a document in Japanese and sends it to the server. The server analyzes the received document and translates it into the target language using natural language processing technology. The translated document is then sent from the server to the parent's device, who can receive the document in a language they can understand.

[0897] Examples:

[0898] A user (school) creates and sends a notification saying, "There will be a sports day next Monday. Please bring your sportswear and water bottles."

[0899] The server receives this document and translates it into "Next Monday, we will have a sports day. Please bring sportswear and a water bottle."

[0900] The translated document will be sent to the parent's device.

[0901] 2. Explanation of specific words and phrases

[0902] When analyzing the content of the document, the server detects certain words and phrases. For these words and phrases, the server retrieves corresponding explanations from a database and provides them to the parent. The explanations are then translated into the target language and sent as an attachment to the original document.

[0903] Examples:

[0904] The server detects the word "sports day" and retrieves the description "sports day is a sporting event held at school" from the database.

[0905] The server adds the explanation "Sports day is a school event where students participate in various sports activities" to the document and sends it along with the translated document.

[0906] 3. Communication between both parties

[0907] When a parent sends a message in their native language, such as English, the message is received by the server. The server translates the received message into Japanese and sends it to the educational institution. The educational institution's reply is also sent to the server, where it is translated into the target language and then sent to the parent's device.

[0908] Examples:

[0909] A parent sends a message saying, "My child has a doctor's appointment on Friday. Will there be any important activities that day?"

[0910] The server translates this to "My child has a hospital appointment on Friday. Do you have any important activities on that day?" and sends it to the institution.

[0911] The educational institution replies, "There are no important activities on that day."

[0912] The server translates this as "There are no particularly important activities on that day" and sends it to the parent's device.

[0913] This system will ensure smooth communication between schools and educational institutions and foreign parents, and will ensure that necessary information is accurately conveyed to parents who do not understand Japanese.

[0914] The processing flow will be explained below.

[0915] Step 1:

[0916] A user (such as a school or educational institution) creates a document and sends it to the server. The document can include, for example, a letter or a contact list.

[0917] Step 2:

[0918] The server analyzes the Japanese document received from the user, taking in the document's contents in text format and extracting specific information from it.

[0919] Step 3:

[0920] The server translates the document content into the target language. The server uses natural language processing (NLP) technology to analyze the entire document and translate it appropriately. For example, it calls a translation API to translate "There will be a sports day next Monday." into "Next Monday, we will have a sports day."

[0921] Step 4:

[0922] The server detects specific words and phrases within a document, for example, event names such as "athletic meet" or "athletic festival" or special words.

[0923] Step 5:

[0924] The server retrieves from the database the description that corresponds to the detected word or phrase, for example, "Undokai is a sports event held at Japanese schools."

[0925] Step 6:

[0926] The server translates the retrieved description into the target language. For example, the description could be translated as "Sports day is an event held at Japanese schools where students participate in various sports activities."

[0927] Step 7:

[0928] The server adds an explanatory text to the translated document, for example, "Next Monday, we will have a sports day. (Sports day is an event held at Japanese schools where students participate in various sports activities)."

[0929] Step 8:

[0930] The server sends the translated document to the parent's device, allowing the parent to receive information in their own language.

[0931] Step 9:

[0932] The user (parent) enters questions or comments in their native language and sends them to the server from their device. For example, they send a message like, "My child has a doctor's appointment on Friday. Will there be any important activities that day?"

[0933] Step 10:

[0934] The server receives the message from the parent and translates it into Japanese, saying, "My child has a hospital appointment on Friday. Do you have any important activities on that day?"

[0935] Step 11:

[0936] The server sends the translated message to the educational institution, which can then understand the parent's question in Japanese.

[0937] Step 12:

[0938] The user (educational institution) enters a reply message in Japanese and sends it to the server. For example, the reply may say, "There are no particularly important activities on that day."

[0939] Step 13:

[0940] The server receives the reply message from the educational institution and translates it into the parent's native language, stating, "There are no particularly important activities on that day."

[0941] Step 14:

[0942] The server then sends the translated reply message to the parent's device, allowing the parent to receive a reply in their own language and respond appropriately.

[0943] As described above, each step of the process is carried out sequentially, and smooth and accurate communication between users is achieved through this series of processes.

[0944] Example 1

[0945] 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."

[0946] Traditionally, communication between schools and educational institutions and parents has often been hindered by language barriers. Important notices and event notifications may not be conveyed accurately to parents, leading to misunderstandings and inconvenience. Furthermore, questions and messages from parents may not be translated properly, making smooth communication with educational institutions difficult.

[0947] 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.

[0948] In this invention, the server includes means for receiving documents sent from a school or educational institution, means for analyzing the received documents, means for translating the received documents into a target language, means for sending the translation results to the parent's terminal, means for detecting specific words or phrases and obtaining corresponding explanatory text, means for translating the obtained explanatory text into the target language and sending the translation results together with the explanatory text to the parent's terminal, means for receiving messages from the parent and translating them into the language of the educational institution, means for sending the translated messages to the educational institution, means for receiving reply messages from the educational institution and translating them into the parent's target language, means for sending the translated reply messages to the parent's terminal, means for presenting received documents in a form that is easy for users to understand, and means for translating messages sent by users into the original language of the documents, thereby enabling smooth and accurate communication that transcends language barriers.

[0949] "Schools and educational institutions" are organizations and facilities established for the purpose of education, including daycare centers, kindergartens, elementary schools, junior high schools, high schools, universities, and vocational schools.

[0950] A "document" is written information or instructions in text form that is produced for the purpose of communication or record-keeping.

[0951] "Receiving" is the act of receiving information or data sent from another party.

[0952] A "server" is a central computer that processes data and provides services to other computers and devices over a network.

[0953] "Analysis" is the act of investigating and evaluating data and information in detail to clarify their meaning and structure.

[0954] "Target language" is the language into which the original document is to be translated in a translation task.

[0955] "Translation" is the act of converting content written in one language into another language.

[0956] A "terminal" is a device connected to a computer system that can be operated by a user.

[0957] "Specific words or phrases" refers to certain keywords or expressions that are important or frequently appear in a document or message.

[0958] An "explanatory text" is a piece of text that explains the meaning and usage of a particular word or phrase.

[0959] A "message" is a document or word sent between users to convey information.

[0960] A "database" is a collection of information that has been organized and stored so that the data can be efficiently searched and used.

[0961] "Machine learning" is a technology that uses algorithms and statistical models to allow computers to learn from experience and perform data analysis and predictions.

[0962] MODE FOR CARRYING OUT THE INVENTION

[0963] This invention is a translation system for properly conveying documents sent from schools and educational institutions to parents, realizing smooth communication beyond language barriers. This system is specifically implemented through the process of receiving, analyzing, translating, and transmitting data between a server, terminals, and users.

[0964] First, a user (school or educational institution) creates a document in Japanese and sends it to a server via the Internet from their terminal. The user can use a standard computer or mobile device to send the document using a web interface or a dedicated application.

[0965] The server parses the received document using a natural language processing library (e.g., spaCy or NLTK) to extract important keywords and phrases from the document.

[0966] The server then calls a translation service such as Google Translate API or DeepL to translate the analyzed document into the target language, and the translated document is sent to the parent's device, allowing the parent to receive the document in a language they understand.

[0967] For example, if a user creates a document stating, "We have a sports day next Monday. Please bring sportswear and a water bottle.", the server translates this into "Next Monday, we will have a sports day. Please bring sportswear and a water bottle." and sends it to the parent's device.

[0968] The server also detects specific words and phrases in the document and retrieves corresponding explanations from the database. The explanations are also translated into the target language and added to the original document before being sent to the parent's device. This process allows the parent to gain a deeper understanding of the document's contents.

[0969] For example, if the server detects the word "sports day," it retrieves the explanation "sports day is a school event where students participate in various sports activities" from the database, translates it into "sports day is a school event where students participate in various sports activities," and adds it to the document. The document with this additional explanation is then sent to the parent's device.

[0970] Furthermore, messages from parents are also exchanged via the server. When parents send messages in their native language, such as English, the server translates them into Japanese and sends them to the educational institution. Responses from the educational institution are also sent to the server, which translates them into the parent's target language. This ensures smooth two-way communication between the educational institution and parents.

[0971] Examples of specific prompts include:

[0972] "Please translate the Japanese sentence, 'There will be a sports day next Monday. Please bring your sportswear and water bottles.' into English."

[0973] "Write a sentence explaining the meaning of the word 'sports day'."

[0974] "Please translate the following English message into Japanese: 'My child has a doctor's appointment on Friday. Will there be any important activities that day?'"

[0975] This makes it possible to obtain appropriate translations and descriptions from generative AI models.

[0976] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0977] Step 1:

[0978] A user (school or educational institution) creates a document and sends it to the server from their device.

[0979] Specifically, a user uses word processing software or a dedicated application to create a document, such as "There will be a sports day next Monday. Please bring your sportswear and water bottle." The document is then uploaded to a server from the terminal via the Internet. The input is the document, and the output is the uploading of the document to the server.

[0980] Step 2:

[0981] The server parses the received document.

[0982] The server begins analyzing the received document using a natural language processing library (e.g., spaCy or NLTK). Specifically, it extracts important keywords and phrases (e.g., "sports day") from the document. The input is the received document, and the output is the extracted keywords and phrases.

[0983] Step 3:

[0984] The server translates the document into the target language.

[0985] The server calls a translation service such as Google Translate API or DeepL and translates the parsed document into the target language. For example, it translates "We will have a sports day next Monday. Please bring sportswear and a water bottle." into "Next Monday, we will have a sports day. Please bring sportswear and a water bottle." The input is the parsed document, and the output is the translated document.

[0986] Step 4:

[0987] The server sends the translation results to the parent's device.

[0988] The server sends the translated document to the designated parent's device. Specifically, the translated document is delivered to the parent via email or a dedicated application. The input is the translated document, and the output is the document sent to the parent's device.

[0989] Step 5:

[0990] The server detects specific words or phrases in the document and retrieves their corresponding descriptions from a database.

[0991] The server detects specific words or phrases (e.g., "sports day") from the parsed document and retrieves the description "sports day is a sporting event held at school" from the database. The input is the parsed document, and the output is the retrieved description.

[0992] Step 6:

[0993] The server translates the description into the target language and adds it to the original document.

[0994] The server translates the retrieved description into the target language using the Google Translate API or DeepL. Specifically, it translates "Sports day is a school event where students participate in various sports activities" into "Sports day is a school event where students participate in various sports activities" and adds it to the translated document. The input is the retrieved description, and the output is the translated document with the description added.

[0995] Step 7:

[0996] The server sends the translated document with explanatory text to the parent's device.

[0997] The server adds explanatory text to the translated document and sends it to the parent's device. Specifically, it presents it in a format that is easy for the parent to understand. The input is the translated document with explanatory text added, and the output is the translated document with explanatory text sent to the parent's device.

[0998] Step 8:

[0999] The server receives messages from parents and translates them into the institution's language.

[1000] For example, a parent sends a message from their device to the server, such as "My child has a doctor's appointment on Friday. Will there be any important activities that day?" The server translates this to "My child has a doctor's appointment on Friday. Will there be any important activities that day?" The input is the message from the parent, and the output is the message translated into the language of the educational institution.

[1001] Step 9:

[1002] The server sends the translated message to the institution.

[1003] The server sends the translated message to the educational institution's terminal. The specific operation is to provide information in a form that the educational institution can easily understand. The input is the translated message, and the output is the message sent to the educational institution's terminal.

[1004] Step 10:

[1005] The server receives the reply message from the educational institution and translates it into the parent's target language.

[1006] The institution sends a reply to the server, for example, "There are no particularly important activities on that day." The server translates this to "There are no particularly important activities on that day." The input is the reply message from the institution, and the output is the translated reply message.

[1007] Step 11:

[1008] The server sends the translated reply message to the parent's device.

[1009] The server sends the translated reply message to the parent's device. Specifically, the server provides the translated reply message in a format that the parent can easily check. The input is the translated reply message, and the output is the message sent to the parent's device.

[1010] (Application example 1)

[1011] 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."

[1012] The present invention relates to a system for accurately and quickly transmitting order information between customers who speak different languages ​​and restaurants, thereby eliminating language barriers in food delivery services.

[1013] Specifically, there is a need for a means that allows customers to place orders in their own language, ensures that the restaurant accurately understands the content of the orders, and also allows the restaurant to properly convey order confirmation messages to customers.

[1014] 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.

[1015] In this invention, the server includes means for receiving a document sent from a school or educational institution, means for translating the received document into a target language, means for transmitting the translation result to the parent's terminal, means for detecting specific words or phrases and obtaining corresponding explanatory text, means for translating the obtained explanatory text into the target language and transmitting the translation result together with the explanatory text to the parent's terminal, means for receiving a message from the parent and translating it into the language of the educational institution, means for transmitting the translated message to the educational institution, means for receiving a reply message from the educational institution and translating it into the parent's target language, means for transmitting the translated reply message to the parent's terminal, means for receiving order information from a customer and translating it into the restaurant's language, means for transmitting the translated order information to the restaurant, means for receiving order confirmation information from the restaurant and translating it into the customer's target language, and means for transmitting the translated order confirmation information to the customer's terminal. This enables order information to be communicated accurately and quickly between customers and restaurants, eliminating language barriers.

[1016] "Schools and educational institutions" refers to organizations and facilities for carrying out educational activities, such as elementary schools, junior high schools, high schools, universities, and vocational schools.

[1017] "Document" means any paper or digital file containing textual information, including notices, correspondence, reports, etc.

[1018] "Translation means" refers to the methods and techniques used to convert a document written in one language into another language.

[1019] "Parental devices" refers to electronic devices such as computers, smartphones, and tablets used by parents of children or students.

[1020] "Specific words and phrases" refer to specific keywords and expressions in a document, and are words and phrases that contain important information that requires explanation.

[1021] An "explanation" is text information that provides detailed explanations of the meaning and background of specific words or phrases.

[1022] "Customer" means the person or entity placing an order to purchase or use a service or product.

[1023] "Order Information" means the requests and details provided by Customer when purchasing goods or services.

[1024] "Food and beverage establishments" refers to establishments and restaurants that serve food and beverages.

[1025] "Order confirmation information" is information that a restaurant sends back after confirming the order details received from a customer.

[1026] System Configuration

[1027] The present invention is a translation system for accurately and quickly communicating order information between customers and restaurants, and includes the following components: a server, a customer terminal, and a restaurant terminal, all of which work in conjunction with one another.

[1028] Hardware and Software

[1029] The hardware used is as follows:

[1030] Server (e.g., Amazon Web Services EC2 instance)

[1031] Customer and restaurant devices (e.g., smartphones, tablets, computers)

[1032] The software used is as follows:

[1033] Server-side framework: Flask (a Python micro web framework)

[1034] Translation engine: Google Translate API

[1035] Natural language processing techniques and machine learning algorithms: Machine learning models (e.g., BERT, GPT-3)

[1036] Data processing and calculation flow

[1037] The server processes the data according to the following procedure.

[1038] 1. Document Receipt and Translation:

[1039] The server receives the order information entered on the customer's device. For example, the sentence "I would like to order a large pizza and a Coke" is sent.

[1040] The server uses the Google Translate API to translate this order information from the customer's native language into the restaurant's language.

[1041] 2. Sending Order Information:

[1042] When the translation result is "I would like to order a large pizza and a Coke," it is sent to the restaurant's terminal.

[1043] 3. Receiving and translating order confirmation messages:

[1044] Receive order confirmation from the restaurant terminal, such as "We have received your order for a large pizza and a Coke."

[1045] The server uses the Google Translate API to translate this confirmation information from the restaurant's language into the customer's native language.

[1046] 4. Sending confirmation information:

[1047] Once the translation result is "Your order for a large pizza and a Coke has been accepted," it is sent to the customer's terminal.

[1048] Specific examples

[1049] For example, if an English-speaking customer places an order by saying, "I would like to order a large pizza and a Coke," this order information is translated and transmitted as follows:

[1050] "I would like to order a large pizza and a Coke."

[1051] "I'd like to order a large pizza and a Coke."

[1052] Similarly, a confirmation message from a restaurant saying "We have received your order for a large pizza and a Coke" would be translated and sent as follows:

[1053] "Your order for a large pizza and a Coke has been accepted."

[1054] Translated into:English "We have received your order for a large pizza and a Coke."

[1055] Prompt Sentence Examples

[1056] English prompt:

[1057] "I would like to order a large pizza and a Coke."

[1058] Translated Japanese:

[1059] "I'd like to order a large pizza and a Coke."

[1060] In this way, the system of the present invention allows customers to input orders in their native language and accurately translates them into the restaurant's language, enabling smooth communication of order information across language barriers. By using the Google Translate API as the translation engine and performing detailed data processing on the server side, accurate and prompt communication between customers and restaurants is achieved.

[1061] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1062] Step 1:

[1063] A user inputs order information into a terminal. The input information is text data such as "I would like to order a large pizza and a Coke." This information is in the customer's native language and is sent from the terminal to the server.

[1064] Step 2:

[1065] The server receives the order information from the customer's device. The input is in the customer's native language (e.g., "I would like to order a large pizza and a Coke."), and this information is converted for processing within the server.

[1066] Step 3:

[1067] The server translates the order information using the Google Translate API. The input is the order information in English, the customer's native language, and the output is the order information translated into the restaurant's language (e.g., "I'd like to order a large pizza and a Coke."). The server accesses the API, converts the text data, and obtains the translation results.

[1068] Step 4:

[1069] The server sends the translated order information to the restaurant's terminal. The input is the translated order information (e.g., "I'd like to order a large pizza and a Coke."), and the output is the data sent to the restaurant's terminal. The server sends the information using a network protocol.

[1070] Step 5:

[1071] The restaurant terminal receives the order information and generates a confirmation message. The input is the translated order information received from the server, and the output is a confirmation message written in the restaurant's language (e.g., "We have received your order for a large pizza and a Coke."). The restaurant terminal may automatically generate this message, or a staff member may type it in.

[1072] Step 6:

[1073] The server receives an order confirmation message from the restaurant terminal. The input is the confirmation message in the restaurant's language (e.g., "We have received your order for a large pizza and a Coke."), and the output is retained for processing within the server.

[1074] Step 7:

[1075] The server translates the confirmation message using the Google Translate API. The input is the confirmation message in the restaurant's language, and the output is the confirmation message translated into the customer's native language (e.g., "Your order for a large pizza and a Coke has been accepted."). The server accesses the API, converts the text data, and obtains the translation result.

[1076] Step 8:

[1077] The server sends a translated confirmation message to the customer's device. The input is the translated confirmation message (e.g., "Your order for a large pizza and a Coke has been accepted.") and the output is the data sent to the customer's device. The server sends the information using a network protocol.

[1078] This series of processes enables smooth communication between customers who speak different languages ​​and restaurants, allowing for accurate and prompt ordering and confirmation.

[1079] 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.

[1080] This invention is a system for facilitating communication between schools and educational institutions and parents, and in particular provides technology for accurately conveying information across language barriers. A key feature of this invention is that it recognizes the user's emotions by combining an emotion engine and applies a translation tone accordingly. The processing flow of this system will now be described in detail.

[1081] 1. Automatic translation of letters and contacts

[1082] A user (school or educational institution) creates a document in Japanese and sends it to the server. The server analyzes the received document and uses natural language processing technology to translate the content into the target language. The translated document is then sent from the server to the parent's device, who can receive the document in a language they can understand. The emotion engine can also analyze the emotion contained in the document and adjust the translation tone accordingly.

[1083] Examples:

[1084] A user (school) creates and sends a notification saying, "There will be a sports day next Monday. Please bring your sportswear and water bottles."

[1085] The server receives this document and translates it into "Next Monday, we will have a sports day. Please bring sportswear and a water bottle."

[1086] The emotion engine recognizes the tone of the document as "cheerful" and reflects this in the translation.

[1087] The translated document will be sent to the parent's device.

[1088] 2. Explanation of specific words and phrases

[1089] When analyzing the content of a document, the server detects certain words and phrases. For these words and phrases, the server retrieves corresponding explanations from a database and provides them to parents as part of the translation. After the explanations are translated into the target language, they are added to the original document.

[1090] Examples:

[1091] The server detects the word "sports day" and retrieves the description "sports day is a sports event held at Japanese schools" from the database.

[1092] The server translates the description into "Sports day is an event held at Japanese schools where students participate in various sports activities."

[1093] The server responds to the document with the following: "Next Monday, we will have a sports day. Please bring sportswear and a water bottle. (Sports day is an event held at Japanese schools where students participate in various sports activities)."

[1094] 3. Communication between both parties

[1095] When a parent sends a message in their native language, the message is received by the server. The server translates the received message into Japanese and sends it to the educational institution. Reply messages are similarly translated and sent to the parent's device in their native language. The server uses an emotion engine to recognize the emotions contained in the parent's and educational institution's messages and applies the corresponding translation tone.

[1096] Examples:

[1097] A parent sends a message saying, "My child has a doctor's appointment on Friday. Will there be any important activities that day?"

[1098] The server translates this to "My child has a hospital appointment on Friday. Do you have any important activities on that day?" and sends it to the institution.

[1099] The emotion engine recognizes the emotion of the question as "concern" and adjusts the tone of the reply appropriately.

[1100] The educational institution replies, "There are no important activities on that day."

[1101] The server translates this as "There are no particularly important activities on that day" and sends it to the parent's device.

[1102] This invention enables smooth and accurate communication that transcends language barriers, deepening understanding between parents and educational institutions. By utilizing an emotion engine, the content of messages is conveyed appropriately, making dialogue even smoother.

[1103] The processing flow will be explained below.

[1104] Step 1:

[1105] A user (school or educational institution) creates a document and sends it to the server. For example, a user creates a notification saying, "We have a sports day next Monday. Please bring your sports clothes and water bottles."

[1106] Step 2:

[1107] The server analyzes the Japanese document received from the user, captures the document content in text format, and prepares it for translation.

[1108] Step 3:

[1109] The server uses natural language processing technology to translate the content of the document into the target language. For example, when translating a Japanese document into English, the server converts it into "Next Monday, we will have a sports day. Please bring sportswear and a water bottle."

[1110] Step 4:

[1111] The server detects specific words and phrases within a document, for example recognizing words like "sports day" and "sportswear."

[1112] Step 5:

[1113] The server retrieves from the database the description that corresponds to the detected word or phrase, for example, "Undokai is a sports event held at Japanese schools."

[1114] Step 6:

[1115] The server translates the obtained description into the target language. For example, "Sports day is an event held at Japanese schools where students participate in various sports activities."

[1116] Step 7:

[1117] The server generates a document with the translated explanation added. For example, the server adds an explanation to the translated document, such as "Next Monday, we will have a sports day. Please bring sportswear and a water bottle. (Sports day is an event held at Japanese schools where students participate in various sports activities)."

[1118] Step 8:

[1119] The server uses an emotion engine to analyze the emotion contained in the document, for example, recognizing the document's tone as "cheerful."

[1120] Step 9:

[1121] The server adjusts the translation tone based on the emotion recognition results. For example, it sets an appropriate translation tone and provides the document to the parent as a cheerful document.

[1122] Step 10:

[1123] The server sends the translated document to the parent's device, where the parent can receive the document in a language they understand.

[1124] Step 11:

[1125] The user (parent) creates a message in their native language and sends it to the server from their device. For example, they send a question such as, "My child has a doctor's appointment on Friday. Will there be any important activities that day?"

[1126] Step 12:

[1127] The server receives the message from the parent and uses natural language processing technology to translate it into Japanese, for example, "My child has a hospital appointment on Friday. Are there any important activities on that day?"

[1128] Step 13:

[1129] The server sends the translated message to the educational institution, which can then receive the parent's questions in Japanese.

[1130] Step 14:

[1131] The server uses an emotion engine to analyze the emotion contained in the parent's message. For example, the emotion engine recognizes "worry."

[1132] Step 15:

[1133] The user (educational institution) creates a reply message in Japanese and sends it to the server. For example, they reply, "There are no particularly important activities on that day."

[1134] Step 16:

[1135] The server receives the reply message from the educational institution and translates it into the target language, for example, "There are no particularly important activities on that day."

[1136] Step 17:

[1137] The server uses an emotion engine to analyze the emotion contained in the educational institution's reply message, for example recognizing "a sense of relief."

[1138] Step 18:

[1139] The server adjusts the translation tone based on the emotion recognition result, and applies an appropriate tone to provide the translated message to the parent.

[1140] Step 19:

[1141] The server sends the translated reply message to the parent's device, who can then receive the reply in their own language through the device.

[1142] As described above, processing is performed at each step, ensuring smooth communication between users.

[1143] Example 2

[1144] 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."

[1145] Language barriers can be an obstacle when communicating between schools and parents, making it difficult to convey information accurately. Furthermore, the quality of communication can decline if emotional nuances are not conveyed properly when documents are translated. Furthermore, replies and inquiries from parents cannot be handled quickly and accurately.

[1146] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1147] In this invention, the server includes a means for receiving a document sent from a school or educational institution, a means for translating the received document into a target language, and a means for analyzing the emotion of the document using an emotion recognition engine and adjusting the translation tone. This enables accurate communication that transcends language barriers and incorporates emotional nuances. The server also includes a means for retrieving specific words or phrases from a database and translating the corresponding explanatory text into the target language, a means for translating the retrieved explanatory text into the target language and sending the translation result to the parent's terminal, a means for receiving a message from the parent and translating it into the language of the educational institution, a means for sending the translated message to the educational institution, a means for receiving a reply message from the educational institution and translating it into the parent's target language, and a means for sending the translated reply message to the parent's terminal. This system enables rapid and accurate two-way communication.

[1148] "Schools and educational institutions" is a general term for organizations that provide education to children and students.

[1149] "Document" refers to documents or text data that express information using characters and diagrams.

[1150] A "server" refers to a computer system that provides services and data in response to client requests over a network.

[1151] "Target language" refers to a language other than Japanese that is designated as the target language for translation.

[1152] "Translation means" refers to a function that converts a document written in one language into another language.

[1153] An "emotion recognition engine" refers to a software system that analyzes the emotions and tone in a document and adjusts the appropriate translation tone based on the results.

[1154] A "terminal" refers to a device, such as a PC or smartphone, that allows a user to receive or send information over a network.

[1155] "Specific words and phrases" refer to words or expressions that are considered particularly important in the document to be translated.

[1156] A "database" refers to a system that organizes and stores specific data so that it can be easily searched and retrieved.

[1157] "Parent" refers to a parent or legal guardian of a child or student, or any other adult who is required to communicate with an educational institution.

[1158] A "machine learning algorithm" refers to a computational method that learns patterns from data and uses those patterns to predict or classify new data.

[1159] The present invention provides a system for facilitating communication between schools or educational institutions and parents, and in particular provides technology for accurately transmitting information across language barriers. Specific embodiments of this system are described in detail below.

[1160] Hardware and software used

[1161] Server: Use a high-performance network server, such as an Apache or Nginx server.

[1162] Device: A device used by users and parents. This primarily refers to devices that can connect to the internet, such as smartphones, tablets, and PCs.

[1163] Translation API: Used to automatically translate documents, specifically Google Translate API and Microsoft Translator.

[1164] Natural language processing libraries: Used for document analysis and sentiment recognition. Examples include the nltk library and spaCy.

[1165] Machine learning models, such as OpenAI's GPT model, are used to analyze the sentiment of a document and adjust the translation tone.

[1166] Explanation of system processing

[1167] 1. Sending documents

[1168] Users (schools and educational institutions) create letters and contacts in Japanese, and the documents are sent to the server via a specified API endpoint.

[1169] Example: A user creates a notification that says, "We have a sports day next Monday. Please bring your sports clothes and water bottle."

[1170] 2. Document analysis on the server

[1171] The server analyzes the received document, tokenizing it using a natural language processing library (e.g., nltk, spaCy) and performing syntax and sentiment analysis.

[1172] 3. Translation Processing

[1173] Based on the analysis results, the server identifies the target language and calls a translation API (e.g., Google Translate API) to translate the document into the target language, using an emotion recognition engine to analyze the sentiment of the document and adjust the appropriate translation tone.

[1174] Example: The server translates the sentence "There will be a sports day next Monday." into "Next Monday, we will have a sports day.", and the emotion engine determines that this sentence is "cheerful."

[1175] 4. Sending translation results

[1176] The server converts the translated text into packets and sends them to the parent's device, which interprets the received packets and displays them on the screen.

[1177] 5. Processing responses from parents

[1178] Parents can create a reply message in their own language and send it to the server. The server then translates the message and sends it to the educational institution. The educational institution's reply is also translated via the server and sent to the parent's device.

[1179] Examples of concrete examples and prompts

[1180] Example: A parent sends a message saying, "My child has a doctor's appointment on Friday. Will there be any important activities that day?" The server translates this to "My child has a doctor's appointment on Friday. Will there be any important activities that day?" and sends it to the educational institution. The educational institution replies, "There are no particularly important activities on that day," which the server translates to "There are no particularly important activities on that day" and sends it to the parent's device.

[1181] Example prompt sentence:

[1182] "We have a sports day next Monday. Please bring your sports clothes and water bottles."

[1183] "My child has a doctor's appointment on Friday. Will there be any important activities that day?"

[1184] In this way, the system of the present invention works in combination with users, servers, and terminals to provide accurate and efficient communication across language barriers. By utilizing an emotion recognition engine and machine learning algorithms, the tone and emotion of the message are also reflected appropriately.

[1185] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1186] Step 1:

[1187] A user creates a document and submits it to the server.

[1188] Specifically, the user (school or educational institution) creates a letter or contact list in Japanese. The created document is sent to the server via the API endpoint.

[1189] Input: Document written in Japanese

[1190] Output: The document sent to the server

[1191] Step 2:

[1192] The server receives and parses the document.

[1193] Specifically, the server analyzes the received document using a natural language processing library (e.g., nltk, spaCy), which includes tokenization, syntactic analysis, and sentiment analysis.

[1194] Input: Received Japanese document

[1195] Output: Analysis results (tokenized data, syntactic information, sentiment analysis results)

[1196] Step 3:

[1197] The server translates the received document as needed.

[1198] Specifically, the server calls a translation API (e.g., Google Translate API) based on the analysis results to translate the document into the target language, using an emotion recognition engine to analyze the emotion of the document and adjust the appropriate translation tone.

[1199] Input: Analysis results, Japanese document

[1200] Output: Document translated into target language

[1201] Step 4:

[1202] The server uses an emotion engine to analyze the sentiment of the document and adjust the translation tone.

[1203] Specifically, the server uses an emotion recognition engine (e.g., OpenAI GPT model) to analyze the emotions in the document and adjust the translation tone based on the results.

[1204] Input: translated document, sentiment analysis results

[1205] Output: A translated document with adjusted tone

[1206] Step 5:

[1207] The server sends the translation results to the parent's device.

[1208] Specifically, the server converts the translated document into packets and sends them to the parent's device, which then interprets the received packets and displays them on the screen.

[1209] Input: translated document

[1210] Output: Document sent to parent's device

[1211] Step 6:

[1212] Parents review the documents they receive.

[1213] Specifically, the parent's terminal receives packets from the server and displays the document.

[1214] Input: Document sent to parent's device

[1215] Output: Document that parents can view on the screen

[1216] Step 7:

[1217] The parent creates a reply message and sends it to the server.

[1218] Specifically, the parent composes a reply message on the device, types it in their native language, and sends the message to the server.

[1219] Input: A message in your native language created by the parent

[1220] Output: Reply message sent to the server

[1221] Step 8:

[1222] The server translates messages from parents and sends them to the educational institution.

[1223] Specifically, the server receives messages from parents, calls a translation API to translate the messages into Japanese, and then sends the translation results to the educational institution.

[1224] Input: Parental Message

[1225] Output: Japanese translated message

[1226] Step 9:

[1227] The educational institution will review and respond to the message.

[1228] Specifically, the educational institution checks the message received from the server, creates a reply message in Japanese if necessary, and sends the created message to the server.

[1229] Input: Japanese translated message

[1230] Output: Reply message written in Japanese

[1231] Step 10:

[1232] The server translates the response from the educational institution and sends it to the parent.

[1233] Specifically, the server receives the reply from the educational institution, translates the message into the parent's native language using a translation API, converts the translated result into a packet format, and sends it to the parent's device.

[1234] Input: Reply message from educational institution

[1235] Output: Reply message translated into the parent's native language

[1236] Through the above processing steps, the system can provide accurate and efficient communication across language barriers.

[1237] (Application example 2)

[1238] 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."

[1239] Language barriers exist in communication between schools or educational institutions and parents, or between security services and multinational clients, resulting in problems with information not being transmitted accurately and promptly. Misunderstandings and mistranslations are particularly likely in emergencies or when emotions are strong, posing a risk of reduced credibility and adversely affecting actual crisis response. The present invention aims to solve these problems and provide smooth and accurate communication across language barriers.

[1240] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving a document sent from a school or educational institution; means for translating the received document into a target language; means for transmitting the translation result to the parent's terminal; means for detecting specific words or phrases and obtaining corresponding explanatory text; means for translating the obtained explanatory text into the target language and transmitting the translation result together with the explanatory text to the parent's terminal; means for receiving a message from the parent and translating it into the language of the educational institution; means for transmitting the translated message to the educational institution; means for receiving a reply message from the educational institution and translating it into the parent's target language; and means for analyzing the emotion contained in the message during translation and adjusting the translation tone accordingly. This allows smooth and accurate communication between the two parties, and enables information to be communicated in an appropriate tone, especially in emergencies when emotions are strongly expressed.

[1241] "Document receiving means" refers to a means for receiving documents sent from a school, educational institution, or security service.

[1242] A "translation means" is a means for translating a received document into a target language.

[1243] "Transmission means" refers to the means for transmitting the translated document to the parent or client's terminal.

[1244] A "specific word detection means" is a means for detecting specific words or phrases within a document.

[1245] The "explanation acquisition means" is a means for acquiring an explanation corresponding to a detected specific word or phrase from a database.

[1246] "Sentiment analysis means" is a means for analyzing the emotions contained in a message and adjusting the translation tone according to the results.

[1247] The "means for receiving a parental message" is a means for receiving a message from a parent.

[1248] "Educational institution language translation means" refers to a means for translating messages received from parents into the language of the educational institution.

[1249] The "means for receiving a reply message" is a means for receiving a reply message from an educational institution.

[1250] "Machine learning algorithms" are algorithms used to improve translation accuracy.

[1251] The "translation tone adjustment means" is a means for adjusting the tone of a translated message based on the results of sentiment analysis.

[1252] To implement this invention, a system is constructed that links multiple terminals and a server owned by schools, educational institutions, parents, and security services. The program processing procedures of this system and the hardware and software used are described in detail below.

[1253] System Overview:

[1254] The system consists of various devices (educational institution devices, parent devices, and security service devices) and a server. The server is equipped with a document receiving means, a translation means, a sending means, a specific word detection means, a description acquisition means, a sentiment analysis means, a parent message receiving means, an educational institution language translation means, and a reply message receiving means.

[1255] Hardware and software used:

[1256] Hardware: smartphones, laptops, servers, etc.

[1257] Software: Python 3.x, Googletrans library (translation function), TextBlob (sentiment analysis), REST API (HTTP communication).

[1258] Internet connection: Required to enable system-wide communication.

[1259] Data processing and calculation details:

[1260] 1. Document Receipt Method:

[1261] The server receives documents sent by educational institutions and security services, including event notifications and emergency reports.

[1262] 2. Translation Methods:

[1263] Translates the received document into the target language using the Python GoogleTrans library. If the document is in Japanese, it will be translated into a specific language (e.g., English or Chinese).

[1264] 3. Sentiment analysis tools:

[1265] Using Natural Language Processing (NLP) techniques, the TextBlob library analyzes the sentiment contained in a document and adjusts the translation tone accordingly.

[1266] 4. Specific word detection method:

[1267] The server detects specific words and phrases and retrieves their corresponding descriptions from a database, in accordance with US standards.

[1268] 5. Description acquisition method:

[1269] For each detected word or phrase, an explanation is retrieved from the database and translated into the target language.

[1270] 6. Means of transmission:

[1271] The translated document and explanation are sent to the parent or client's device. Communication is carried out using a REST API.

[1272] 7. Parental Message Reception Method:

[1273] Receive messages from parents and clients, translate them into the language of the educational institution or security service, and send reply messages in the same way.

[1274] Examples and prompts:

[1275] For example, if a client sends you a message saying, "My security alarm went off unexpectedly," use the following prompt to apply the appropriate translation tone:

[1276] Translate the following English emergency message into Japanese with the appropriate tone: "My security alarm went off unexpectedly." Ensure the translated message reflects a serious tone appropriate for an emergency situation.

[1277] In this way, the server analyzes the sentiment of the received message and sends a translated message with the appropriate tone to educational institutions and security services, enabling accurate and prompt communication across language barriers.

[1278] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1279] Step 1:

[1280] The server receives documents sent from schools, educational institutions, or security services. As input, document data is sent to the server. As output, the received document data is stored within the server. Specifically, the server receives the document data over the network using the standard HTTP protocol.

[1281] Step 2:

[1282] The server translates the received document into the target language. As input, the received document data and target language information are provided to the server. As output, a document translated into the target language is generated. Specifically, the server translates the document using Python's Google Trans library.

[1283] Step 3:

[1284] The server analyzes the sentiment of the document and adjusts the translation tone accordingly. As input, the translated document is fed into the server. As output, a translated document with an adjusted tone is generated. Specifically, the server uses the TextBlob library to analyze the sentiment of the document and apply a tone adjustment algorithm.

[1285] Step 4:

[1286] The server detects specific words or phrases in a document and retrieves their corresponding descriptions from a database. The translated document is entered into the server as input. The corresponding descriptions are obtained as output. Specifically, the server uses Natural Language Processing (NLP) techniques to detect specific words and executes a database query.

[1287] Step 5:

[1288] The server translates the acquired description into the target language. The acquired description is input to the server as input. The translated description into the target language is generated as output. Specifically, the server again translates the description using the GoogleTrans library.

[1289] Step 6:

[1290] The server sends the translated document and explanatory text to the parent or client device. As input, the adjusted translated document and explanatory text are entered into the server. As output, the translated document and explanatory text are sent to the parent or client device. Specifically, the server sends data using a REST API.

[1291] Step 7:

[1292] The server receives messages from guardians and clients. As input, messages sent from guardians and clients are input to the server. As output, the received messages are stored in the server. In concrete terms, the server receives and stores messages via the network.

[1293] Step 8:

[1294] The server translates the received message into the language of the educational institution or security service. As input, the server is provided with the received message and information about the target language. As output, the translated message in the target language is generated. Specifically, the server uses the GoogleTrans library to translate the message.

[1295] Step 9:

[1296] The server sends the translated message to the educational institution or security service. As input, the translated message is entered into the server. As output, the translated message is sent to the educational institution or security service's terminal. In concrete terms, the server sends data using a REST API.

[1297] Step 10:

[1298] The server receives a reply message from the educational institution or security service. As input, the reply message sent from the educational institution or security service is input to the server. As output, the received reply message is stored in the server. In concrete terms, the server receives the reply message via the network.

[1299] Step 11:

[1300] The server translates the received reply message into the language of the parent or client. As input, the received reply message and target language information are provided to the server. As output, a reply message translated into the target language is generated. Specifically, the server translates the reply message using the GoogleTrans library.

[1301] Step 12:

[1302] The server sends the translated reply message to the parent or client's device. The translated reply message is entered into the server as input. The translated reply message is sent to the parent or client's device as output. Specifically, the server sends data using a REST API.

[1303] 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.

[1304] 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.

[1305] 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.

[1306] [Fourth embodiment]

[1307] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1308] 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.

[1309] 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).

[1310] 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.

[1311] 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.

[1312] 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).

[1313] 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. 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.

[1314] 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.

[1315] 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.

[1316] 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.

[1317] 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.

[1318] 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.

[1319] 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."

[1320] The present invention is a translation system for appropriately conveying documents from schools and educational institutions to parents, and realizes smooth communication beyond language barriers. The system of the present invention is specifically implemented as follows.

[1321] First, in terms of the system configuration, documents created by users (schools and educational institutions) are sent to a server via the Internet. The server then analyzes the contents of the document and translates it as necessary.

[1322] 1. Automatic translation of letters and contacts

[1323] The user (school or educational institution) creates a document in Japanese and sends it to the server. The server analyzes the received document and translates it into the target language using natural language processing technology. The translated document is then sent from the server to the parent's device, who can receive the document in a language they can understand.

[1324] Examples:

[1325] A user (school) creates and sends a notification saying, "There will be a sports day next Monday. Please bring your sportswear and water bottles."

[1326] The server receives this document and translates it into "Next Monday, we will have a sports day. Please bring sportswear and a water bottle."

[1327] The translated document will be sent to the parent's device.

[1328] 2. Explanation of specific words and phrases

[1329] When analyzing the content of the document, the server detects certain words and phrases. For these words and phrases, the server retrieves corresponding explanations from a database and provides them to the parent. The explanations are then translated into the target language and sent as an attachment to the original document.

[1330] Examples:

[1331] The server detects the word "sports day" and retrieves the description "sports day is a sporting event held at school" from the database.

[1332] The server adds the explanation "Sports day is a school event where students participate in various sports activities" to the document and sends it along with the translated document.

[1333] 3. Communication between both parties

[1334] When a parent sends a message in their native language, such as English, the message is received by the server. The server translates the received message into Japanese and sends it to the educational institution. The educational institution's reply is also sent to the server, where it is translated into the target language and then sent to the parent's device.

[1335] Examples:

[1336] A parent sends a message saying, "My child has a doctor's appointment on Friday. Will there be any important activities that day?"

[1337] The server translates this to "My child has a hospital appointment on Friday. Do you have any important activities on that day?" and sends it to the institution.

[1338] The educational institution replies, "There are no important activities on that day."

[1339] The server translates this as "There are no particularly important activities on that day" and sends it to the parent's device.

[1340] This system will ensure smooth communication between schools and educational institutions and foreign parents, and will ensure that necessary information is accurately conveyed to parents who do not understand Japanese.

[1341] The processing flow will be explained below.

[1342] Step 1:

[1343] A user (such as a school or educational institution) creates a document and sends it to the server. The document can include, for example, a letter or a contact list.

[1344] Step 2:

[1345] The server analyzes the Japanese document received from the user, taking in the document's contents in text format and extracting specific information from it.

[1346] Step 3:

[1347] The server translates the document content into the target language. The server uses natural language processing (NLP) technology to analyze the entire document and translate it appropriately. For example, it calls a translation API to translate "There will be a sports day next Monday." into "Next Monday, we will have a sports day."

[1348] Step 4:

[1349] The server detects specific words and phrases within a document, for example, event names such as "athletic meet" or "athletic festival" or special words.

[1350] Step 5:

[1351] The server retrieves from the database the description that corresponds to the detected word or phrase, for example, "Undokai is a sports event held at Japanese schools."

[1352] Step 6:

[1353] The server translates the retrieved description into the target language. For example, the description could be translated as "Sports day is an event held at Japanese schools where students participate in various sports activities."

[1354] Step 7:

[1355] The server adds an explanatory text to the translated document, for example, "Next Monday, we will have a sports day. (Sports day is an event held at Japanese schools where students participate in various sports activities)."

[1356] Step 8:

[1357] The server sends the translated document to the parent's device, allowing the parent to receive information in their own language.

[1358] Step 9:

[1359] The user (parent) enters questions or comments in their native language and sends them to the server from their device. For example, they send a message like, "My child has a doctor's appointment on Friday. Will there be any important activities that day?"

[1360] Step 10:

[1361] The server receives the message from the parent and translates it into Japanese, saying, "My child has a hospital appointment on Friday. Do you have any important activities on that day?"

[1362] Step 11:

[1363] The server sends the translated message to the educational institution, which can then understand the parent's question in Japanese.

[1364] Step 12:

[1365] The user (educational institution) enters a reply message in Japanese and sends it to the server. For example, the reply may say, "There are no particularly important activities on that day."

[1366] Step 13:

[1367] The server receives the reply message from the educational institution and translates it into the parent's native language, stating, "There are no particularly important activities on that day."

[1368] Step 14:

[1369] The server then sends the translated reply message to the parent's device, allowing the parent to receive a reply in their own language and respond appropriately.

[1370] As described above, each step of the process is carried out sequentially, and smooth and accurate communication between users is achieved through this series of processes.

[1371] Example 1

[1372] 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."

[1373] Traditionally, communication between schools and educational institutions and parents has often been hindered by language barriers. Important notices and event notifications may not be conveyed accurately to parents, leading to misunderstandings and inconvenience. Furthermore, questions and messages from parents may not be translated properly, making smooth communication with educational institutions difficult.

[1374] 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.

[1375] In this invention, the server includes means for receiving documents sent from a school or educational institution, means for analyzing the received documents, means for translating the received documents into a target language, means for sending the translation results to the parent's terminal, means for detecting specific words or phrases and obtaining corresponding explanatory text, means for translating the obtained explanatory text into the target language and sending the translation results together with the explanatory text to the parent's terminal, means for receiving messages from the parent and translating them into the language of the educational institution, means for sending the translated messages to the educational institution, means for receiving reply messages from the educational institution and translating them into the parent's target language, means for sending the translated reply messages to the parent's terminal, means for presenting received documents in a form that is easy for users to understand, and means for translating messages sent by users into the original language of the documents, thereby enabling smooth and accurate communication that transcends language barriers.

[1376] "Schools and educational institutions" are organizations and facilities established for the purpose of education, including daycare centers, kindergartens, elementary schools, junior high schools, high schools, universities, and vocational schools.

[1377] A "document" is written information or instructions in text form that is produced for the purpose of communication or record-keeping.

[1378] "Receiving" is the act of receiving information or data sent from another party.

[1379] A "server" is a central computer that processes data and provides services to other computers and devices over a network.

[1380] "Analysis" is the act of investigating and evaluating data and information in detail to clarify their meaning and structure.

[1381] "Target language" is the language into which the original document is to be translated in a translation task.

[1382] "Translation" is the act of converting content written in one language into another language.

[1383] A "terminal" is a device connected to a computer system that can be operated by a user.

[1384] "Specific words or phrases" refers to certain keywords or expressions that are important or frequently appear in a document or message.

[1385] An "explanatory text" is a piece of text that explains the meaning and usage of a particular word or phrase.

[1386] A "message" is a document or word sent between users to convey information.

[1387] A "database" is a collection of information that has been organized and stored so that the data can be efficiently searched and used.

[1388] "Machine learning" is a technology that uses algorithms and statistical models to allow computers to learn from experience and perform data analysis and predictions.

[1389] MODE FOR CARRYING OUT THE INVENTION

[1390] This invention is a translation system for properly conveying documents sent from schools and educational institutions to parents, realizing smooth communication beyond language barriers. This system is specifically implemented through the process of receiving, analyzing, translating, and transmitting data between a server, terminals, and users.

[1391] First, a user (school or educational institution) creates a document in Japanese and sends it to a server via the Internet from their terminal. The user can use a standard computer or mobile device to send the document using a web interface or a dedicated application.

[1392] The server parses the received document using a natural language processing library (e.g., spaCy or NLTK) to extract important keywords and phrases from the document.

[1393] The server then calls a translation service such as Google Translate API or DeepL to translate the analyzed document into the target language, and the translated document is sent to the parent's device, allowing the parent to receive the document in a language they understand.

[1394] For example, if a user creates a document stating, "We have a sports day next Monday. Please bring sportswear and a water bottle.", the server translates this into "Next Monday, we will have a sports day. Please bring sportswear and a water bottle." and sends it to the parent's device.

[1395] The server also detects specific words and phrases in the document and retrieves corresponding explanations from the database. The explanations are also translated into the target language and added to the original document before being sent to the parent's device. This process allows the parent to gain a deeper understanding of the document's contents.

[1396] For example, if the server detects the word "sports day," it retrieves the explanation "sports day is a school event where students participate in various sports activities" from the database, translates it into "sports day is a school event where students participate in various sports activities," and adds it to the document. The document with this additional explanation is then sent to the parent's device.

[1397] Furthermore, messages from parents are also exchanged via the server. When parents send messages in their native language, such as English, the server translates them into Japanese and sends them to the educational institution. Responses from the educational institution are also sent to the server, which translates them into the parent's target language. This ensures smooth two-way communication between the educational institution and parents.

[1398] Examples of specific prompts include:

[1399] "Please translate the Japanese sentence, 'There will be a sports day next Monday. Please bring your sportswear and water bottles.' into English."

[1400] "Write a sentence explaining the meaning of the word 'sports day'."

[1401] "Please translate the following English message into Japanese: 'My child has a doctor's appointment on Friday. Will there be any important activities that day?'"

[1402] This makes it possible to obtain appropriate translations and descriptions from generative AI models.

[1403] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1404] Step 1:

[1405] A user (school or educational institution) creates a document and sends it to the server from their device.

[1406] Specifically, a user uses word processing software or a dedicated application to create a document, such as "There will be a sports day next Monday. Please bring your sportswear and water bottle." The document is then uploaded to a server from the terminal via the Internet. The input is the document, and the output is the uploading of the document to the server.

[1407] Step 2:

[1408] The server parses the received document.

[1409] The server begins analyzing the received document using a natural language processing library (e.g., spaCy or NLTK). Specifically, it extracts important keywords and phrases (e.g., "sports day") from the document. The input is the received document, and the output is the extracted keywords and phrases.

[1410] Step 3:

[1411] The server translates the document into the target language.

[1412] The server calls a translation service such as Google Translate API or DeepL and translates the parsed document into the target language. For example, it translates "We will have a sports day next Monday. Please bring sportswear and a water bottle." into "Next Monday, we will have a sports day. Please bring sportswear and a water bottle." The input is the parsed document, and the output is the translated document.

[1413] Step 4:

[1414] The server sends the translation results to the parent's device.

[1415] The server sends the translated document to the designated parent's device. Specifically, the translated document is delivered to the parent via email or a dedicated application. The input is the translated document, and the output is the document sent to the parent's device.

[1416] Step 5:

[1417] The server detects specific words or phrases in the document and retrieves their corresponding descriptions from a database.

[1418] The server detects specific words or phrases (e.g., "sports day") from the parsed document and retrieves the description "sports day is a sporting event held at school" from the database. The input is the parsed document, and the output is the retrieved description.

[1419] Step 6:

[1420] The server translates the description into the target language and adds it to the original document.

[1421] The server translates the retrieved description into the target language using the Google Translate API or DeepL. Specifically, it translates "Sports day is a school event where students participate in various sports activities" into "Sports day is a school event where students participate in various sports activities" and adds it to the translated document. The input is the retrieved description, and the output is the translated document with the description added.

[1422] Step 7:

[1423] The server sends the translated document with explanatory text to the parent's device.

[1424] The server adds explanatory text to the translated document and sends it to the parent's device. Specifically, it presents it in a format that is easy for the parent to understand. The input is the translated document with explanatory text added, and the output is the translated document with explanatory text sent to the parent's device.

[1425] Step 8:

[1426] The server receives messages from parents and translates them into the institution's language.

[1427] For example, a parent sends a message from their device to the server, such as "My child has a doctor's appointment on Friday. Will there be any important activities that day?" The server translates this to "My child has a doctor's appointment on Friday. Will there be any important activities that day?" The input is the message from the parent, and the output is the message translated into the language of the educational institution.

[1428] Step 9:

[1429] The server sends the translated message to the institution.

[1430] The server sends the translated message to the educational institution's terminal. The specific operation is to provide information in a form that the educational institution can easily understand. The input is the translated message, and the output is the message sent to the educational institution's terminal.

[1431] Step 10:

[1432] The server receives the reply message from the educational institution and translates it into the parent's target language.

[1433] The institution sends a reply to the server, for example, "There are no particularly important activities on that day." The server translates this to "There are no particularly important activities on that day." The input is the reply message from the institution, and the output is the translated reply message.

[1434] Step 11:

[1435] The server sends the translated reply message to the parent's device.

[1436] The server sends the translated reply message to the parent's device. Specifically, the server provides the translated reply message in a format that the parent can easily check. The input is the translated reply message, and the output is the message sent to the parent's device.

[1437] (Application example 1)

[1438] 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."

[1439] The present invention relates to a system for accurately and quickly transmitting order information between customers who speak different languages ​​and restaurants, thereby eliminating language barriers in food delivery services.

[1440] Specifically, there is a need for a means that allows customers to place orders in their own language, ensures that the restaurant accurately understands the content of the orders, and also allows the restaurant to properly convey order confirmation messages to customers.

[1441] 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.

[1442] In this invention, the server includes means for receiving a document sent from a school or educational institution, means for translating the received document into a target language, means for transmitting the translation result to the parent's terminal, means for detecting specific words or phrases and obtaining corresponding explanatory text, means for translating the obtained explanatory text into the target language and transmitting the translation result together with the explanatory text to the parent's terminal, means for receiving a message from the parent and translating it into the language of the educational institution, means for transmitting the translated message to the educational institution, means for receiving a reply message from the educational institution and translating it into the parent's target language, means for transmitting the translated reply message to the parent's terminal, means for receiving order information from a customer and translating it into the restaurant's language, means for transmitting the translated order information to the restaurant, means for receiving order confirmation information from the restaurant and translating it into the customer's target language, and means for transmitting the translated order confirmation information to the customer's terminal. This enables order information to be communicated accurately and quickly between customers and restaurants, eliminating language barriers.

[1443] "Schools and educational institutions" refers to organizations and facilities for carrying out educational activities, such as elementary schools, junior high schools, high schools, universities, and vocational schools.

[1444] "Document" means any paper or digital file containing textual information, including notices, correspondence, reports, etc.

[1445] "Translation means" refers to the methods and techniques used to convert a document written in one language into another language.

[1446] "Parental devices" refers to electronic devices such as computers, smartphones, and tablets used by parents of children or students.

[1447] "Specific words and phrases" refer to specific keywords and expressions in a document, and are words and phrases that contain important information that requires explanation.

[1448] An "explanation" is text information that provides detailed explanations of the meaning and background of specific words or phrases.

[1449] "Customer" means the person or entity placing an order to purchase or use a service or product.

[1450] "Order Information" means the requests and details provided by Customer when purchasing goods or services.

[1451] "Food and beverage establishments" refers to establishments and restaurants that serve food and beverages.

[1452] "Order confirmation information" is information that a restaurant sends back after confirming the order details received from a customer.

[1453] System Configuration

[1454] The present invention is a translation system for accurately and quickly communicating order information between customers and restaurants, and includes the following components: a server, a customer terminal, and a restaurant terminal, all of which work in conjunction with one another.

[1455] Hardware and Software

[1456] The hardware used is as follows:

[1457] Server (e.g., Amazon Web Services EC2 instance)

[1458] Customer and restaurant devices (e.g., smartphones, tablets, computers)

[1459] The software used is as follows:

[1460] Server-side framework: Flask (a Python micro web framework)

[1461] Translation engine: Google Translate API

[1462] Natural language processing techniques and machine learning algorithms: Machine learning models (e.g., BERT, GPT-3)

[1463] Data processing and calculation flow

[1464] The server processes the data according to the following procedure.

[1465] 1. Document Receipt and Translation:

[1466] The server receives the order information entered on the customer's device. For example, the sentence "I would like to order a large pizza and a Coke" is sent.

[1467] The server uses the Google Translate API to translate this order information from the customer's native language into the restaurant's language.

[1468] 2. Sending Order Information:

[1469] When the translation result is "I would like to order a large pizza and a Coke," it is sent to the restaurant's terminal.

[1470] 3. Receiving and translating order confirmation messages:

[1471] Receive order confirmation from the restaurant terminal, such as "We have received your order for a large pizza and a Coke."

[1472] The server uses the Google Translate API to translate this confirmation information from the restaurant's language into the customer's native language.

[1473] 4. Sending confirmation information:

[1474] Once the translation result is "Your order for a large pizza and a Coke has been accepted," it is sent to the customer's terminal.

[1475] Specific examples

[1476] For example, if an English-speaking customer places an order by saying, "I would like to order a large pizza and a Coke," this order information is translated and transmitted as follows:

[1477] "I would like to order a large pizza and a Coke."

[1478] "I'd like to order a large pizza and a Coke."

[1479] Similarly, a confirmation message from a restaurant saying "We have received your order for a large pizza and a Coke" would be translated and sent as follows:

[1480] "Your order for a large pizza and a Coke has been accepted."

[1481] Translated into:English "We have received your order for a large pizza and a Coke."

[1482] Prompt Sentence Examples

[1483] English prompt:

[1484] "I would like to order a large pizza and a Coke."

[1485] Translated Japanese:

[1486] "I'd like to order a large pizza and a Coke."

[1487] In this way, the system of the present invention allows customers to input orders in their native language and accurately translates them into the restaurant's language, enabling smooth communication of order information across language barriers. By using the Google Translate API as the translation engine and performing detailed data processing on the server side, accurate and prompt communication between customers and restaurants is achieved.

[1488] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1489] Step 1:

[1490] A user inputs order information into a terminal. The input information is text data such as "I would like to order a large pizza and a Coke." This information is in the customer's native language and is sent from the terminal to the server.

[1491] Step 2:

[1492] The server receives the order information from the customer's device. The input is in the customer's native language (e.g., "I would like to order a large pizza and a Coke."), and this information is converted for processing within the server.

[1493] Step 3:

[1494] The server translates the order information using the Google Translate API. The input is the order information in English, the customer's native language, and the output is the order information translated into the restaurant's language (e.g., "I'd like to order a large pizza and a Coke."). The server accesses the API, converts the text data, and obtains the translation results.

[1495] Step 4:

[1496] The server sends the translated order information to the restaurant's terminal. The input is the translated order information (e.g., "I'd like to order a large pizza and a Coke."), and the output is the data sent to the restaurant's terminal. The server sends the information using a network protocol.

[1497] Step 5:

[1498] The restaurant terminal receives the order information and generates a confirmation message. The input is the translated order information received from the server, and the output is a confirmation message written in the restaurant's language (e.g., "We have received your order for a large pizza and a Coke."). The restaurant terminal may automatically generate this message, or a staff member may type it in.

[1499] Step 6:

[1500] The server receives an order confirmation message from the restaurant terminal. The input is the confirmation message in the restaurant's language (e.g., "We have received your order for a large pizza and a Coke."), and the output is retained for processing within the server.

[1501] Step 7:

[1502] The server translates the confirmation message using the Google Translate API. The input is the confirmation message in the restaurant's language, and the output is the confirmation message translated into the customer's native language (e.g., "Your order for a large pizza and a Coke has been accepted."). The server accesses the API, converts the text data, and obtains the translation result.

[1503] Step 8:

[1504] The server sends a translated confirmation message to the customer's device. The input is the translated confirmation message (e.g., "Your order for a large pizza and a Coke has been accepted.") and the output is the data sent to the customer's device. The server sends the information using a network protocol.

[1505] This series of processes enables smooth communication between customers who speak different languages ​​and restaurants, allowing for accurate and prompt ordering and confirmation.

[1506] 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.

[1507] This invention is a system for facilitating communication between schools and educational institutions and parents, and in particular provides technology for accurately conveying information across language barriers. A key feature of this invention is that it recognizes the user's emotions by combining an emotion engine and applies a translation tone accordingly. The processing flow of this system will now be described in detail.

[1508] 1. Automatic translation of letters and contacts

[1509] A user (school or educational institution) creates a document in Japanese and sends it to the server. The server analyzes the received document and uses natural language processing technology to translate the content into the target language. The translated document is then sent from the server to the parent's device, who can receive the document in a language they can understand. The emotion engine can also analyze the emotion contained in the document and adjust the translation tone accordingly.

[1510] Examples:

[1511] A user (school) creates and sends a notification saying, "There will be a sports day next Monday. Please bring your sportswear and water bottles."

[1512] The server receives this document and translates it into "Next Monday, we will have a sports day. Please bring sportswear and a water bottle."

[1513] The emotion engine recognizes the tone of the document as "cheerful" and reflects this in the translation.

[1514] The translated document will be sent to the parent's device.

[1515] 2. Explanation of specific words and phrases

[1516] When analyzing the content of a document, the server detects certain words and phrases. For these words and phrases, the server retrieves corresponding explanations from a database and provides them to parents as part of the translation. After the explanations are translated into the target language, they are added to the original document.

[1517] Examples:

[1518] The server detects the word "sports day" and retrieves the description "sports day is a sports event held at Japanese schools" from the database.

[1519] The server translates the description into "Sports day is an event held at Japanese schools where students participate in various sports activities."

[1520] The server responds to the document with the following: "Next Monday, we will have a sports day. Please bring sportswear and a water bottle. (Sports day is an event held at Japanese schools where students participate in various sports activities)."

[1521] 3. Communication between both parties

[1522] When a parent sends a message in their native language, the message is received by the server. The server translates the received message into Japanese and sends it to the educational institution. Reply messages are similarly translated and sent to the parent's device in their native language. The server uses an emotion engine to recognize the emotions contained in the parent's and educational institution's messages and applies the corresponding translation tone.

[1523] Examples:

[1524] A parent sends a message saying, "My child has a doctor's appointment on Friday. Will there be any important activities that day?"

[1525] The server translates this to "My child has a hospital appointment on Friday. Do you have any important activities on that day?" and sends it to the institution.

[1526] The emotion engine recognizes the emotion of the question as "concern" and adjusts the tone of the reply appropriately.

[1527] The educational institution replies, "There are no important activities on that day."

[1528] The server translates this as "There are no particularly important activities on that day" and sends it to the parent's device.

[1529] This invention enables smooth and accurate communication that transcends language barriers, deepening understanding between parents and educational institutions. By utilizing an emotion engine, the content of messages is conveyed appropriately, making dialogue even smoother.

[1530] The processing flow will be explained below.

[1531] Step 1:

[1532] A user (school or educational institution) creates a document and sends it to the server. For example, a user creates a notification saying, "We have a sports day next Monday. Please bring your sports clothes and water bottles."

[1533] Step 2:

[1534] The server analyzes the Japanese document received from the user, captures the document content in text format, and prepares it for translation.

[1535] Step 3:

[1536] The server uses natural language processing technology to translate the content of the document into the target language. For example, when translating a Japanese document into English, the server converts it into "Next Monday, we will have a sports day. Please bring sportswear and a water bottle."

[1537] Step 4:

[1538] The server detects specific words and phrases within a document, for example recognizing words like "sports day" and "sportswear."

[1539] Step 5:

[1540] The server retrieves from the database the description that corresponds to the detected word or phrase, for example, "Undokai is a sports event held at Japanese schools."

[1541] Step 6:

[1542] The server translates the obtained description into the target language. For example, "Sports day is an event held at Japanese schools where students participate in various sports activities."

[1543] Step 7:

[1544] The server generates a document with the translated explanation added. For example, the server adds an explanation to the translated document, such as "Next Monday, we will have a sports day. Please bring sportswear and a water bottle. (Sports day is an event held at Japanese schools where students participate in various sports activities)."

[1545] Step 8:

[1546] The server uses an emotion engine to analyze the emotion contained in the document, for example, recognizing the document's tone as "cheerful."

[1547] Step 9:

[1548] The server adjusts the translation tone based on the emotion recognition results. For example, it sets an appropriate translation tone and provides the document to the parent as a cheerful document.

[1549] Step 10:

[1550] The server sends the translated document to the parent's device, where the parent can receive the document in a language they understand.

[1551] Step 11:

[1552] The user (parent) creates a message in their native language and sends it to the server from their device. For example, they send a question such as, "My child has a doctor's appointment on Friday. Will there be any important activities that day?"

[1553] Step 12:

[1554] The server receives the message from the parent and uses natural language processing technology to translate it into Japanese, for example, "My child has a hospital appointment on Friday. Are there any important activities on that day?"

[1555] Step 13:

[1556] The server sends the translated message to the educational institution, which can then receive the parent's questions in Japanese.

[1557] Step 14:

[1558] The server uses an emotion engine to analyze the emotion contained in the parent's message. For example, the emotion engine recognizes "worry."

[1559] Step 15:

[1560] The user (educational institution) creates a reply message in Japanese and sends it to the server. For example, they reply, "There are no particularly important activities on that day."

[1561] Step 16:

[1562] The server receives the reply message from the educational institution and translates it into the target language, for example, "There are no particularly important activities on that day."

[1563] Step 17:

[1564] The server uses an emotion engine to analyze the emotion contained in the educational institution's reply message, for example recognizing "a sense of relief."

[1565] Step 18:

[1566] The server adjusts the translation tone based on the emotion recognition result, and applies an appropriate tone to provide the translated message to the parent.

[1567] Step 19:

[1568] The server sends the translated reply message to the parent's device, who can then receive the reply in their own language through the device.

[1569] As described above, processing is performed at each step, ensuring smooth communication between users.

[1570] Example 2

[1571] 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."

[1572] Language barriers can be an obstacle when communicating between schools and parents, making it difficult to convey information accurately. Furthermore, the quality of communication can decline if emotional nuances are not conveyed properly when documents are translated. Furthermore, replies and inquiries from parents cannot be handled quickly and accurately.

[1573] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1574] In this invention, the server includes a means for receiving a document sent from a school or educational institution, a means for translating the received document into a target language, and a means for analyzing the emotion of the document using an emotion recognition engine and adjusting the translation tone. This enables accurate communication that transcends language barriers and incorporates emotional nuances. The server also includes a means for retrieving specific words or phrases from a database and translating the corresponding explanatory text into the target language, a means for translating the retrieved explanatory text into the target language and sending the translation result to the parent's terminal, a means for receiving a message from the parent and translating it into the language of the educational institution, a means for sending the translated message to the educational institution, a means for receiving a reply message from the educational institution and translating it into the parent's target language, and a means for sending the translated reply message to the parent's terminal. This system enables rapid and accurate two-way communication.

[1575] "Schools and educational institutions" is a general term for organizations that provide education to children and students.

[1576] "Document" refers to documents or text data that express information using characters and diagrams.

[1577] A "server" refers to a computer system that provides services and data in response to client requests over a network.

[1578] "Target language" refers to a language other than Japanese that is designated as the target language for translation.

[1579] "Translation means" refers to a function that converts a document written in one language into another language.

[1580] An "emotion recognition engine" refers to a software system that analyzes the emotions and tone in a document and adjusts the appropriate translation tone based on the results.

[1581] A "terminal" refers to a device, such as a PC or smartphone, that allows a user to receive or send information over a network.

[1582] "Specific words and phrases" refer to words or expressions that are considered particularly important in the document to be translated.

[1583] A "database" refers to a system that organizes and stores specific data so that it can be easily searched and retrieved.

[1584] "Parent" refers to a parent or legal guardian of a child or student, or any other adult who is required to communicate with an educational institution.

[1585] A "machine learning algorithm" refers to a computational method that learns patterns from data and uses those patterns to predict or classify new data.

[1586] The present invention provides a system for facilitating communication between schools or educational institutions and parents, and in particular provides technology for accurately transmitting information across language barriers. Specific embodiments of this system are described in detail below.

[1587] Hardware and software used

[1588] Server: Use a high-performance network server, such as an Apache or Nginx server.

[1589] Device: A device used by users and parents. This primarily refers to devices that can connect to the internet, such as smartphones, tablets, and PCs.

[1590] Translation API: Used to automatically translate documents, specifically Google Translate API and Microsoft Translator.

[1591] Natural language processing libraries: Used for document analysis and sentiment recognition. Examples include the nltk library and spaCy.

[1592] Machine learning models, such as OpenAI's GPT model, are used to analyze the sentiment of a document and adjust the translation tone.

[1593] Explanation of system processing

[1594] 1. Sending documents

[1595] Users (schools and educational institutions) create letters and contacts in Japanese, and the documents are sent to the server via a specified API endpoint.

[1596] Example: A user creates a notification that says, "We have a sports day next Monday. Please bring your sports clothes and water bottle."

[1597] 2. Document analysis on the server

[1598] The server analyzes the received document, tokenizing it using a natural language processing library (e.g., nltk, spaCy) and performing syntax and sentiment analysis.

[1599] 3. Translation Processing

[1600] Based on the analysis results, the server identifies the target language and calls a translation API (e.g., Google Translate API) to translate the document into the target language, using an emotion recognition engine to analyze the sentiment of the document and adjust the appropriate translation tone.

[1601] Example: The server translates the sentence "There will be a sports day next Monday." into "Next Monday, we will have a sports day.", and the emotion engine determines that this sentence is "cheerful."

[1602] 4. Sending translation results

[1603] The server converts the translated text into packets and sends them to the parent's device, which interprets the received packets and displays them on the screen.

[1604] 5. Processing responses from parents

[1605] Parents can create a reply message in their own language and send it to the server. The server then translates the message and sends it to the educational institution. The educational institution's reply is also translated via the server and sent to the parent's device.

[1606] Examples of concrete examples and prompts

[1607] Example: A parent sends a message saying, "My child has a doctor's appointment on Friday. Will there be any important activities that day?" The server translates this to "My child has a doctor's appointment on Friday. Will there be any important activities that day?" and sends it to the educational institution. The educational institution replies, "There are no particularly important activities on that day," which the server translates to "There are no particularly important activities on that day" and sends it to the parent's device.

[1608] Example prompt sentence:

[1609] "We have a sports day next Monday. Please bring your sports clothes and water bottles."

[1610] "My child has a doctor's appointment on Friday. Will there be any important activities that day?"

[1611] In this way, the system of the present invention works in combination with users, servers, and terminals to provide accurate and efficient communication across language barriers. By utilizing an emotion recognition engine and machine learning algorithms, the tone and emotion of the message are also reflected appropriately.

[1612] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1613] Step 1:

[1614] A user creates a document and submits it to the server.

[1615] Specifically, the user (school or educational institution) creates a letter or contact list in Japanese. The created document is sent to the server via the API endpoint.

[1616] Input: Document written in Japanese

[1617] Output: The document sent to the server

[1618] Step 2:

[1619] The server receives and parses the document.

[1620] Specifically, the server analyzes the received document using a natural language processing library (e.g., nltk, spaCy), which includes tokenization, syntactic analysis, and sentiment analysis.

[1621] Input: Received Japanese document

[1622] Output: Analysis results (tokenized data, syntactic information, sentiment analysis results)

[1623] Step 3:

[1624] The server translates the received document as needed.

[1625] Specifically, the server calls a translation API (e.g., Google Translate API) based on the analysis results to translate the document into the target language, using an emotion recognition engine to analyze the emotion of the document and adjust the appropriate translation tone.

[1626] Input: Analysis results, Japanese document

[1627] Output: Document translated into target language

[1628] Step 4:

[1629] The server uses an emotion engine to analyze the sentiment of the document and adjust the translation tone.

[1630] Specifically, the server uses an emotion recognition engine (e.g., OpenAI GPT model) to analyze the emotions in the document and adjust the translation tone based on the results.

[1631] Input: translated document, sentiment analysis results

[1632] Output: A translated document with adjusted tone

[1633] Step 5:

[1634] The server sends the translation results to the parent's device.

[1635] Specifically, the server converts the translated document into packets and sends them to the parent's device, which then interprets the received packets and displays them on the screen.

[1636] Input: translated document

[1637] Output: Document sent to parent's device

[1638] Step 6:

[1639] Parents review the documents they receive.

[1640] Specifically, the parent's terminal receives packets from the server and displays the document.

[1641] Input: Document sent to parent's device

[1642] Output: Document that parents can view on the screen

[1643] Step 7:

[1644] The parent creates a reply message and sends it to the server.

[1645] Specifically, the parent composes a reply message on the device, types it in their native language, and sends the message to the server.

[1646] Input: A message in your native language created by the parent

[1647] Output: Reply message sent to the server

[1648] Step 8:

[1649] The server translates messages from parents and sends them to the educational institution.

[1650] Specifically, the server receives messages from parents, calls a translation API to translate the messages into Japanese, and then sends the translation results to the educational institution.

[1651] Input: Parental Message

[1652] Output: Japanese translated message

[1653] Step 9:

[1654] The educational institution will review and respond to the message.

[1655] Specifically, the educational institution checks the message received from the server, creates a reply message in Japanese if necessary, and sends the created message to the server.

[1656] Input: Japanese translated message

[1657] Output: Reply message written in Japanese

[1658] Step 10:

[1659] The server translates the response from the educational institution and sends it to the parent.

[1660] Specifically, the server receives the reply from the educational institution, translates the message into the parent's native language using a translation API, converts the translated result into a packet format, and sends it to the parent's device.

[1661] Input: Reply message from educational institution

[1662] Output: Reply message translated into the parent's native language

[1663] Through the above processing steps, the system can provide accurate and efficient communication across language barriers.

[1664] (Application example 2)

[1665] 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."

[1666] Language barriers exist in communication between schools or educational institutions and parents, or between security services and multinational clients, resulting in problems with information not being transmitted accurately and promptly. Misunderstandings and mistranslations are particularly likely in emergencies or when emotions are strong, posing a risk of reduced credibility and adversely affecting actual crisis response. The present invention aims to solve these problems and provide smooth and accurate communication across language barriers.

[1667] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving a document sent from a school or educational institution; means for translating the received document into a target language; means for transmitting the translation result to the parent's terminal; means for detecting specific words or phrases and obtaining corresponding explanatory text; means for translating the obtained explanatory text into the target language and transmitting the translation result together with the explanatory text to the parent's terminal; means for receiving a message from the parent and translating it into the language of the educational institution; means for transmitting the translated message to the educational institution; means for receiving a reply message from the educational institution and translating it into the parent's target language; and means for analyzing the emotion contained in the message during translation and adjusting the translation tone accordingly. This allows smooth and accurate communication between the two parties, and enables information to be communicated in an appropriate tone, especially in emergencies when emotions are strongly expressed.

[1668] "Document receiving means" refers to a means for receiving documents sent from a school, educational institution, or security service.

[1669] A "translation means" is a means for translating a received document into a target language.

[1670] "Transmission means" refers to the means for transmitting the translated document to the parent or client's terminal.

[1671] A "specific word detection means" is a means for detecting specific words or phrases within a document.

[1672] The "explanation acquisition means" is a means for acquiring an explanation corresponding to a detected specific word or phrase from a database.

[1673] "Sentiment analysis means" is a means for analyzing the emotions contained in a message and adjusting the translation tone according to the results.

[1674] The "means for receiving a parental message" is a means for receiving a message from a parent.

[1675] "Educational institution language translation means" refers to a means for translating messages received from parents into the language of the educational institution.

[1676] The "means for receiving a reply message" is a means for receiving a reply message from an educational institution.

[1677] "Machine learning algorithms" are algorithms used to improve translation accuracy.

[1678] The "translation tone adjustment means" is a means for adjusting the tone of a translated message based on the results of sentiment analysis.

[1679] To implement this invention, a system is constructed that links multiple terminals and a server owned by schools, educational institutions, parents, and security services. The program processing procedures of this system and the hardware and software used are described in detail below.

[1680] System Overview:

[1681] The system consists of various devices (educational institution devices, parent devices, and security service devices) and a server. The server is equipped with a document receiving means, a translation means, a sending means, a specific word detection means, a description acquisition means, a sentiment analysis means, a parent message receiving means, an educational institution language translation means, and a reply message receiving means.

[1682] Hardware and software used:

[1683] Hardware: smartphones, laptops, servers, etc.

[1684] Software: Python 3.x, Googletrans library (translation function), TextBlob (sentiment analysis), REST API (HTTP communication).

[1685] Internet connection: Required to enable system-wide communication.

[1686] Data processing and calculation details:

[1687] 1. Document Receipt Method:

[1688] The server receives documents sent by educational institutions and security services, including event notifications and emergency reports.

[1689] 2. Translation Methods:

[1690] Translates the received document into the target language using the Python GoogleTrans library. If the document is in Japanese, it will be translated into a specific language (e.g., English or Chinese).

[1691] 3. Sentiment analysis tools:

[1692] Using Natural Language Processing (NLP) techniques, the TextBlob library analyzes the sentiment contained in a document and adjusts the translation tone accordingly.

[1693] 4. Specific word detection method:

[1694] The server detects specific words and phrases and retrieves their corresponding descriptions from a database, in accordance with US standards.

[1695] 5. Description acquisition method:

[1696] For each detected word or phrase, an explanation is retrieved from the database and translated into the target language.

[1697] 6. Means of transmission:

[1698] The translated document and explanation are sent to the parent or client's device. Communication is carried out using a REST API.

[1699] 7. Parental Message Reception Method:

[1700] Receive messages from parents and clients, translate them into the language of the educational institution or security service, and send reply messages in the same way.

[1701] Examples and prompts:

[1702] For example, if a client sends you a message saying, "My security alarm went off unexpectedly," use the following prompt to apply the appropriate translation tone:

[1703] Translate the following English emergency message into Japanese with the appropriate tone: "My security alarm went off unexpectedly." Ensure the translated message reflects a serious tone appropriate for an emergency situation.

[1704] In this way, the server analyzes the sentiment of the received message and sends a translated message with the appropriate tone to educational institutions and security services, enabling accurate and prompt communication across language barriers.

[1705] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1706] Step 1:

[1707] The server receives documents sent from schools, educational institutions, or security services. As input, document data is sent to the server. As output, the received document data is stored within the server. Specifically, the server receives the document data over the network using the standard HTTP protocol.

[1708] Step 2:

[1709] The server translates the received document into the target language. As input, the received document data and target language information are provided to the server. As output, a document translated into the target language is generated. Specifically, the server translates the document using Python's Google Trans library.

[1710] Step 3:

[1711] The server analyzes the sentiment of the document and adjusts the translation tone accordingly. As input, the translated document is fed into the server. As output, a translated document with an adjusted tone is generated. Specifically, the server uses the TextBlob library to analyze the sentiment of the document and apply a tone adjustment algorithm.

[1712] Step 4:

[1713] The server detects specific words or phrases in a document and retrieves their corresponding descriptions from a database. The translated document is entered into the server as input. The corresponding descriptions are obtained as output. Specifically, the server uses Natural Language Processing (NLP) techniques to detect specific words and executes a database query.

[1714] Step 5:

[1715] The server translates the acquired description into the target language. The acquired description is input to the server as input. The translated description into the target language is generated as output. Specifically, the server again translates the description using the GoogleTrans library.

[1716] Step 6:

[1717] The server sends the translated document and explanatory text to the parent or client device. As input, the adjusted translated document and explanatory text are entered into the server. As output, the translated document and explanatory text are sent to the parent or client device. Specifically, the server sends data using a REST API.

[1718] Step 7:

[1719] The server receives messages from guardians and clients. As input, messages sent from guardians and clients are input to the server. As output, the received messages are stored in the server. In concrete terms, the server receives and stores messages via the network.

[1720] Step 8:

[1721] The server translates the received message into the language of the educational institution or security service. As input, the server is provided with the received message and information about the target language. As output, the translated message in the target language is generated. Specifically, the server uses the GoogleTrans library to translate the message.

[1722] Step 9:

[1723] The server sends the translated message to the educational institution or security service. As input, the translated message is entered into the server. As output, the translated message is sent to the educational institution or security service's terminal. In concrete terms, the server sends data using a REST API.

[1724] Step 10:

[1725] The server receives a reply message from the educational institution or security service. As input, the reply message sent from the educational institution or security service is input to the server. As output, the received reply message is stored in the server. In concrete terms, the server receives the reply message via the network.

[1726] Step 11:

[1727] The server translates the received reply message into the language of the parent or client. As input, the received reply message and target language information are provided to the server. As output, a reply message translated into the target language is generated. Specifically, the server translates the reply message using the GoogleTrans library.

[1728] Step 12:

[1729] The server sends the translated reply message to the parent or client's device. The translated reply message is entered into the server as input. The translated reply message is sent to the parent or client's device as output. Specifically, the server sends data using a REST API.

[1730] 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.

[1731] 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.

[1732] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1733] 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.

[1734] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1735] 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.

[1736] 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).

[1737] 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.

[1738] 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."

[1739] 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.

[1740] 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).

[1741] 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.

[1742] 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.

[1743] 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.

[1744] 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.

[1745] 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.

[1746] 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.

[1747] 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.

[1748] 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.

[1749] 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.

[1750] 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.

[1751] The following is further disclosed regarding the above embodiment.

[1752] (Claim 1)

[1753] A means of receiving documents sent by schools and educational institutions;

[1754] means for translating the received document into a target language;

[1755] A means for transmitting the translation result to the parent's device;

[1756] A means for detecting specific words and phrases and obtaining their corresponding descriptions;

[1757] A means for translating the acquired explanatory text into a target language and transmitting the translation result to the parent's device;

[1758] A means of receiving messages from parents and translating them into the institution's language;

[1759] a means for transmitting the translated message to the educational institution;

[1760] a means for receiving reply messages from the educational institution and translating them into the parent's target language;

[1761] The system includes a means for sending a translated reply message to the parent's device.

[1762] (Claim 2)

[1763] 10. The system of claim 1, wherein the system retrieves specific words or phrases from a database and translates the descriptions into a target language.

[1764] (Claim 3)

[1765] 10. The system of claim 1, wherein the system uses machine learning algorithms to improve translation accuracy.

[1766] "Example 1"

[1767] (Claim 1)

[1768] A means of receiving documents sent by schools and educational institutions;

[1769] means for parsing the received document;

[1770] means for translating the received document into a target language;

[1771] A means for transmitting the translation result to the parent's device;

[1772] A means for detecting specific words and phrases and obtaining their corresponding descriptions;

[1773] A means for translating the acquired explanatory text into a target language and transmitting the translation result to the parent's device;

[1774] A means of receiving messages from parents and translating them into the institution's language;

[1775] a means for transmitting the translated message to the educational institution;

[1776] a means for receiving reply messages from the educational institution and translating them into the parent's target language;

[1777] A means for sending the translated reply message to the parent's device;

[1778] A means for presenting the received document in a form that is easy for the user to understand;

[1779] A system that includes a means for translating user-submitted messages into the source language of the document.

[1780] (Claim 2)

[1781] 10. The system of claim 1, wherein the system retrieves specific words or phrases from a database and translates the descriptions into a target language.

[1782] (Claim 3)

[1783] 10. The system of claim 1, wherein the system uses machine learning algorithms to improve translation accuracy.

[1784] "Application Example 1"

[1785] (Claim 1)

[1786] A means of receiving documents sent by schools and educational institutions;

[1787] means for translating the received document into a target language;

[1788] A means for transmitting the translation result to the parent's device;

[1789] A means for detecting specific words and phrases and obtaining their corresponding descriptions;

[1790] A means for translating the acquired explanatory text into a target language and transmitting the translation result to the parent's device;

[1791] A means of receiving messages from parents and translating them into the institution's language;

[1792] a means for transmitting the translated message to the educational institution;

[1793] a means for receiving reply messages from the educational institution and translating them into the parent's target language;

[1794] A means for sending the translated reply message to the parent's device;

[1795] a means for receiving order information from a customer and translating the order information into the restaurant's language;

[1796] a means for transmitting the translated order information to the restaurant;

[1797] a means for receiving an order confirmation from the restaurant and translating the order confirmation into the customer's target language;

[1798] The system includes a means for transmitting the translated order confirmation information to the customer's terminal.

[1799] (Claim 2)

[1800] 10. The sy...

Claims

1. A means of receiving documents sent by schools and educational institutions; means for translating the received document into a target language; A means for transmitting the translation result to the parent's device; A means for detecting specific words and phrases and obtaining their corresponding descriptions; A means for translating the acquired explanatory text into a target language and transmitting the translation result to the parent's device; A means of receiving messages from parents and translating them into the institution's language; a means for transmitting the translated message to the educational institution; a means for receiving reply messages from the educational institution and translating them into the parent's target language; The system includes a means for sending a translated reply message to the parent's device.

2. 10. The system of claim 1, wherein the system retrieves specific words or phrases from a database and translates their descriptions into a target language.

3. The system of claim 1 , wherein the system uses machine learning algorithms to improve translation accuracy.

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