System

Abstract

A system is provided.SOLUTION: A multilingual business reporting system comprising: means for a user to input business reports in a native language; means for a terminal to transmit business report data to a server; means for the server to receive and store the transmitted business report data; translation engine means for the server to translate the business report data into another language; and means for the server to distribute the translated business report data in an appropriate language for each member.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Multinational teams are becoming commonplace in modern business environments, but language barriers pose a significant obstacle when it comes to sending business reports. Translation is necessary for members with different native languages ​​to communicate smoothly and share information about work progress and issues. However, manual translation methods pose problems in terms of time and accuracy. This has led to a demand for automated translation systems that support multiple languages, but conventional systems have struggled to accurately translate and distribute business reports in real time while supporting multiple languages. The present invention aims to solve these problems. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by the following means: A multilingual business report system includes a means for a user to input a business report in their native language, a means for a terminal to transmit the business report data to a server, a means for the server to receive and store the transmitted business report data, a translation engine for the server to translate the business report data into other languages, and a means for the server to distribute the translated business report data in an appropriate language for each member. This allows users to submit business reports in their native language, and other members can view the report content in a language they understand, enabling communication that overcomes language barriers. Furthermore, the server includes a database for storing the translation results and a means for acquiring business report data based on a language selected by the user, thereby realizing an efficient and accurate multilingual business report system.

[0006] "User" refers to the person who operates the system and inputs business reports.

[0007] The term "terminal" refers to an electronic device used by a user that provides an interface for transmitting business report data to a server.

[0008] "Server" refers to the central facility that receives, stores, translates, and distributes business report data to other members in the appropriate language.

[0009] "Business report data" refers to data containing information about business operations that is input by a user and sent by a terminal to a server.

[0010] "Translation Engine" refers to software or a system that resides in a server and that translates business report data into different languages.

[0011] "Multilingual support" refers to the ability of a system to support multiple different languages ​​and provide information in the appropriate language.

[0012] "Database" refers to a storage device used by the server to store business report data and translation results.

[0013] "Distribution" refers to the act of the server providing translated business report data to other members in the appropriate language.

[0014] A "request" refers to an action in which a terminal requests information from a server.

[0015] A "response" refers to information that a server returns in response to a terminal request. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention aims to realize a multilingual business reporting system, and will be described in detail in the following form. The entire system consists of three main components: users, terminals, and servers. This system allows multinational members to smoothly submit business reports across language barriers.

[0038] System Overview

[0039] In this system, users input business reports in their native language, and the server receives, translates, and distributes the reports. The translated results are then displayed in the appropriate language for each member.

[0040] User operations

[0041] The user opens the work report input screen on the terminal and inputs the work report in their native language. For example, they might input "Today's work involved the initial design of a new project" in Japanese. When the user presses the send button, the terminal sends the report data to the server. At this time, the report content and the language information used by the user are included.

[0042] Device behavior

[0043] The terminal sends the business report data entered by the user to the server. Specifically, the terminal uses an HTTP POST request to send the business report data to the server. This request includes the report content and the user's language information.

[0044] Server Operation

[0045] Receiving and storing report data

[0046] The server receives the business report data sent from the terminal. The received data is stored in a database. For example, suppose the server receives the following data:

[0047] json

[0048] {

[0049] "content": "Today's work involved initial design of a new project.",

[0050] "language": "ja"

[0051] }

[0052] The server temporarily stores this data in a database.

[0053] Using a translation engine

[0054] The server passes the saved business report data to the translation engine and requests translation. The translation engine translates the business report into multiple languages ​​(e.g., English, French, and Spanish). For example, when a Japanese report is translated into English, it becomes, "Today, I worked on the initial design of a new project."

[0055] Storage and distribution of translation results

[0056] The server stores the translated business reports in a database. The stored translation results are dynamically provided in response to user requests. For example, if a user who prefers English checks a report, the server provides the report translated into English.

[0057] Specific examples

[0058] Here are some concrete examples:

[0059] 1. User report input

[0060] What the user types: "Today, I worked on the initial design for a new project."

[0061] Language: Japanese

[0062] 2. Data transmission from the device

[0063] Data sent by the device:

[0064] json

[0065] {

[0066] "content": "Today's work involved initial design of a new project.",

[0067] "language": "ja"

[0068] }

[0069] 3. Server Reception and Translation

[0070] The server received:

[0071] json

[0072] {

[0073] "content": "Today's work involved initial design of a new project.",

[0074] "language": "ja"

[0075] }

[0076] Translation result (English): "Today, I worked on the initial design of a new project."

[0077] 4. Report Distribution

[0078] If the user selects English, the server provides the user with the following data:

[0079] json

[0080] {

[0081] "content": "Today, I worked on the initial design of a new project."

[0082] }

[0083] In this way, the system allows multinational members to easily submit business reports and have the contents of those reports confirmed in a language that other members can understand.

[0084] The processing flow will be explained below.

[0085] Step 1:

[0086] The user opens the work report input screen on the terminal. The user inputs the work report in their native language. For example, they input "Today's work involved the initial design of a new project" in Japanese.

[0087] Step 2:

[0088] The user completes the input and presses the send button. The terminal structures the input business report data. Specifically, it generates the following data structure:

[0089] json

[0090] {

[0091] "content": "Today's work involved initial design of a new project.",

[0092] "language": "ja"

[0093] }

[0094] Step 3:

[0095] The device sends this data to the server as an HTTP POST request, for example to the / report endpoint.

[0096] Step 4:

[0097] The server receives the POST request and extracts the report data and language information from the request body.

[0098] Step 5:

[0099] The server stores the received data in a database, including the report content and the original language information.

[0100] Step 6:

[0101] The server invokes the translation engine, which translates the report into the specified language (e.g., English, French, Spanish, etc.).

[0102] Step 7:

[0103] The translation engine receives the translation result. For example, in English, the translation result might be "Today, I worked on the initial design of a new project."

[0104] Step 8:

[0105] The server stores the translation results in a database, which includes the original text and the translated report content in each language.

[0106] Step 9:

[0107] The user opens the application on their device to view the report. The user selects the display language (e.g., English).

[0108] Step 10:

[0109] The device sends an HTTP GET request to the server based on the selected language, including the language parameter (e.g., / report?language=en).

[0110] Step 11:

[0111] The server retrieves the report in the requested language from its database.

[0112] Step 12:

[0113] The server returns the search results to the device as an HTTP response, which includes the translated report in the selected language.

[0114] Step 13:

[0115] The device displays the received report to the user. For example, it displays "Today, I worked on the initial design of a new project." in English.

[0116] In this way, the system realizes multilingual business reports through each step.

[0117] Example 1

[0118] 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."

[0119] In a work environment involving multinational members, language barriers are a major obstacle to smooth business reporting and information sharing. This can lead to misunderstandings and reduced business efficiency. Conventional systems rely on one language or require expensive translation services. To improve this situation, it is necessary to provide a user-friendly business reporting system that supports multiple languages ​​in real time.

[0120] 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.

[0121] In this invention, the server includes a means for transmitting data including user language information from the terminal to the server, a means for the server to retrieve and provide appropriate data from a database based on a user request, and a means for the terminal to use an HTTP POST request to transmit data to the server, thereby enabling efficient and smooth business reporting and information sharing among users of multiple nationalities.

[0122] "User" means any person or organization that accesses the system to enter business reports and receive information.

[0123] A "terminal" is a device such as a computer, smartphone, or tablet that a user uses to input business reports and send and receive data.

[0124] A "server" is a central computer system that receives and stores data sent from terminals, performs the necessary processing, and provides the results.

[0125] "Business report data" is text information in which a user describes the details of business activities, and is data that is processed after being entered into the system.

[0126] "Translation Engine" means software or services for automatically translating business report data into other languages.

[0127] "Storage" means storing the received data in a storage medium such as a database so that it can be accessed later.

[0128] "Translation engine means" refers to hardware or software that provides the function of translating business report data into multiple languages.

[0129] A "member" is an individual or group who has registered with the system to use the system to submit business reports and share information with other users.

[0130] "Appropriate language" refers to a language that each user can understand and that is used by the translation engine to provide translation results.

[0131] An "HTTP POST request" is a form of Internet communication protocol used by a terminal to send data to a server.

[0132] A "database" is an information management system that stores data in an organized manner and enables efficient access when needed.

[0133] A "generative model" is an artificial intelligence model used to generate new information, translations, etc. based on input data.

[0134] A "request" is an action or content of a user requesting a system to perform a specific operation or provide information.

[0135] The present invention relates to a multilingual business report system, which will be described in detail below. In this system, users input business reports in their native language, and the server receives, translates, and distributes the report content. The translation results are displayed in the appropriate language for each member.

[0136] User operations

[0137] The user opens the work report input screen on the device and enters the work report in their native language. For example, they might enter "Today's work involved the initial design of a new project" in Japanese. When the user presses the send button, the device sends this work report data to the server. At this time, the report content and the user's language information are also included.

[0138] Device behavior

[0139] The terminal sends the business report data entered by the user to the server using an HTTP POST request. The sent data includes the report content and the user's language information. For example, the following data is sent:

[0140] json

[0141] {

[0142] "content": "Today's work involved initial design of a new project.",

[0143] "language": "ja"

[0144] }

[0145] Server Operation

[0146] Receiving and storing report data

[0147] The server receives the business report data sent from the terminal and stores it in a database. For example, the server receives the following data:

[0148] json

[0149] {

[0150] "content": "Today's work involved initial design of a new project.",

[0151] "language": "ja"

[0152] }

[0153] The server runs SQL queries to store this data in a database.

[0154] Using a translation engine

[0155] The server passes the saved business report data to a translation engine and requests translation. For example, it uses a translation service such as Google (registered trademark) Translate API or DeepL API. It sends the following prompt to the translation engine:

[0156] "Today's work involved the initial design of a new project."

[0157] As a response from the translation engine, for example, you receive the English translation result "Today, I worked on the initial design of a new project."

[0158] Storage and distribution of translation results

[0159] The server stores the translated business reports in a database. For example, it executes the query "INSERT INTO translated_reports (report_id, content, language) VALUES (1, 'Today, I worked on the initial design of a new project.', 'en')". The stored translation results are dynamically provided in response to user requests. For example, if a user who prefers English checks a report, the server provides the report translated into English.

[0160] In this way, this system allows multinational members to easily submit work reports and have the report contents confirmed in a language that other members can understand. This system is realized by combining technologies such as HTTP POST requests, SQL databases, and translation APIs. In addition, appropriate translations are provided based on each user's language settings, enabling smooth work reports across language barriers.

[0161] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0162] Step 1:

[0163] The user inputs a business report.

[0164] The user opens the work report input screen on the device and enters the work report in the text field in their native language. For example, they might enter "Today's work involved the initial design of a new project" in Japanese. Once they have completed the input, they press the send button.

[0165] Input: User-entered business report text and selected native language

[0166] Output: Data packet created by pressing the send button

[0167] Step 2:

[0168] The terminal transmits the business report data to the server.

[0169] When the send button is pressed, the device creates an HTTP POST request and sends the business report text and native language information to the server. For example, the following JSON data is included in the packet:

[0170] json

[0171] {

[0172] "content": "Today's work involved initial design of a new project.",

[0173] "language": "ja"

[0174] }

[0175] Input: Business report text and native language information

[0176] Output: Data sent to the server as an HTTP POST request

[0177] Step 3:

[0178] The server receives and stores the transmitted business report data.

[0179] The server analyzes the data received from the device and stores it in a database, for example by executing the following SQL query:

[0180] sql

[0181] INSERT INTO reports (content, language) VALUES ('Today's work involved the initial design of a new project.', 'ja')

[0182] Input: Business report data sent via HTTP POST request

[0183] Output: Business report data stored in a database

[0184] Step 4:

[0185] The server passes the business report data to the translation engine.

[0186] The server sends the saved business report to a translation engine to translate it into other languages, for example, by using Google Translate API or DeepL API. It sends the following prompt to the translation engine:

[0187] "Today's work involved the initial design of a new project."

[0188] Input: Saved business report data

[0189] Output: Translation request data sent to the translation engine

[0190] Step 5:

[0191] The translation engine returns the translation results.

[0192] The translation engine translates the Japanese data into the specified language and returns the result to the server. For example, the English translation result "Today, I worked on the initial design of a new project." is returned.

[0193] Input: Translation request data

[0194] Output: Translation result data

[0195] Step 6:

[0196] The server stores the translation results in a database.

[0197] Once the server receives the translation results, it stores them in a database, for example by executing the following SQL query:

[0198] sql

[0199] INSERT INTO translated_reports (report_id, content, language) VALUES (1, 'Today, I worked on the initial design of a new project.', 'en')

[0200] Input: Translation result data

[0201] Output: Translation data stored in a database

[0202] Step 7:

[0203] A user requests translated business report data.

[0204] A user sends a request to the server through a terminal to obtain a business report translated in a specific language.

[0205] Input: Language specification request from user

[0206] Output: Request data to the server

[0207] Step 8:

[0208] The server provides the appropriate translation results.

[0209] The server retrieves the business report stored in the requested language from the database and provides it to the user. For example, if you request a business report in English, it will return the following data:

[0210] json

[0211] {

[0212] "content": "Today, I worked on the initial design of a new project."

[0213] }

[0214] Input: A request from the user

[0215] Output: Translation result data

[0216] Through these processing steps, the system enables multinational members to smoothly report on their work across language barriers and obtain the necessary information in the appropriate language.

[0217] (Application example 1)

[0218] 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."

[0219] In environments where multinational factory staff coexist, language barriers can make it difficult to smoothly share maintenance information and work reports. In particular, if machine maintenance reports are not properly communicated, this can lead to reduced work efficiency and safety risks. To solve this problem, there is a need to provide a reporting system that supports multiple languages.

[0220] 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.

[0221] In this invention, the server includes means for users to input work reports in their native language, means for terminals to transmit work report data to the server, means for the server to receive and store the transmitted work report data, translation engine means for the server to translate the work report data into other languages, means for the server to distribute the translated work report data in an appropriate language for each member, means for factory machines to automatically generate and transmit maintenance reports, and means for staff to check the reports on communication terminals used by staff. This enables multinational factory staff to quickly and accurately share maintenance information across language barriers.

[0222] A "multilingual business report system" is a system in which a user inputs a business report in their native language, and the report is translated into other languages ​​and distributed to each member in the appropriate language.

[0223] "Means for users to input business reports in their own native language" refers to means for providing an interface that allows users to input business details in their own native language.

[0224] The "means for the terminal to transmit business report data to the server" refers to a means that utilizes communication technology for transmitting data containing input business reports from the terminal to the server.

[0225] The "means for the server to receive and store the business report data transmitted" is a means for the server to receive the business report data transmitted from the terminal and store it in a database or the like.

[0226] The "translation engine means for the server to translate the business report data into another language" refers to the means by which the server converts the transmitted data into another language using a translation engine.

[0227] "Means for the server to deliver translated business report data in an appropriate language for each member" refers to means for providing translated data in the language selected by each member.

[0228] "Means for factory machines to automatically generate and send maintenance reports" refers to a means by which machines installed in a factory detect their own condition and the details of maintenance that is required, and automatically generate and send a report to a server.

[0229] "Means for staff to check reports on communication devices used by staff" refers to means for staff to check translated maintenance reports and work reports using communication devices such as smartphones and tablets.

[0230] This invention aims to realize a multilingual business report system, and is configured so that users, terminals, and servers can work together to share business reports across language barriers among members of various nationalities. This system is particularly notable in a factory environment in that it can automatically report machine maintenance.

[0231] This system first provides an interface for users to input business reports in their native language. For example, a text input field is displayed on the screen of a smartphone or tablet, allowing users to "input business details in their native language." The input business report data is then sent from the device to the server.

[0232] The server receives the data sent via the HTTP POST request, stores it in a database, and passes it to a translation engine to translate it into other languages, typically a generative AI model such as the Google Translate API.

[0233] The translated results are stored in a database by the server and are provided appropriately according to the language selected by the user. For example, "a report entered in Japanese can be translated into English, French, or Spanish, and members who select each language can view it on their smartphones or tablets."

[0234] Furthermore, factory machines automatically detect maintenance status and report the details. For example, "if a robot's operating temperature exceeds the normal range, a maintenance report is automatically generated and sent to the server." These automatic reports are also translated and distributed, allowing multinational staff to receive the reports in the appropriate language and respond quickly.

[0235] Examples include the following operations:

[0236] 1. The user types in Japanese, "The robot's operating temperature has exceeded the normal range. The cooling system needs to be inspected."

[0237] 2. This report data is sent from the terminal to the server, which receives it and stores it in a database.

[0238] 3. The server uses the Google Translate API to translate the message as "The robot's operating temperature has exceeded the normal range. The cooling system needs to be checked." and saves this data in the database.

[0239] 4. When staff who prefer English check the report on their smartphone, the translated English report will be displayed.

[0240] Example prompt sentence:

[0241] "The robot's operating temperature has exceeded the normal range. The cooling system needs to be inspected."

[0242] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0243] Step 1:

[0244] A user inputs a business report in his / her native language.

[0245] Input: The user types in their native language, "The robot's operating temperature has exceeded the normal range. The cooling system needs to be inspected."

[0246] How it works: The user enters a business report into the input field displayed on the screen of their smartphone or tablet.

[0247] Step 2:

[0248] The terminal transmits the business report data to the server.

[0249] Input: Business report data entered by the user and its language information.

[0250] Operation: The terminal sends business report data to the server using an HTTP POST request.

[0251] Output: Business report data is sent to the server.

[0252] Step 3:

[0253] The server receives and stores the transmitted business report data.

[0254] Input: Business report data (e.g., "The robot's operating temperature has exceeded the normal range. The cooling system needs to be inspected.") and its language information.

[0255] Operation: The server stores the received data in a database.

[0256] Output: Business report data stored in a database.

[0257] Step 4:

[0258] The server uses a translation engine means for translating the business report data into other languages.

[0259] Input: Business report data and its language information stored in the database.

[0260] How it works: The server uses the Google Translate API to translate business report data into other languages.

[0261] Output: Translated operational report data (e.g., "The robot's operating temperature has exceeded the normal range. The cooling system needs to be checked.").

[0262] Step 5:

[0263] The server stores the translated business report data in a database.

[0264] Input: Translated business report data.

[0265] How it works: The server stores the translated data in a database.

[0266] Output: Business report data stored in a database in multiple languages.

[0267] Step 6:

[0268] The server distributes the translated business report data to each member in the appropriate language.

[0269] Input: User's language preference and translation data stored in the database.

[0270] Operation: The server provides business report data in the appropriate language based on the user's language preference.

[0271] Output: Translated business report data displayed on the user's communication terminal.

[0272] Step 7:

[0273] Factory machines automatically generate and transmit maintenance reports.

[0274] Input: Machine status information (e.g. operating temperature, error codes, etc.).

[0275] How it works: Factory machines automatically detect their own condition, generate maintenance reports, and send them to a server.

[0276] Output: Auto-generated maintenance report data sent to the server.

[0277] Step 8:

[0278] Check the report on the communication device used by the staff.

[0279] Input: Staff communication terminals and translated business report data provided by the server.

[0280] Operation: A staff member accesses the server using a communication terminal and checks the translated business report data.

[0281] Output: Translated business report data displayed on the staff's communication terminal screen.

[0282] 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.

[0283] This invention further improves the multilingual business reporting system by incorporating an emotion engine that recognizes the user's emotions. This not only overcomes language barriers, but also understands the user's emotions expressed in the report content, making it possible to support richer communication.

[0284] System Overview

[0285] In this system, users input business reports in their native language, and the server receives, translates, analyzes emotions, and distributes the reports. By incorporating an emotion engine, the system can also analyze the emotional elements of the report content and reflect them in the translation and display results.

[0286] User operations

[0287] The user opens the work report input screen on the terminal and inputs the work report in their native language. For example, they might input "Today's work involved the initial design of a new project. I'm very happy" in Japanese. When the user presses the send button, the terminal sends the report data and the user's emotional information to the server. This includes the report content, the language used by the user, and the emotional information.

[0288] Device behavior

[0289] The terminal sends the business report data entered by the user to the server. Specifically, it sends the business report data and emotion information to the server using an HTTP POST request. This request includes the report content, user language information, and emotion information.

[0290] Server Operation

[0291] Receiving and storing report data

[0292] The server receives the business report data sent from the terminal. The received data is stored in a database. For example, suppose the server receives the following data:

[0293] json

[0294] {

[0295] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[0296] "language": "ja",

[0297] "emotion": "happy"

[0298] }

[0299] The server temporarily stores this data in a database.

[0300] Using a sentiment analysis engine

[0301] The server uses an emotion engine to analyze emotions from the report content. The emotion engine analyzes the report content and identifies and tags the emotions contained in the content.

[0302] Using a translation engine

[0303] The server passes the saved business report data to the translation engine and requests translation. The translation engine translates the business report into multiple languages ​​(e.g., English, French, and Spanish). For example, when a Japanese report is translated into English, it becomes, "Today, I worked on the initial design of a new project. I am very happy."

[0304] Saving translation results and emotional information

[0305] The server stores the translated business reports and emotional information in a database. The stored data includes the report content translated into each language and the associated emotional information.

[0306] Delivery of translation results and emotional information

[0307] The user opens the application on their device to view the report. The user selects the display language (e.g., English). The device sends an HTTP GET request to the server based on the selected language. The request includes a language parameter (e.g., / report?language=en).

[0308] The server searches the database for the report in the requested language and sends it to the device along with emotional information. The report received by the device is displayed as emotional information (joy), for example, along with the content, "Today, I worked on the initial design of a new project. I am very happy."

[0309] Specific examples

[0310] Here are some concrete examples:

[0311] 1. User report input

[0312] What you type: "Today I worked on the initial design for a new project. I'm very happy."

[0313] Language: Japanese

[0314] Emotion: Joy (User can specify emotion when typing or the system will recognize it automatically)

[0315] 2. Data transmission from the device

[0316] Data sent by the device:

[0317] json

[0318] {

[0319] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[0320] "language": "ja",

[0321] "emotion": "happy"

[0322] }

[0323] 3. Server Reception and Translation

[0324] The server received:

[0325] json

[0326] {

[0327] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[0328] "language": "ja",

[0329] "emotion": "happy"

[0330] }

[0331] Translation result (English): "Today, I worked on the initial design of a new project. I am very happy."

[0332] 4. Reporting and emotional information distribution

[0333] If the user selects English, the server provides the user with the following data:

[0334] json

[0335] {

[0336] "content": "Today, I worked on the initial design of a new project. I am very happy.",

[0337] "emotion": "happy"

[0338] }

[0339] In this way, through each step, the system realizes business reports that are multilingual and emotionally aware.

[0340] The processing flow will be explained below.

[0341] Step 1:

[0342] The user opens the work report input screen on the terminal. The user inputs the work report in their native language. For example, they might input "Today's work involved the initial design of a new project. I'm very happy" in Japanese.

[0343] Step 2:

[0344] The user completes the input and presses the send button. The terminal structures the input business report data. Specifically, it generates the following data structure:

[0345] json

[0346] {

[0347] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[0348] "language": "ja"

[0349] }

[0350] Step 3:

[0351] The device calls the emotion engine and analyzes the emotion from the user's report. The emotion engine identifies the emotion "happy" from "I'm very happy."

[0352] Step 4:

[0353] The device builds the data with added emotion information. Specifically, it generates the following data structure:

[0354] json

[0355] {

[0356] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[0357] "language": "ja",

[0358] "emotion": "happy"

[0359] }

[0360] Step 5:

[0361] The device sends this data to the server as an HTTP POST request, for example to the / report endpoint.

[0362] Step 6:

[0363] The server receives the POST request and extracts the report data, language information, and sentiment information from the request body.

[0364] Step 7:

[0365] The server stores the received data in a database, for example:

[0366] json

[0367] {

[0368] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[0369] "language": "ja",

[0370] "emotion": "happy"

[0371] }

[0372] Step 8:

[0373] The server passes the saved data to a translation engine and requests a translation, which then translates the Japanese report into English.

[0374] Step 9:

[0375] Receive translation results from the translation engine. For example, receive the following translation results:

[0376] json

[0377] {

[0378] "translatedText": "Today, I worked on the initial design of a new project. I am very happy."

[0379] }

[0380] Step 10:

[0381] The server adds emotion information to the translation results and stores them in a database. Specifically, it stores them as follows:

[0382] json

[0383] {

[0384] "originalContent": "Today's work involved the initial design of a new project. I'm very happy about that.",

[0385] "originalLanguage": "ja",

[0386] "translatedContent": "Today, I worked on the initial design of a new project. I am very happy.",

[0387] "translatedLanguage": "en",

[0388] "emotion": "happy"

[0389] }

[0390] Step 11:

[0391] The user opens the application on their device to view the report. The user selects the display language (e.g., English).

[0392] Step 12:

[0393] The device sends an HTTP GET request to the server based on the selected language, including the language parameter (e.g., / report?language=en).

[0394] Step 13:

[0395] The server searches the database for the report content and emotion information in the requested language and returns it to the device as an HTTP response. Specifically, it returns the following data:

[0396] json

[0397] {

[0398] "translatedContent": "Today, I worked on the initial design of a new project. I am very happy.",

[0399] "emotion": "happy"

[0400] }

[0401] Step 14:

[0402] The device displays the received report and emotional information to the user. For example, "Today, I worked on the initial design of a new project. I am very happy." along with the emotional information (joy).

[0403] In this way, through each step, the system realizes business reports that are multilingual and emotionally aware.

[0404] Example 2

[0405] 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."

[0406] Conventional multilingual business reporting systems have the ability to translate report content into other languages, but lack the ability to understand the user's emotions and enrich communication. This has led to problems such as users' emotions not being reflected in the report, making accurate communication difficult.

[0407] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an emotion analysis engine means for analyzing business report data and identifying emotion information, a translation engine means for translating the business report data into another language, and a means for saving the translated business report data and emotion information. This enables richer communication by identifying the user's emotion contained in the business report and reflecting it in the translation result.

[0408] "User" refers to a person who uses the system to input and review business reports.

[0409] "Terminal" refers to a computer that a user uses to input business reports and send them to a server.

[0410] "Business report data" refers to information including the content of a business report entered by a user in their native language.

[0411] "Server" refers to a computer that has the functions of receiving, analyzing, translating, storing, and distributing business report data sent from a terminal.

[0412] "Sentiment Analysis Engine" refers to software for identifying and tagging user emotions from received business report data.

[0413] "Translation Engine" means software for translating business report data into multiple languages.

[0414] "Database" refers to a structured data storage system for the server to store business report data, translation results, and emotion information.

[0415] "Emotion information" refers to information indicating the user's emotions identified by the emotion analysis engine.

[0416] This invention incorporates an emotion analysis function into a multilingual business report system, enabling it to understand users' emotions and realize richer communication. In this system, users input business reports in their native language, and the server receives, translates, analyzes emotions, and distributes the reports.

[0417] The process begins with the user entering and sending a work report via a terminal. The user opens the work report input screen on the terminal and enters specific details in their native language, such as, "Today's work involved the initial design of a new project. I'm very happy about that." Once the input is complete, the user presses the send button. At this time, the work report content, the language used by the user, and emotional information are included.

[0418] The device sends business report data to the server using an HTTP POST request. This request includes the report content, language information, and emotion information. The data sent from the device is received by the server and stored in a database.

[0419] The server passes the saved business report data to a sentiment analysis engine to identify emotional information. For example, if the report contains the phrase "I'm very happy," the sentiment analysis engine tags it as "happy." The server then passes the business report content to a translation engine to translate it into other languages. For example, when a Japanese report content is translated into English, it becomes "Today, I worked on the initial design of a new project. I am very happy."

[0420] The translated business report data and emotion information are then saved back into the database. To check the report content, the user opens the application on their device and selects the display language. The device then sends an HTTP GET request to the server based on the selected language, and the server retrieves the report content and emotion information in the requested language from the database and sends it to the device. For example, if the user selects English, the content displayed will be "Today, I worked on the initial design of a new project. I am very happy.", with "happy" displayed as the emotion information.

[0421] As an example of a specific operation, suppose the user enters the following prompt:

[0422] "Today's work involved the initial design of a new project. I'm very happy."

[0423] The server analyzes this, identifies the emotional information as "happy," and translates it into "Today, I worked on the initial design of a new project. I am very happy." In this way, this system realizes business reports that are both multilingual and emotionally recognizable.

[0424] In practicing the invention, a general generative AI model can be used as the engine for sentiment analysis and translation, enabling advanced analysis of user input and supporting more accurate and richer communication.

[0425] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0426] Step 1: User enters business report

[0427] Specific operation: The user opens the work report input screen on the terminal, enters, for example, "Today's work involved the initial design of a new project. I'm very happy," and presses the send button.

[0428] Input: The business report content entered by the user, and the language information used (e.g., Japanese).

[0429] Output: Business report data (content, language information, emotion information) is generated on the device and ready to be sent.

[0430] Step 2: The device sends the data to the server

[0431] Specific operation: The device generates an HTTP POST request and sends it to the server in JSON format, including the work report data (e.g., "content": "Today's work involved the initial design of a new project. I'm very happy.", "language": "ja", "emotion": "happy").

[0432] Input: Business report data entered by the user.

[0433] Output: The business report data is sent from the terminal to the server as an HTTP POST request.

[0434] Step 3: The server receives and stores the data

[0435] Specific operation: The server receives the HTTP POST request sent from the device, extracts the report data, and stores it in a database.

[0436] Input: Work report data sent from the device (e.g., "content": "Today's work involved the initial design of a new project. I'm very happy.", "language": "ja", "emotion": "happy").

[0437] Output: Business report data stored in a database.

[0438] Step 4: The server performs sentiment analysis

[0439] Specific operation: The server passes business report data to the emotion analysis engine, which analyzes the report content and identifies the user's emotion. For example, it identifies "happy" from a phrase such as "I'm very happy."

[0440] Input: Business report data stored in a database.

[0441] Output: Sentiment information ("happy") from the sentiment analysis engine.

[0442] Step 5: The server does the translation

[0443] Specific operation: The server passes the business report data to the translation engine, which then translates the report content into another language. For example, when translating from Japanese to English, "Today's work involved the initial design of a new project" becomes "Today, I worked on the initial design of a new project."

[0444] Input: Business report data stored in a database, and sentiment information.

[0445] Output: Translated business report data (e.g., "Today, I worked on the initial design of a new project. I am very happy.") along with the original language and sentiment information ("happy").

[0446] Step 6: The server stores the translation results and emotion information

[0447] Specific operation: The server stores the translated business report data and emotion information in a database.

[0448] Input: Translated business report data and sentiment information.

[0449] Output: Translation results and sentiment information added to and saved in a database.

[0450] Step 7: Deliver data as requested by the user

[0451] Specific operation: The user specifies the language selected on the device and sends a request to the server so that business report data can be displayed. The server retrieves the corresponding language and emotion information from the database and delivers it to the device.

[0452] Input: HTTP GET request from user (e.g. " / report?language=en").

[0453] Output: Translation results and emotional information from the server to the device (e.g., "Today, I worked on the initial design of a new project. I am very happy.").

[0454] This series of processing steps enables users to effectively handle business reports that are multilingual and contain emotional information.

[0455] (Application example 2)

[0456] 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."

[0457] Conventional multilingual business reporting systems simply translate content without considering the user's emotions, making it difficult to respond based on emotions. Therefore, there is a need to understand user emotions and respond appropriately, especially in customer support and review management on online shopping sites. The present invention aims to solve this problem by utilizing user emotional information to provide more appropriate and prompt customer support.

[0458] The identification process by the identification 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 analyzing emotions in business report data using an emotion analysis engine, means for automatically selecting appropriate countermeasures based on the analyzed emotion information, and means for the terminal to display the translation result and the emotion information. This makes it possible to understand the user's emotions and provide appropriate countermeasures based on them.

[0459] "User" refers to a person who uses the system to input business reports.

[0460] "Native language" refers to the language with which the user is most familiar.

[0461] "Work report" refers to information that users record and report on their daily work, progress, emotions, etc.

[0462] "Terminal" refers to a device through which a user inputs business reports and communicates with a server.

[0463] "Server" refers to a computer system that has the functionality to receive, store, analyze, translate, and distribute business report data.

[0464] "Business report data" refers to data including the contents of a business report entered by a user.

[0465] "Translation Engine" means software or a system for translating business reporting data into other languages.

[0466] "Translated business report data" refers to business report data that has been converted into another language by a translation engine.

[0467] "Member" means a person or group that can receive business reporting data within the system.

[0468] "Emotion analysis engine" refers to software or a system for analyzing user emotions from business report data and extracting appropriate emotional information.

[0469] "Emotion information" refers to information that indicates the user's emotional state obtained by the emotion analysis engine.

[0470] "Appropriate countermeasures" refer to the countermeasures that the system automatically selects based on the user's emotional information.

[0471] "Translation result" refers to business report data translated into another language by a translation engine.

[0472] "Database" refers to a system or software for storing business report data, translation results, and emotional information.

[0473] "Display means" refers to a means for visually displaying the translation result and emotional information to the user.

[0474] The present invention aims to improve user response, particularly in customer support and review management, on an online shopping site that incorporates a multilingual emotion analysis system. The specific configuration and operation procedure of the system based on this embodiment are described below.

[0475] The system begins when a user inputs a business report using a device such as a smartphone, tablet, or PC. The data entered by the user in their native language is sent to a server by the device. The server first stores the received business report data in a database.

[0476] Next, the server uses an emotion analysis engine to analyze the user's emotions from the business report data. Based on this analysis, appropriate emotional information is extracted. Based on the emotional information, the server automatically selects an appropriate response. For example, if the user is determined to be "anxious," a prompt response is required.

[0477] The server then passes the business report data to a translation engine for translation into multiple languages. The translation results and emotion information are then stored in a database. The server then sends the translation results and emotion information to the terminal and displays them for the user to visually confirm.

[0478] The implementation of this system uses the following hardware and software:

[0479] Hardware: Smartphone, tablet, PC, server (cloud-based is also acceptable)

[0480] Software: Python, Flask (web server framework), Google Translate API (translation engine), nlp_emotion (emotion analysis library)

[0481] As a concrete example, consider the case where a user enters and submits the following in Japanese: "My item hasn't arrived. I'm very worried." This data is processed as follows:

[0482] 1. "Anxiety" is extracted as the result of analysis by the emotion analysis engine.

[0483] 2. The translation engine translates this into English as, "The product has not arrived. I am very anxious."

[0484] 3. The server stores this data and displays the translation results and emotion information to the user.

[0485] An example prompt for a generative AI model might look like this:

[0486] "Analyze the following sentence using an emotion recognition engine and output its emotion. Sentence: "My item hasn't arrived. I'm very worried.""

[0487] In this way, the present invention is a system that combines multilingual support and emotion analysis to understand user emotions and provide appropriate and prompt responses, particularly in customer support and review management on online shopping sites.

[0488] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0489] Step 1:

[0490] The user enters a business report

[0491] Users use devices such as smartphones, tablets, and PCs to enter business reports and reviews in their native language. The entered data includes the content of the report or review. For example: "The product hasn't arrived. I'm very worried."

[0492] Step 2:

[0493] The device sends the data to the server

[0494] The terminal transmits the business report data input by the user to the server via an HTTP POST request, and the data includes the report content and language information.

[0495] Step 3:

[0496] The server receives and stores the data

[0497] The server receives the business report data sent from the terminal. The received data is stored in a database. For example, the following data is stored:

[0498] json

[0499] {

[0500] "content": "My item hasn't arrived. I'm very worried.",

[0501] "language": "ja"

[0502] }

[0503] Step 4:

[0504] The server analyzes the sentiment using a sentiment analysis engine.

[0505] The server passes the saved business report data to the emotion analysis engine, which analyzes the emotions. The analyzed emotion information (e.g., "anxiety") is stored in a database.

[0506] Step 5:

[0507] The server automatically selects the appropriate countermeasure.

[0508] Based on the results of emotion analysis, the server automatically selects the appropriate response. For example, if the emotion analyzed is "anxiety," a prompt response is required.

[0509] Step 6:

[0510] The server translates the business report data.

[0511] The server passes the saved business report data to a translation engine, which translates it into multiple languages ​​(e.g., English). The translated content is saved back into the database. For example, "The product has not arrived. I am very anxious." is translated into "The product has not arrived. I am very anxious."

[0512] Step 7:

[0513] The server sends the translation results and emotion information

[0514] When a user checks the contents of a business report, the device requests the translation result and emotion information from the server based on the selected language (e.g., English). The server retrieves the necessary data from the database and sends it to the device.

[0515] Step 8:

[0516] The device displays the translation results and emotional information.

[0517] The device displays the translation result and emotional information it receives on the screen. For example, the user can see the translation result "The product has not arrived. I am very anxious." along with the emotional information "anxiety."

[0518] This process allows the system to understand the user's emotions and quickly respond appropriately based on those emotions.

[0519] 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.

[0520] 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.

[0521] 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.

[0522] [Second embodiment]

[0523] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0524] 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.

[0525] 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).

[0526] 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.

[0527] 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.

[0528] 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).

[0529] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0530] 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.

[0531] 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.

[0532] 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.

[0533] 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.

[0534] 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."

[0535] This invention aims to realize a multilingual business reporting system, and will be described in detail in the following form. The entire system consists of three main components: users, terminals, and servers. This system allows multinational members to smoothly submit business reports across language barriers.

[0536] System Overview

[0537] In this system, users input business reports in their native language, and the server receives, translates, and distributes the reports. The translated results are then displayed in the appropriate language for each member.

[0538] User operations

[0539] The user opens the work report input screen on the terminal and inputs the work report in their native language. For example, they might input "Today's work involved the initial design of a new project" in Japanese. When the user presses the send button, the terminal sends the report data to the server. At this time, the report content and the language information used by the user are included.

[0540] Device behavior

[0541] The terminal sends the business report data entered by the user to the server. Specifically, the terminal uses an HTTP POST request to send the business report data to the server. This request includes the report content and the user's language information.

[0542] Server Operation

[0543] Receiving and storing report data

[0544] The server receives the business report data sent from the terminal. The received data is stored in a database. For example, suppose the server receives the following data:

[0545] json

[0546] {

[0547] "content": "Today's work involved initial design of a new project.",

[0548] "language": "ja"

[0549] }

[0550] The server temporarily stores this data in a database.

[0551] Using a translation engine

[0552] The server passes the saved business report data to the translation engine and requests translation. The translation engine translates the business report into multiple languages ​​(e.g., English, French, and Spanish). For example, when a Japanese report is translated into English, it becomes, "Today, I worked on the initial design of a new project."

[0553] Storage and distribution of translation results

[0554] The server stores the translated business reports in a database. The stored translation results are dynamically provided in response to user requests. For example, if a user who prefers English checks a report, the server provides the report translated into English.

[0555] Specific examples

[0556] Here are some concrete examples:

[0557] 1. User report input

[0558] What the user types: "Today, I worked on the initial design for a new project."

[0559] Language: Japanese

[0560] 2. Data transmission from the device

[0561] Data sent by the device:

[0562] json

[0563] {

[0564] "content": "Today's work involved initial design of a new project.",

[0565] "language": "ja"

[0566] }

[0567] 3. Server Reception and Translation

[0568] The server received:

[0569] json

[0570] {

[0571] "content": "Today's work involved initial design of a new project.",

[0572] "language": "ja"

[0573] }

[0574] Translation result (English): "Today, I worked on the initial design of a new project."

[0575] 4. Report Distribution

[0576] If the user selects English, the server provides the user with the following data:

[0577] json

[0578] {

[0579] "content": "Today, I worked on the initial design of a new project."

[0580] }

[0581] In this way, the system allows multinational members to easily submit business reports and have the contents of those reports confirmed in a language that other members can understand.

[0582] The processing flow will be explained below.

[0583] Step 1:

[0584] The user opens the work report input screen on the terminal. The user inputs the work report in their native language. For example, they input "Today's work involved the initial design of a new project" in Japanese.

[0585] Step 2:

[0586] The user completes the input and presses the send button. The terminal structures the input business report data. Specifically, it generates the following data structure:

[0587] json

[0588] {

[0589] "content": "Today's work involved initial design of a new project.",

[0590] "language": "ja"

[0591] }

[0592] Step 3:

[0593] The device sends this data to the server as an HTTP POST request, for example to the / report endpoint.

[0594] Step 4:

[0595] The server receives the POST request and extracts the report data and language information from the request body.

[0596] Step 5:

[0597] The server stores the received data in a database, including the report content and the original language information.

[0598] Step 6:

[0599] The server invokes the translation engine, which translates the report into the specified language (e.g., English, French, Spanish, etc.).

[0600] Step 7:

[0601] The translation engine receives the translation result. For example, in English, the translation result might be "Today, I worked on the initial design of a new project."

[0602] Step 8:

[0603] The server stores the translation results in a database, which includes the original text and the translated report content in each language.

[0604] Step 9:

[0605] The user opens the application on their device to view the report. The user selects the display language (e.g., English).

[0606] Step 10:

[0607] The device sends an HTTP GET request to the server based on the selected language, including the language parameter (e.g., / report?language=en).

[0608] Step 11:

[0609] The server retrieves the report in the requested language from its database.

[0610] Step 12:

[0611] The server returns the search results to the device as an HTTP response, which includes the translated report in the selected language.

[0612] Step 13:

[0613] The device displays the received report to the user. For example, it displays "Today, I worked on the initial design of a new project." in English.

[0614] In this way, the system realizes multilingual business reports through each step.

[0615] Example 1

[0616] 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."

[0617] In a work environment involving multinational members, language barriers are a major obstacle to smooth business reporting and information sharing. This can lead to misunderstandings and reduced business efficiency. Conventional systems rely on one language or require expensive translation services. To improve this situation, it is necessary to provide a user-friendly business reporting system that supports multiple languages ​​in real time.

[0618] 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.

[0619] In this invention, the server includes a means for transmitting data including user language information from the terminal to the server, a means for the server to retrieve and provide appropriate data from a database based on a user request, and a means for the terminal to use an HTTP POST request to transmit data to the server, thereby enabling efficient and smooth business reporting and information sharing among users of multiple nationalities.

[0620] "User" means any person or organization that accesses the system to enter business reports and receive information.

[0621] A "terminal" is a device such as a computer, smartphone, or tablet that a user uses to input business reports and send and receive data.

[0622] A "server" is a central computer system that receives and stores data sent from terminals, performs the necessary processing, and provides the results.

[0623] "Business report data" is text information in which a user describes the details of business activities, and is data that is processed after being entered into the system.

[0624] "Translation Engine" means software or services for automatically translating business report data into other languages.

[0625] "Storage" means storing the received data in a storage medium such as a database so that it can be accessed later.

[0626] "Translation engine means" refers to hardware or software that provides the function of translating business report data into multiple languages.

[0627] A "member" is an individual or group who has registered with the system to use the system to submit business reports and share information with other users.

[0628] "Appropriate language" refers to a language that each user can understand and that is used by the translation engine to provide translation results.

[0629] An "HTTP POST request" is a form of Internet communication protocol used by a terminal to send data to a server.

[0630] A "database" is an information management system that stores data in an organized manner and enables efficient access when needed.

[0631] A "generative model" is an artificial intelligence model used to generate new information, translations, etc. based on input data.

[0632] A "request" is an action or content of a user requesting a system to perform a specific operation or provide information.

[0633] The present invention relates to a multilingual business report system, which will be described in detail below. In this system, users input business reports in their native language, and the server receives, translates, and distributes the report content. The translation results are displayed in the appropriate language for each member.

[0634] User operations

[0635] The user opens the work report input screen on the device and enters the work report in their native language. For example, they might enter "Today's work involved the initial design of a new project" in Japanese. When the user presses the send button, the device sends this work report data to the server. At this time, the report content and the user's language information are also included.

[0636] Device behavior

[0637] The terminal sends the business report data entered by the user to the server using an HTTP POST request. The sent data includes the report content and the user's language information. For example, the following data is sent:

[0638] json

[0639] {

[0640] "content": "Today's work involved initial design of a new project.",

[0641] "language": "ja"

[0642] }

[0643] Server Operation

[0644] Receiving and storing report data

[0645] The server receives the business report data sent from the terminal and stores it in a database. For example, the server receives the following data:

[0646] json

[0647] {

[0648] "content": "Today's work involved initial design of a new project.",

[0649] "language": "ja"

[0650] }

[0651] The server runs SQL queries to store this data in a database.

[0652] Using a translation engine

[0653] The server passes the saved business report data to a translation engine and requests translation. For example, it uses a translation service such as Google Translate API or DeepL API. It sends the following prompt to the translation engine:

[0654] "Today's work involved the initial design of a new project."

[0655] As a response from the translation engine, for example, you receive the English translation result "Today, I worked on the initial design of a new project."

[0656] Storage and distribution of translation results

[0657] The server stores the translated business reports in a database. For example, it executes the query "INSERT INTO translated_reports (report_id, content, language) VALUES (1, 'Today, I worked on the initial design of a new project.', 'en')". The stored translation results are dynamically provided in response to user requests. For example, if a user who prefers English checks a report, the server provides the report translated into English.

[0658] In this way, this system allows multinational members to easily submit work reports and have the report contents confirmed in a language that other members can understand. This system is realized by combining technologies such as HTTP POST requests, SQL databases, and translation APIs. In addition, appropriate translations are provided based on each user's language settings, enabling smooth work reports across language barriers.

[0659] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0660] Step 1:

[0661] The user inputs a business report.

[0662] The user opens the work report input screen on the device and enters the work report in the text field in their native language. For example, they might enter "Today's work involved the initial design of a new project" in Japanese. Once they have completed the input, they press the send button.

[0663] Input: User-entered business report text and selected native language

[0664] Output: Data packet created by pressing the send button

[0665] Step 2:

[0666] The terminal transmits the business report data to the server.

[0667] When the send button is pressed, the device creates an HTTP POST request and sends the business report text and native language information to the server. For example, the following JSON data is included in the packet:

[0668] json

[0669] {

[0670] "content": "Today's work involved initial design of a new project.",

[0671] "language": "ja"

[0672] }

[0673] Input: Business report text and native language information

[0674] Output: Data sent to the server as an HTTP POST request

[0675] Step 3:

[0676] The server receives and stores the transmitted business report data.

[0677] The server analyzes the data received from the device and stores it in a database, for example by executing the following SQL query:

[0678] sql

[0679] INSERT INTO reports (content, language) VALUES ('Today's work involved the initial design of a new project.', 'ja')

[0680] Input: Business report data sent via HTTP POST request

[0681] Output: Business report data stored in a database

[0682] Step 4:

[0683] The server passes the business report data to the translation engine.

[0684] The server sends the saved business report to a translation engine to translate it into other languages, for example, by using Google Translate API or DeepL API. It sends the following prompt to the translation engine:

[0685] "Today's work involved the initial design of a new project."

[0686] Input: Saved business report data

[0687] Output: Translation request data sent to the translation engine

[0688] Step 5:

[0689] The translation engine returns the translation results.

[0690] The translation engine translates the Japanese data into the specified language and returns the result to the server. For example, the English translation result "Today, I worked on the initial design of a new project." is returned.

[0691] Input: Translation request data

[0692] Output: Translation result data

[0693] Step 6:

[0694] The server stores the translation results in a database.

[0695] Once the server receives the translation results, it stores them in a database, for example by executing the following SQL query:

[0696] sql

[0697] INSERT INTO translated_reports (report_id, content, language) VALUES (1, 'Today, I worked on the initial design of a new project.', 'en')

[0698] Input: Translation result data

[0699] Output: Translation data stored in a database

[0700] Step 7:

[0701] A user requests translated business report data.

[0702] A user sends a request to the server through a terminal to obtain a business report translated in a specific language.

[0703] Input: Language specification request from user

[0704] Output: Request data to the server

[0705] Step 8:

[0706] The server provides the appropriate translation results.

[0707] The server retrieves the business report stored in the requested language from the database and provides it to the user. For example, if you request a business report in English, it will return the following data:

[0708] json

[0709] {

[0710] "content": "Today, I worked on the initial design of a new project."

[0711] }

[0712] Input: A request from the user

[0713] Output: Translation result data

[0714] Through these processing steps, the system enables multinational members to smoothly report on their work across language barriers and obtain the necessary information in the appropriate language.

[0715] (Application example 1)

[0716] 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."

[0717] In environments where multinational factory staff coexist, language barriers can make it difficult to smoothly share maintenance information and work reports. In particular, if machine maintenance reports are not properly communicated, this can lead to reduced work efficiency and safety risks. To solve this problem, there is a need to provide a reporting system that supports multiple languages.

[0718] 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.

[0719] In this invention, the server includes means for users to input work reports in their native language, means for terminals to transmit work report data to the server, means for the server to receive and store the transmitted work report data, translation engine means for the server to translate the work report data into other languages, means for the server to distribute the translated work report data in an appropriate language for each member, means for factory machines to automatically generate and transmit maintenance reports, and means for staff to check the reports on communication terminals used by staff. This enables multinational factory staff to quickly and accurately share maintenance information across language barriers.

[0720] A "multilingual business report system" is a system in which a user inputs a business report in their native language, and the report is translated into other languages ​​and distributed to each member in the appropriate language.

[0721] "Means for users to input business reports in their own native language" refers to means for providing an interface that allows users to input business details in their own native language.

[0722] The "means for the terminal to transmit business report data to the server" refers to a means that utilizes communication technology for transmitting data containing input business reports from the terminal to the server.

[0723] The "means for the server to receive and store the business report data transmitted" is a means for the server to receive the business report data transmitted from the terminal and store it in a database or the like.

[0724] The "translation engine means for the server to translate the business report data into another language" refers to the means by which the server converts the transmitted data into another language using a translation engine.

[0725] "Means for the server to deliver translated business report data in an appropriate language for each member" refers to means for providing translated data in the language selected by each member.

[0726] "Means for factory machines to automatically generate and send maintenance reports" refers to a means by which machines installed in a factory detect their own condition and the details of maintenance that is required, and automatically generate and send a report to a server.

[0727] "Means for staff to check reports on communication devices used by staff" refers to means for staff to check translated maintenance reports and work reports using communication devices such as smartphones and tablets.

[0728] This invention aims to realize a multilingual business report system, and is configured so that users, terminals, and servers can work together to share business reports across language barriers among members of various nationalities. This system is particularly notable in a factory environment in that it can automatically report machine maintenance.

[0729] This system first provides an interface for users to input business reports in their native language. For example, a text input field is displayed on the screen of a smartphone or tablet, allowing users to "input business details in their native language." The input business report data is then sent from the device to the server.

[0730] The server receives the data sent via the HTTP POST request, stores it in a database, and passes it to a translation engine to translate it into other languages, typically a generative AI model such as the Google Translate API.

[0731] The translated results are stored in a database by the server and are provided appropriately according to the language selected by the user. For example, "a report entered in Japanese can be translated into English, French, or Spanish, and members who select each language can view it on their smartphones or tablets."

[0732] Furthermore, factory machines automatically detect maintenance status and report the details. For example, "if a robot's operating temperature exceeds the normal range, a maintenance report is automatically generated and sent to the server." These automatic reports are also translated and distributed, allowing multinational staff to receive the reports in the appropriate language and respond quickly.

[0733] Examples include the following operations:

[0734] 1. The user types in Japanese, "The robot's operating temperature has exceeded the normal range. The cooling system needs to be inspected."

[0735] 2. This report data is sent from the terminal to the server, which receives it and stores it in a database.

[0736] 3. The server uses the Google Translate API to translate the message as "The robot's operating temperature has exceeded the normal range. The cooling system needs to be checked." and saves this data in the database.

[0737] 4. When staff who prefer English check the report on their smartphone, the translated English report will be displayed.

[0738] Example prompt sentence:

[0739] "The robot's operating temperature has exceeded the normal range. The cooling system needs to be inspected."

[0740] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0741] Step 1:

[0742] A user inputs a business report in his / her native language.

[0743] Input: The user types in their native language, "The robot's operating temperature has exceeded the normal range. The cooling system needs to be inspected."

[0744] How it works: The user enters a business report into the input field displayed on the screen of their smartphone or tablet.

[0745] Step 2:

[0746] The terminal transmits the business report data to the server.

[0747] Input: Business report data entered by the user and its language information.

[0748] Operation: The terminal sends business report data to the server using an HTTP POST request.

[0749] Output: Business report data is sent to the server.

[0750] Step 3:

[0751] The server receives and stores the transmitted business report data.

[0752] Input: Business report data (e.g., "The robot's operating temperature has exceeded the normal range. The cooling system needs to be inspected.") and its language information.

[0753] Operation: The server stores the received data in a database.

[0754] Output: Business report data stored in a database.

[0755] Step 4:

[0756] The server uses a translation engine means for translating the business report data into other languages.

[0757] Input: Business report data and its language information stored in the database.

[0758] How it works: The server uses the Google Translate API to translate business report data into other languages.

[0759] Output: Translated operational report data (e.g., "The robot's operating temperature has exceeded the normal range. The cooling system needs to be checked.").

[0760] Step 5:

[0761] The server stores the translated business report data in a database.

[0762] Input: Translated business report data.

[0763] How it works: The server stores the translated data in a database.

[0764] Output: Business report data stored in a database in multiple languages.

[0765] Step 6:

[0766] The server distributes the translated business report data to each member in the appropriate language.

[0767] Input: User's language preference and translation data stored in the database.

[0768] Operation: The server provides business report data in the appropriate language based on the user's language preference.

[0769] Output: Translated business report data displayed on the user's communication terminal.

[0770] Step 7:

[0771] Factory machines automatically generate and transmit maintenance reports.

[0772] Input: Machine status information (e.g. operating temperature, error codes, etc.).

[0773] How it works: Factory machines automatically detect their own condition, generate maintenance reports, and send them to a server.

[0774] Output: Auto-generated maintenance report data sent to the server.

[0775] Step 8:

[0776] Check the report on the communication device used by the staff.

[0777] Input: Staff communication terminals and translated business report data provided by the server.

[0778] Operation: A staff member accesses the server using a communication terminal and checks the translated business report data.

[0779] Output: Translated business report data displayed on the staff's communication terminal screen.

[0780] 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.

[0781] This invention further improves the multilingual business reporting system by incorporating an emotion engine that recognizes the user's emotions. This not only overcomes language barriers, but also understands the user's emotions expressed in the report content, making it possible to support richer communication.

[0782] System Overview

[0783] In this system, users input business reports in their native language, and the server receives, translates, analyzes emotions, and distributes the reports. By incorporating an emotion engine, the system can also analyze the emotional elements of the report content and reflect them in the translation and display results.

[0784] User operations

[0785] The user opens the work report input screen on the terminal and inputs the work report in their native language. For example, they might input "Today's work involved the initial design of a new project. I'm very happy" in Japanese. When the user presses the send button, the terminal sends the report data and the user's emotional information to the server. This includes the report content, the language used by the user, and the emotional information.

[0786] Device behavior

[0787] The terminal sends the business report data entered by the user to the server. Specifically, it sends the business report data and emotion information to the server using an HTTP POST request. This request includes the report content, user language information, and emotion information.

[0788] Server Operation

[0789] Receiving and storing report data

[0790] The server receives the business report data sent from the terminal. The received data is stored in a database. For example, suppose the server receives the following data:

[0791] json

[0792] {

[0793] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[0794] "language": "ja",

[0795] "emotion": "happy"

[0796] }

[0797] The server temporarily stores this data in a database.

[0798] Using a sentiment analysis engine

[0799] The server uses an emotion engine to analyze emotions from the report content. The emotion engine analyzes the report content and identifies and tags the emotions contained in the content.

[0800] Using a translation engine

[0801] The server passes the saved business report data to the translation engine and requests translation. The translation engine translates the business report into multiple languages ​​(e.g., English, French, and Spanish). For example, when a Japanese report is translated into English, it becomes, "Today, I worked on the initial design of a new project. I am very happy."

[0802] Saving translation results and emotional information

[0803] The server stores the translated business reports and emotional information in a database. The stored data includes the report content translated into each language and the associated emotional information.

[0804] Delivery of translation results and emotional information

[0805] The user opens the application on their device to view the report. The user selects the display language (e.g., English). The device sends an HTTP GET request to the server based on the selected language. The request includes a language parameter (e.g., / report?language=en).

[0806] The server searches the database for the report in the requested language and sends it to the device along with emotional information. The report received by the device is displayed as emotional information (joy), for example, along with the content, "Today, I worked on the initial design of a new project. I am very happy."

[0807] Specific examples

[0808] Here are some concrete examples:

[0809] 1. User report input

[0810] What you type: "Today I worked on the initial design for a new project. I'm very happy."

[0811] Language: Japanese

[0812] Emotion: Joy (User can specify emotion when typing or the system will recognize it automatically)

[0813] 2. Data transmission from the device

[0814] Data sent by the device:

[0815] json

[0816] {

[0817] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[0818] "language": "ja",

[0819] "emotion": "happy"

[0820] }

[0821] 3. Server Reception and Translation

[0822] The server received:

[0823] json

[0824] {

[0825] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[0826] "language": "ja",

[0827] "emotion": "happy"

[0828] }

[0829] Translation result (English): "Today, I worked on the initial design of a new project. I am very happy."

[0830] 4. Reporting and emotional information distribution

[0831] If the user selects English, the server provides the user with the following data:

[0832] json

[0833] {

[0834] "content": "Today, I worked on the initial design of a new project. I am very happy.",

[0835] "emotion": "happy"

[0836] }

[0837] In this way, through each step, the system realizes business reports that are multilingual and emotionally aware.

[0838] The processing flow will be explained below.

[0839] Step 1:

[0840] The user opens the work report input screen on the terminal. The user inputs the work report in their native language. For example, they might input "Today's work involved the initial design of a new project. I'm very happy" in Japanese.

[0841] Step 2:

[0842] The user completes the input and presses the send button. The terminal structures the input business report data. Specifically, it generates the following data structure:

[0843] json

[0844] {

[0845] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[0846] "language": "ja"

[0847] }

[0848] Step 3:

[0849] The device calls the emotion engine and analyzes the emotion from the user's report. The emotion engine identifies the emotion "happy" from "I'm very happy."

[0850] Step 4:

[0851] The device builds the data with added emotion information. Specifically, it generates the following data structure:

[0852] json

[0853] {

[0854] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[0855] "language": "ja",

[0856] "emotion": "happy"

[0857] }

[0858] Step 5:

[0859] The device sends this data to the server as an HTTP POST request, for example to the / report endpoint.

[0860] Step 6:

[0861] The server receives the POST request and extracts the report data, language information, and sentiment information from the request body.

[0862] Step 7:

[0863] The server stores the received data in a database, for example:

[0864] json

[0865] {

[0866] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[0867] "language": "ja",

[0868] "emotion": "happy"

[0869] }

[0870] Step 8:

[0871] The server passes the saved data to a translation engine and requests a translation, which then translates the Japanese report into English.

[0872] Step 9:

[0873] Receive translation results from the translation engine. For example, receive the following translation results:

[0874] json

[0875] {

[0876] "translatedText": "Today, I worked on the initial design of a new project. I am very happy."

[0877] }

[0878] Step 10:

[0879] The server adds emotion information to the translation results and stores them in a database. Specifically, it stores them as follows:

[0880] json

[0881] {

[0882] "originalContent": "Today's work involved the initial design of a new project. I'm very happy about that.",

[0883] "originalLanguage": "ja",

[0884] "translatedContent": "Today, I worked on the initial design of a new project. I am very happy.",

[0885] "translatedLanguage": "en",

[0886] "emotion": "happy"

[0887] }

[0888] Step 11:

[0889] The user opens the application on their device to view the report. The user selects the display language (e.g., English).

[0890] Step 12:

[0891] The device sends an HTTP GET request to the server based on the selected language, including the language parameter (e.g., / report?language=en).

[0892] Step 13:

[0893] The server searches the database for the report content and emotion information in the requested language and returns it to the device as an HTTP response. Specifically, it returns the following data:

[0894] json

[0895] {

[0896] "translatedContent": "Today, I worked on the initial design of a new project. I am very happy.",

[0897] "emotion": "happy"

[0898] }

[0899] Step 14:

[0900] The device displays the received report and emotional information to the user. For example, "Today, I worked on the initial design of a new project. I am very happy." along with the emotional information (joy).

[0901] In this way, through each step, the system realizes business reports that are multilingual and emotionally aware.

[0902] Example 2

[0903] 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."

[0904] Conventional multilingual business reporting systems have the ability to translate report content into other languages, but lack the ability to understand the user's emotions and enrich communication. This has led to problems such as users' emotions not being reflected in the report, making accurate communication difficult.

[0905] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an emotion analysis engine means for analyzing business report data and identifying emotion information, a translation engine means for translating the business report data into another language, and a means for saving the translated business report data and emotion information. This enables richer communication by identifying the user's emotion contained in the business report and reflecting it in the translation result.

[0906] "User" refers to a person who uses the system to input and review business reports.

[0907] "Terminal" refers to a computer that a user uses to input business reports and send them to a server.

[0908] "Business report data" refers to information including the content of a business report entered by a user in their native language.

[0909] "Server" refers to a computer that has the functions of receiving, analyzing, translating, storing, and distributing business report data sent from a terminal.

[0910] "Sentiment Analysis Engine" refers to software for identifying and tagging user emotions from received business report data.

[0911] "Translation Engine" means software for translating business report data into multiple languages.

[0912] "Database" refers to a structured data storage system for the server to store business report data, translation results, and emotion information.

[0913] "Emotion information" refers to information indicating the user's emotions identified by the emotion analysis engine.

[0914] This invention incorporates an emotion analysis function into a multilingual business report system, enabling it to understand users' emotions and realize richer communication. In this system, users input business reports in their native language, and the server receives, translates, analyzes emotions, and distributes the reports.

[0915] The process begins with the user entering and sending a work report via a terminal. The user opens the work report input screen on the terminal and enters specific details in their native language, such as, "Today's work involved the initial design of a new project. I'm very happy about that." Once the input is complete, the user presses the send button. At this time, the work report content, the language used by the user, and emotional information are included.

[0916] The device sends business report data to the server using an HTTP POST request. This request includes the report content, language information, and emotion information. The data sent from the device is received by the server and stored in a database.

[0917] The server passes the saved business report data to a sentiment analysis engine to identify emotional information. For example, if the report contains the phrase "I'm very happy," the sentiment analysis engine tags it as "happy." The server then passes the business report content to a translation engine to translate it into other languages. For example, when a Japanese report content is translated into English, it becomes "Today, I worked on the initial design of a new project. I am very happy."

[0918] The translated business report data and emotion information are then saved back into the database. To check the report content, the user opens the application on their device and selects the display language. The device then sends an HTTP GET request to the server based on the selected language, and the server retrieves the report content and emotion information in the requested language from the database and sends it to the device. For example, if the user selects English, the content displayed will be "Today, I worked on the initial design of a new project. I am very happy.", with "happy" displayed as the emotion information.

[0919] As an example of a specific operation, suppose the user enters the following prompt:

[0920] "Today's work involved the initial design of a new project. I'm very happy."

[0921] The server analyzes this, identifies the emotional information as "happy," and translates it into "Today, I worked on the initial design of a new project. I am very happy." In this way, this system realizes business reports that are both multilingual and emotionally recognizable.

[0922] In practicing the invention, a general generative AI model can be used as the engine for sentiment analysis and translation, enabling advanced analysis of user input and supporting more accurate and richer communication.

[0923] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0924] Step 1: User enters business report

[0925] Specific operation: The user opens the work report input screen on the terminal, enters, for example, "Today's work involved the initial design of a new project. I'm very happy," and presses the send button.

[0926] Input: The business report content entered by the user, and the language information used (e.g., Japanese).

[0927] Output: Business report data (content, language information, emotion information) is generated on the device and ready to be sent.

[0928] Step 2: The device sends the data to the server

[0929] Specific operation: The device generates an HTTP POST request and sends it to the server in JSON format, including the work report data (e.g., "content": "Today's work involved the initial design of a new project. I'm very happy.", "language": "ja", "emotion": "happy").

[0930] Input: Business report data entered by the user.

[0931] Output: The business report data is sent from the terminal to the server as an HTTP POST request.

[0932] Step 3: The server receives and stores the data

[0933] Specific operation: The server receives the HTTP POST request sent from the device, extracts the report data, and stores it in a database.

[0934] Input: Work report data sent from the device (e.g., "content": "Today's work involved the initial design of a new project. I'm very happy.", "language": "ja", "emotion": "happy").

[0935] Output: Business report data stored in a database.

[0936] Step 4: The server performs sentiment analysis

[0937] Specific operation: The server passes business report data to the emotion analysis engine, which analyzes the report content and identifies the user's emotion. For example, it identifies "happy" from a phrase such as "I'm very happy."

[0938] Input: Business report data stored in a database.

[0939] Output: Sentiment information ("happy") from the sentiment analysis engine.

[0940] Step 5: The server does the translation

[0941] Specific operation: The server passes the business report data to the translation engine, which then translates the report content into another language. For example, when translating from Japanese to English, "Today's work involved the initial design of a new project" becomes "Today, I worked on the initial design of a new project."

[0942] Input: Business report data stored in a database, and sentiment information.

[0943] Output: Translated business report data (e.g., "Today, I worked on the initial design of a new project. I am very happy.") along with the original language and sentiment information ("happy").

[0944] Step 6: The server stores the translation results and emotion information

[0945] Specific operation: The server stores the translated business report data and emotion information in a database.

[0946] Input: Translated business report data and sentiment information.

[0947] Output: Translation results and sentiment information added to and saved in a database.

[0948] Step 7: Deliver data as requested by the user

[0949] Specific operation: The user specifies the language selected on the device and sends a request to the server so that business report data can be displayed. The server retrieves the corresponding language and emotion information from the database and delivers it to the device.

[0950] Input: HTTP GET request from user (e.g. " / report?language=en").

[0951] Output: Translation results and emotional information from the server to the device (e.g., "Today, I worked on the initial design of a new project. I am very happy.").

[0952] This series of processing steps enables users to effectively handle business reports that are multilingual and contain emotional information.

[0953] (Application example 2)

[0954] 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."

[0955] Conventional multilingual business reporting systems simply translate content without considering the user's emotions, making it difficult to respond based on emotions. Therefore, there is a need to understand user emotions and respond appropriately, especially in customer support and review management on online shopping sites. The present invention aims to solve this problem by utilizing user emotional information to provide more appropriate and prompt customer support.

[0956] The identification process by the identification 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 analyzing emotions in business report data using an emotion analysis engine, means for automatically selecting appropriate countermeasures based on the analyzed emotion information, and means for the terminal to display the translation result and the emotion information. This makes it possible to understand the user's emotions and provide appropriate countermeasures based on them.

[0957] "User" refers to a person who uses the system to input business reports.

[0958] "Native language" refers to the language with which the user is most familiar.

[0959] "Work report" refers to information that users record and report on their daily work, progress, emotions, etc.

[0960] "Terminal" refers to a device through which a user inputs business reports and communicates with a server.

[0961] "Server" refers to a computer system that has the functionality to receive, store, analyze, translate, and distribute business report data.

[0962] "Business report data" refers to data including the contents of a business report entered by a user.

[0963] "Translation Engine" means software or a system for translating business reporting data into other languages.

[0964] "Translated business report data" refers to business report data that has been converted into another language by a translation engine.

[0965] "Member" means a person or group that can receive business reporting data within the system.

[0966] "Emotion analysis engine" refers to software or a system for analyzing user emotions from business report data and extracting appropriate emotional information.

[0967] "Emotion information" refers to information that indicates the user's emotional state obtained by the emotion analysis engine.

[0968] "Appropriate countermeasures" refer to the countermeasures that the system automatically selects based on the user's emotional information.

[0969] "Translation result" refers to business report data translated into another language by a translation engine.

[0970] "Database" refers to a system or software for storing business report data, translation results, and emotional information.

[0971] "Display means" refers to a means for visually displaying the translation result and emotional information to the user.

[0972] The present invention aims to improve user response, particularly in customer support and review management, on an online shopping site that incorporates a multilingual emotion analysis system. The specific configuration and operation procedure of the system based on this embodiment are described below.

[0973] The system begins when a user inputs a business report using a device such as a smartphone, tablet, or PC. The data entered by the user in their native language is sent to a server by the device. The server first stores the received business report data in a database.

[0974] Next, the server uses an emotion analysis engine to analyze the user's emotions from the business report data. Based on this analysis, appropriate emotional information is extracted. Based on the emotional information, the server automatically selects an appropriate response. For example, if the user is determined to be "anxious," a prompt response is required.

[0975] The server then passes the business report data to a translation engine for translation into multiple languages. The translation results and emotion information are then stored in a database. The server then sends the translation results and emotion information to the terminal and displays them for the user to visually confirm.

[0976] The implementation of this system uses the following hardware and software:

[0977] Hardware: Smartphone, tablet, PC, server (cloud-based is also acceptable)

[0978] Software: Python, Flask (web server framework), Google Translate API (translation engine), nlp_emotion (emotion analysis library)

[0979] As a concrete example, consider the case where a user enters and submits the following in Japanese: "My item hasn't arrived. I'm very worried." This data is processed as follows:

[0980] 1. "Anxiety" is extracted as the result of analysis by the emotion analysis engine.

[0981] 2. The translation engine translates this into English as, "The product has not arrived. I am very anxious."

[0982] 3. The server stores this data and displays the translation results and emotion information to the user.

[0983] An example prompt for a generative AI model might look like this:

[0984] "Analyze the following sentence using an emotion recognition engine and output its emotion. Sentence: "My item hasn't arrived. I'm very worried.""

[0985] In this way, the present invention is a system that combines multilingual support and emotion analysis to understand user emotions and provide appropriate and prompt responses, particularly in customer support and review management on online shopping sites.

[0986] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0987] Step 1:

[0988] The user enters a business report

[0989] Users use devices such as smartphones, tablets, and PCs to enter business reports and reviews in their native language. The entered data includes the content of the report or review. For example: "The product hasn't arrived. I'm very worried."

[0990] Step 2:

[0991] The device sends the data to the server

[0992] The terminal transmits the business report data input by the user to the server via an HTTP POST request, and the data includes the report content and language information.

[0993] Step 3:

[0994] The server receives and stores the data

[0995] The server receives the business report data sent from the terminal. The received data is stored in a database. For example, the following data is stored:

[0996] json

[0997] {

[0998] "content": "My item hasn't arrived. I'm very worried.",

[0999] "language": "ja"

[1000] }

[1001] Step 4:

[1002] The server analyzes the sentiment using a sentiment analysis engine.

[1003] The server passes the saved business report data to the emotion analysis engine, which analyzes the emotions. The analyzed emotion information (e.g., "anxiety") is stored in a database.

[1004] Step 5:

[1005] The server automatically selects the appropriate countermeasure.

[1006] Based on the results of emotion analysis, the server automatically selects the appropriate response. For example, if the emotion analyzed is "anxiety," a prompt response is required.

[1007] Step 6:

[1008] The server translates the business report data.

[1009] The server passes the saved business report data to a translation engine, which translates it into multiple languages ​​(e.g., English). The translated content is saved back into the database. For example, "The product has not arrived. I am very anxious." is translated into "The product has not arrived. I am very anxious."

[1010] Step 7:

[1011] The server sends the translation results and emotion information

[1012] When a user checks the contents of a business report, the device requests the translation result and emotion information from the server based on the selected language (e.g., English). The server retrieves the necessary data from the database and sends it to the device.

[1013] Step 8:

[1014] The device displays the translation results and emotional information.

[1015] The device displays the translation result and emotional information it receives on the screen. For example, the user can see the translation result "The product has not arrived. I am very anxious." along with the emotional information "anxiety."

[1016] This process allows the system to understand the user's emotions and quickly respond appropriately based on those emotions.

[1017] 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.

[1018] 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.

[1019] 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.

[1020] [Third embodiment]

[1021] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1022] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1023] 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).

[1024] 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.

[1025] 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.

[1026] 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).

[1027] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1028] 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.

[1029] 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.

[1030] 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.

[1031] 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.

[1032] 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."

[1033] This invention aims to realize a multilingual business reporting system, and will be described in detail in the following form. The entire system consists of three main components: users, terminals, and servers. This system allows multinational members to smoothly submit business reports across language barriers.

[1034] System Overview

[1035] In this system, users input business reports in their native language, and the server receives, translates, and distributes the reports. The translated results are then displayed in the appropriate language for each member.

[1036] User operations

[1037] The user opens the work report input screen on the terminal and inputs the work report in their native language. For example, they might input "Today's work involved the initial design of a new project" in Japanese. When the user presses the send button, the terminal sends the report data to the server. At this time, the report content and the language information used by the user are included.

[1038] Device behavior

[1039] The terminal sends the business report data entered by the user to the server. Specifically, the terminal uses an HTTP POST request to send the business report data to the server. This request includes the report content and the user's language information.

[1040] Server Operation

[1041] Receiving and storing report data

[1042] The server receives the business report data sent from the terminal. The received data is stored in a database. For example, suppose the server receives the following data:

[1043] json

[1044] {

[1045] "content": "Today's work involved initial design of a new project.",

[1046] "language": "ja"

[1047] }

[1048] The server temporarily stores this data in a database.

[1049] Using a translation engine

[1050] The server passes the saved business report data to the translation engine and requests translation. The translation engine translates the business report into multiple languages ​​(e.g., English, French, and Spanish). For example, when a Japanese report is translated into English, it becomes, "Today, I worked on the initial design of a new project."

[1051] Storage and distribution of translation results

[1052] The server stores the translated business reports in a database. The stored translation results are dynamically provided in response to user requests. For example, if a user who prefers English checks a report, the server provides the report translated into English.

[1053] Specific examples

[1054] Here are some concrete examples:

[1055] 1. User report input

[1056] What the user types: "Today, I worked on the initial design for a new project."

[1057] Language: Japanese

[1058] 2. Data transmission from the device

[1059] Data sent by the device:

[1060] json

[1061] {

[1062] "content": "Today's work involved initial design of a new project.",

[1063] "language": "ja"

[1064] }

[1065] 3. Server Reception and Translation

[1066] The server received:

[1067] json

[1068] {

[1069] "content": "Today's work involved initial design of a new project.",

[1070] "language": "ja"

[1071] }

[1072] Translation result (English): "Today, I worked on the initial design of a new project."

[1073] 4. Report Distribution

[1074] If the user selects English, the server provides the user with the following data:

[1075] json

[1076] {

[1077] "content": "Today, I worked on the initial design of a new project."

[1078] }

[1079] In this way, the system allows multinational members to easily submit business reports and have the contents of those reports confirmed in a language that other members can understand.

[1080] The processing flow will be explained below.

[1081] Step 1:

[1082] The user opens the work report input screen on the terminal. The user inputs the work report in their native language. For example, they input "Today's work involved the initial design of a new project" in Japanese.

[1083] Step 2:

[1084] The user completes the input and presses the send button. The terminal structures the input business report data. Specifically, it generates the following data structure:

[1085] json

[1086] {

[1087] "content": "Today's work involved initial design of a new project.",

[1088] "language": "ja"

[1089] }

[1090] Step 3:

[1091] The device sends this data to the server as an HTTP POST request, for example to the / report endpoint.

[1092] Step 4:

[1093] The server receives the POST request and extracts the report data and language information from the request body.

[1094] Step 5:

[1095] The server stores the received data in a database, including the report content and the original language information.

[1096] Step 6:

[1097] The server invokes the translation engine, which translates the report into the specified language (e.g., English, French, Spanish, etc.).

[1098] Step 7:

[1099] The translation engine receives the translation result. For example, in English, the translation result might be "Today, I worked on the initial design of a new project."

[1100] Step 8:

[1101] The server stores the translation results in a database, which includes the original text and the translated report content in each language.

[1102] Step 9:

[1103] The user opens the application on their device to view the report. The user selects the display language (e.g., English).

[1104] Step 10:

[1105] The device sends an HTTP GET request to the server based on the selected language, including the language parameter (e.g., / report?language=en).

[1106] Step 11:

[1107] The server retrieves the report in the requested language from its database.

[1108] Step 12:

[1109] The server returns the search results to the device as an HTTP response, which includes the translated report in the selected language.

[1110] Step 13:

[1111] The device displays the received report to the user. For example, it displays "Today, I worked on the initial design of a new project." in English.

[1112] In this way, the system realizes multilingual business reports through each step.

[1113] Example 1

[1114] 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."

[1115] In a work environment involving multinational members, language barriers are a major obstacle to smooth business reporting and information sharing. This can lead to misunderstandings and reduced business efficiency. Conventional systems rely on one language or require expensive translation services. To improve this situation, it is necessary to provide a user-friendly business reporting system that supports multiple languages ​​in real time.

[1116] 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.

[1117] In this invention, the server includes a means for transmitting data including user language information from the terminal to the server, a means for the server to retrieve and provide appropriate data from a database based on a user request, and a means for the terminal to use an HTTP POST request to transmit data to the server, thereby enabling efficient and smooth business reporting and information sharing among users of multiple nationalities.

[1118] "User" means any person or organization that accesses the system to enter business reports and receive information.

[1119] A "terminal" is a device such as a computer, smartphone, or tablet that a user uses to input business reports and send and receive data.

[1120] A "server" is a central computer system that receives and stores data sent from terminals, performs the necessary processing, and provides the results.

[1121] "Business report data" is text information in which a user describes the details of business activities, and is data that is processed after being entered into the system.

[1122] "Translation Engine" means software or services for automatically translating business report data into other languages.

[1123] "Storage" means storing the received data in a storage medium such as a database so that it can be accessed later.

[1124] "Translation engine means" refers to hardware or software that provides the function of translating business report data into multiple languages.

[1125] A "member" is an individual or group who has registered with the system to use the system to submit business reports and share information with other users.

[1126] "Appropriate language" refers to a language that each user can understand and that is used by the translation engine to provide translation results.

[1127] An "HTTP POST request" is a form of Internet communication protocol used by a terminal to send data to a server.

[1128] A "database" is an information management system that stores data in an organized manner and enables efficient access when needed.

[1129] A "generative model" is an artificial intelligence model used to generate new information, translations, etc. based on input data.

[1130] A "request" is an action or content of a user requesting a system to perform a specific operation or provide information.

[1131] The present invention relates to a multilingual business report system, which will be described in detail below. In this system, users input business reports in their native language, and the server receives, translates, and distributes the report content. The translation results are displayed in the appropriate language for each member.

[1132] User operations

[1133] The user opens the work report input screen on the device and enters the work report in their native language. For example, they might enter "Today's work involved the initial design of a new project" in Japanese. When the user presses the send button, the device sends this work report data to the server. At this time, the report content and the user's language information are also included.

[1134] Device behavior

[1135] The terminal sends the business report data entered by the user to the server using an HTTP POST request. The sent data includes the report content and the user's language information. For example, the following data is sent:

[1136] json

[1137] {

[1138] "content": "Today's work involved initial design of a new project.",

[1139] "language": "ja"

[1140] }

[1141] Server Operation

[1142] Receiving and storing report data

[1143] The server receives the business report data sent from the terminal and stores it in a database. For example, the server receives the following data:

[1144] json

[1145] {

[1146] "content": "Today's work involved initial design of a new project.",

[1147] "language": "ja"

[1148] }

[1149] The server runs SQL queries to store this data in a database.

[1150] Using a translation engine

[1151] The server passes the saved business report data to a translation engine and requests translation. For example, it uses a translation service such as Google Translate API or DeepL API. It sends the following prompt to the translation engine:

[1152] "Today's work involved the initial design of a new project."

[1153] As a response from the translation engine, for example, you receive the English translation result "Today, I worked on the initial design of a new project."

[1154] Storage and distribution of translation results

[1155] The server stores the translated business reports in a database. For example, it executes the query "INSERT INTO translated_reports (report_id, content, language) VALUES (1, 'Today, I worked on the initial design of a new project.', 'en')". The stored translation results are dynamically provided in response to user requests. For example, if a user who prefers English checks a report, the server provides the report translated into English.

[1156] In this way, this system allows multinational members to easily submit work reports and have the report contents confirmed in a language that other members can understand. This system is realized by combining technologies such as HTTP POST requests, SQL databases, and translation APIs. In addition, appropriate translations are provided based on each user's language settings, enabling smooth work reports across language barriers.

[1157] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1158] Step 1:

[1159] The user inputs a business report.

[1160] The user opens the work report input screen on the device and enters the work report in the text field in their native language. For example, they might enter "Today's work involved the initial design of a new project" in Japanese. Once they have completed the input, they press the send button.

[1161] Input: User-entered business report text and selected native language

[1162] Output: Data packet created by pressing the send button

[1163] Step 2:

[1164] The terminal transmits the business report data to the server.

[1165] When the send button is pressed, the device creates an HTTP POST request and sends the business report text and native language information to the server. For example, the following JSON data is included in the packet:

[1166] json

[1167] {

[1168] "content": "Today's work involved initial design of a new project.",

[1169] "language": "ja"

[1170] }

[1171] Input: Business report text and native language information

[1172] Output: Data sent to the server as an HTTP POST request

[1173] Step 3:

[1174] The server receives and stores the transmitted business report data.

[1175] The server analyzes the data received from the device and stores it in a database, for example by executing the following SQL query:

[1176] sql

[1177] INSERT INTO reports (content, language) VALUES ('Today's work involved the initial design of a new project.', 'ja')

[1178] Input: Business report data sent via HTTP POST request

[1179] Output: Business report data stored in a database

[1180] Step 4:

[1181] The server passes the business report data to the translation engine.

[1182] The server sends the saved business report to a translation engine to translate it into other languages, for example, by using Google Translate API or DeepL API. It sends the following prompt to the translation engine:

[1183] "Today's work involved the initial design of a new project."

[1184] Input: Saved business report data

[1185] Output: Translation request data sent to the translation engine

[1186] Step 5:

[1187] The translation engine returns the translation results.

[1188] The translation engine translates the Japanese data into the specified language and returns the result to the server. For example, the English translation result "Today, I worked on the initial design of a new project." is returned.

[1189] Input: Translation request data

[1190] Output: Translation result data

[1191] Step 6:

[1192] The server stores the translation results in a database.

[1193] Once the server receives the translation results, it stores them in a database, for example by executing the following SQL query:

[1194] sql

[1195] INSERT INTO translated_reports (report_id, content, language) VALUES (1, 'Today, I worked on the initial design of a new project.', 'en')

[1196] Input: Translation result data

[1197] Output: Translation data stored in a database

[1198] Step 7:

[1199] A user requests translated business report data.

[1200] A user sends a request to the server through a terminal to obtain a business report translated in a specific language.

[1201] Input: Language specification request from user

[1202] Output: Request data to the server

[1203] Step 8:

[1204] The server provides the appropriate translation results.

[1205] The server retrieves the business report stored in the requested language from the database and provides it to the user. For example, if you request a business report in English, it will return the following data:

[1206] json

[1207] {

[1208] "content": "Today, I worked on the initial design of a new project."

[1209] }

[1210] Input: A request from the user

[1211] Output: Translation result data

[1212] Through these processing steps, the system enables multinational members to smoothly report on their work across language barriers and obtain the necessary information in the appropriate language.

[1213] (Application example 1)

[1214] 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."

[1215] In environments where multinational factory staff coexist, language barriers can make it difficult to smoothly share maintenance information and work reports. In particular, if machine maintenance reports are not properly communicated, this can lead to reduced work efficiency and safety risks. To solve this problem, there is a need to provide a reporting system that supports multiple languages.

[1216] 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.

[1217] In this invention, the server includes means for users to input work reports in their native language, means for terminals to transmit work report data to the server, means for the server to receive and store the transmitted work report data, translation engine means for the server to translate the work report data into other languages, means for the server to distribute the translated work report data in an appropriate language for each member, means for factory machines to automatically generate and transmit maintenance reports, and means for staff to check the reports on communication terminals used by staff. This enables multinational factory staff to quickly and accurately share maintenance information across language barriers.

[1218] A "multilingual business report system" is a system in which a user inputs a business report in their native language, and the report is translated into other languages ​​and distributed to each member in the appropriate language.

[1219] "Means for users to input business reports in their own native language" refers to means for providing an interface that allows users to input business details in their own native language.

[1220] The "means for the terminal to transmit business report data to the server" refers to a means that utilizes communication technology for transmitting data containing input business reports from the terminal to the server.

[1221] The "means for the server to receive and store the business report data transmitted" is a means for the server to receive the business report data transmitted from the terminal and store it in a database or the like.

[1222] The "translation engine means for the server to translate the business report data into another language" refers to the means by which the server converts the transmitted data into another language using a translation engine.

[1223] "Means for the server to deliver translated business report data in an appropriate language for each member" refers to means for providing translated data in the language selected by each member.

[1224] "Means for factory machines to automatically generate and send maintenance reports" refers to a means by which machines installed in a factory detect their own condition and the details of maintenance that is required, and automatically generate and send a report to a server.

[1225] "Means for staff to check reports on communication devices used by staff" refers to means for staff to check translated maintenance reports and work reports using communication devices such as smartphones and tablets.

[1226] This invention aims to realize a multilingual business report system, and is configured so that users, terminals, and servers can work together to share business reports across language barriers among members of various nationalities. This system is particularly notable in a factory environment in that it can automatically report machine maintenance.

[1227] This system first provides an interface for users to input business reports in their native language. For example, a text input field is displayed on the screen of a smartphone or tablet, allowing users to "input business details in their native language." The input business report data is then sent from the device to the server.

[1228] The server receives the data sent via the HTTP POST request, stores it in a database, and passes it to a translation engine to translate it into other languages, typically a generative AI model such as the Google Translate API.

[1229] The translated results are stored in a database by the server and are provided appropriately according to the language selected by the user. For example, "a report entered in Japanese can be translated into English, French, or Spanish, and members who select each language can view it on their smartphones or tablets."

[1230] Furthermore, factory machines automatically detect maintenance status and report the details. For example, "if a robot's operating temperature exceeds the normal range, a maintenance report is automatically generated and sent to the server." These automatic reports are also translated and distributed, allowing multinational staff to receive the reports in the appropriate language and respond quickly.

[1231] Examples include the following operations:

[1232] 1. The user types in Japanese, "The robot's operating temperature has exceeded the normal range. The cooling system needs to be inspected."

[1233] 2. This report data is sent from the terminal to the server, which receives it and stores it in a database.

[1234] 3. The server uses the Google Translate API to translate the message as "The robot's operating temperature has exceeded the normal range. The cooling system needs to be checked." and saves this data in the database.

[1235] 4. When staff who prefer English check the report on their smartphone, the translated English report will be displayed.

[1236] Example prompt sentence:

[1237] "The robot's operating temperature has exceeded the normal range. The cooling system needs to be inspected."

[1238] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1239] Step 1:

[1240] A user inputs a business report in his / her native language.

[1241] Input: The user types in their native language, "The robot's operating temperature has exceeded the normal range. The cooling system needs to be inspected."

[1242] How it works: The user enters a business report into the input field displayed on the screen of their smartphone or tablet.

[1243] Step 2:

[1244] The terminal transmits the business report data to the server.

[1245] Input: Business report data entered by the user and its language information.

[1246] Operation: The terminal sends business report data to the server using an HTTP POST request.

[1247] Output: Business report data is sent to the server.

[1248] Step 3:

[1249] The server receives and stores the transmitted business report data.

[1250] Input: Business report data (e.g., "The robot's operating temperature has exceeded the normal range. The cooling system needs to be inspected.") and its language information.

[1251] Operation: The server stores the received data in a database.

[1252] Output: Business report data stored in a database.

[1253] Step 4:

[1254] The server uses a translation engine means for translating the business report data into other languages.

[1255] Input: Business report data and its language information stored in the database.

[1256] How it works: The server uses the Google Translate API to translate business report data into other languages.

[1257] Output: Translated operational report data (e.g., "The robot's operating temperature has exceeded the normal range. The cooling system needs to be checked.").

[1258] Step 5:

[1259] The server stores the translated business report data in a database.

[1260] Input: Translated business report data.

[1261] How it works: The server stores the translated data in a database.

[1262] Output: Business report data stored in a database in multiple languages.

[1263] Step 6:

[1264] The server distributes the translated business report data to each member in the appropriate language.

[1265] Input: User's language preference and translation data stored in the database.

[1266] Operation: The server provides business report data in the appropriate language based on the user's language preference.

[1267] Output: Translated business report data displayed on the user's communication terminal.

[1268] Step 7:

[1269] Factory machines automatically generate and transmit maintenance reports.

[1270] Input: Machine status information (e.g. operating temperature, error codes, etc.).

[1271] How it works: Factory machines automatically detect their own condition, generate maintenance reports, and send them to a server.

[1272] Output: Auto-generated maintenance report data sent to the server.

[1273] Step 8:

[1274] Check the report on the communication device used by the staff.

[1275] Input: Staff communication terminals and translated business report data provided by the server.

[1276] Operation: A staff member accesses the server using a communication terminal and checks the translated business report data.

[1277] Output: Translated business report data displayed on the staff's communication terminal screen.

[1278] 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.

[1279] This invention further improves the multilingual business reporting system by incorporating an emotion engine that recognizes the user's emotions. This not only overcomes language barriers, but also understands the user's emotions expressed in the report content, making it possible to support richer communication.

[1280] System Overview

[1281] In this system, users input business reports in their native language, and the server receives, translates, analyzes emotions, and distributes the reports. By incorporating an emotion engine, the system can also analyze the emotional elements of the report content and reflect them in the translation and display results.

[1282] User operations

[1283] The user opens the work report input screen on the terminal and inputs the work report in their native language. For example, they might input "Today's work involved the initial design of a new project. I'm very happy" in Japanese. When the user presses the send button, the terminal sends the report data and the user's emotional information to the server. This includes the report content, the language used by the user, and the emotional information.

[1284] Device behavior

[1285] The terminal sends the business report data entered by the user to the server. Specifically, it sends the business report data and emotion information to the server using an HTTP POST request. This request includes the report content, user language information, and emotion information.

[1286] Server Operation

[1287] Receiving and storing report data

[1288] The server receives the business report data sent from the terminal. The received data is stored in a database. For example, suppose the server receives the following data:

[1289] json

[1290] {

[1291] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[1292] "language": "ja",

[1293] "emotion": "happy"

[1294] }

[1295] The server temporarily stores this data in a database.

[1296] Using a sentiment analysis engine

[1297] The server uses an emotion engine to analyze emotions from the report content. The emotion engine analyzes the report content and identifies and tags the emotions contained in the content.

[1298] Using a translation engine

[1299] The server passes the saved business report data to the translation engine and requests translation. The translation engine translates the business report into multiple languages ​​(e.g., English, French, and Spanish). For example, when a Japanese report is translated into English, it becomes, "Today, I worked on the initial design of a new project. I am very happy."

[1300] Saving translation results and emotional information

[1301] The server stores the translated business reports and emotional information in a database. The stored data includes the report content translated into each language and the associated emotional information.

[1302] Delivery of translation results and emotional information

[1303] The user opens the application on their device to view the report. The user selects the display language (e.g., English). The device sends an HTTP GET request to the server based on the selected language. The request includes a language parameter (e.g., / report?language=en).

[1304] The server searches the database for the report in the requested language and sends it to the device along with emotional information. The report received by the device is displayed as emotional information (joy), for example, along with the content, "Today, I worked on the initial design of a new project. I am very happy."

[1305] Specific examples

[1306] Here are some concrete examples:

[1307] 1. User report input

[1308] What you type: "Today I worked on the initial design for a new project. I'm very happy."

[1309] Language: Japanese

[1310] Emotion: Joy (User can specify emotion when typing or the system will recognize it automatically)

[1311] 2. Data transmission from the device

[1312] Data sent by the device:

[1313] json

[1314] {

[1315] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[1316] "language": "ja",

[1317] "emotion": "happy"

[1318] }

[1319] 3. Server Reception and Translation

[1320] The server received:

[1321] json

[1322] {

[1323] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[1324] "language": "ja",

[1325] "emotion": "happy"

[1326] }

[1327] Translation result (English): "Today, I worked on the initial design of a new project. I am very happy."

[1328] 4. Reporting and emotional information distribution

[1329] If the user selects English, the server provides the user with the following data:

[1330] json

[1331] {

[1332] "content": "Today, I worked on the initial design of a new project. I am very happy.",

[1333] "emotion": "happy"

[1334] }

[1335] In this way, through each step, the system realizes business reports that are multilingual and emotionally aware.

[1336] The processing flow will be explained below.

[1337] Step 1:

[1338] The user opens the work report input screen on the terminal. The user inputs the work report in their native language. For example, they might input "Today's work involved the initial design of a new project. I'm very happy" in Japanese.

[1339] Step 2:

[1340] The user completes the input and presses the send button. The terminal structures the input business report data. Specifically, it generates the following data structure:

[1341] json

[1342] {

[1343] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[1344] "language": "ja"

[1345] }

[1346] Step 3:

[1347] The device calls the emotion engine and analyzes the emotion from the user's report. The emotion engine identifies the emotion "happy" from "I'm very happy."

[1348] Step 4:

[1349] The device builds the data with added emotion information. Specifically, it generates the following data structure:

[1350] json

[1351] {

[1352] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[1353] "language": "ja",

[1354] "emotion": "happy"

[1355] }

[1356] Step 5:

[1357] The device sends this data to the server as an HTTP POST request, for example to the / report endpoint.

[1358] Step 6:

[1359] The server receives the POST request and extracts the report data, language information, and sentiment information from the request body.

[1360] Step 7:

[1361] The server stores the received data in a database, for example:

[1362] json

[1363] {

[1364] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[1365] "language": "ja",

[1366] "emotion": "happy"

[1367] }

[1368] Step 8:

[1369] The server passes the saved data to a translation engine and requests a translation, which then translates the Japanese report into English.

[1370] Step 9:

[1371] Receive translation results from the translation engine. For example, receive the following translation results:

[1372] json

[1373] {

[1374] "translatedText": "Today, I worked on the initial design of a new project. I am very happy."

[1375] }

[1376] Step 10:

[1377] The server adds emotion information to the translation results and stores them in a database. Specifically, it stores them as follows:

[1378] json

[1379] {

[1380] "originalContent": "Today's work involved the initial design of a new project. I'm very happy about that.",

[1381] "originalLanguage": "ja",

[1382] "translatedContent": "Today, I worked on the initial design of a new project. I am very happy.",

[1383] "translatedLanguage": "en",

[1384] "emotion": "happy"

[1385] }

[1386] Step 11:

[1387] The user opens the application on their device to view the report. The user selects the display language (e.g., English).

[1388] Step 12:

[1389] The device sends an HTTP GET request to the server based on the selected language, including the language parameter (e.g., / report?language=en).

[1390] Step 13:

[1391] The server searches the database for the report content and emotion information in the requested language and returns it to the device as an HTTP response. Specifically, it returns the following data:

[1392] json

[1393] {

[1394] "translatedContent": "Today, I worked on the initial design of a new project. I am very happy.",

[1395] "emotion": "happy"

[1396] }

[1397] Step 14:

[1398] The device displays the received report and emotional information to the user. For example, "Today, I worked on the initial design of a new project. I am very happy." along with the emotional information (joy).

[1399] In this way, through each step, the system realizes business reports that are multilingual and emotionally aware.

[1400] Example 2

[1401] 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."

[1402] Conventional multilingual business reporting systems have the ability to translate report content into other languages, but lack the ability to understand the user's emotions and enrich communication. This has led to problems such as users' emotions not being reflected in the report, making accurate communication difficult.

[1403] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an emotion analysis engine means for analyzing business report data and identifying emotion information, a translation engine means for translating the business report data into another language, and a means for saving the translated business report data and emotion information. This enables richer communication by identifying the user's emotion contained in the business report and reflecting it in the translation result.

[1404] "User" refers to a person who uses the system to input and review business reports.

[1405] "Terminal" refers to a computer that a user uses to input business reports and send them to a server.

[1406] "Business report data" refers to information including the content of a business report entered by a user in their native language.

[1407] "Server" refers to a computer that has the functions of receiving, analyzing, translating, storing, and distributing business report data sent from a terminal.

[1408] "Sentiment Analysis Engine" refers to software for identifying and tagging user emotions from received business report data.

[1409] "Translation Engine" means software for translating business report data into multiple languages.

[1410] "Database" refers to a structured data storage system for the server to store business report data, translation results, and emotion information.

[1411] "Emotion information" refers to information indicating the user's emotions identified by the emotion analysis engine.

[1412] This invention incorporates an emotion analysis function into a multilingual business report system, enabling it to understand users' emotions and realize richer communication. In this system, users input business reports in their native language, and the server receives, translates, analyzes emotions, and distributes the reports.

[1413] The process begins with the user entering and sending a work report via a terminal. The user opens the work report input screen on the terminal and enters specific details in their native language, such as, "Today's work involved the initial design of a new project. I'm very happy about that." Once the input is complete, the user presses the send button. At this time, the work report content, the language used by the user, and emotional information are included.

[1414] The device sends business report data to the server using an HTTP POST request. This request includes the report content, language information, and emotion information. The data sent from the device is received by the server and stored in a database.

[1415] The server passes the saved business report data to a sentiment analysis engine to identify emotional information. For example, if the report contains the phrase "I'm very happy," the sentiment analysis engine tags it as "happy." The server then passes the business report content to a translation engine to translate it into other languages. For example, when a Japanese report content is translated into English, it becomes "Today, I worked on the initial design of a new project. I am very happy."

[1416] The translated business report data and emotion information are then saved back into the database. To check the report content, the user opens the application on their device and selects the display language. The device then sends an HTTP GET request to the server based on the selected language, and the server retrieves the report content and emotion information in the requested language from the database and sends it to the device. For example, if the user selects English, the content displayed will be "Today, I worked on the initial design of a new project. I am very happy.", with "happy" displayed as the emotion information.

[1417] As an example of a specific operation, suppose the user enters the following prompt:

[1418] "Today's work involved the initial design of a new project. I'm very happy."

[1419] The server analyzes this, identifies the emotional information as "happy," and translates it into "Today, I worked on the initial design of a new project. I am very happy." In this way, this system realizes business reports that are both multilingual and emotionally recognizable.

[1420] In practicing the invention, a general generative AI model can be used as the engine for sentiment analysis and translation, enabling advanced analysis of user input and supporting more accurate and richer communication.

[1421] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1422] Step 1: User enters business report

[1423] Specific operation: The user opens the work report input screen on the terminal, enters, for example, "Today's work involved the initial design of a new project. I'm very happy," and presses the send button.

[1424] Input: The business report content entered by the user, and the language information used (e.g., Japanese).

[1425] Output: Business report data (content, language information, emotion information) is generated on the device and ready to be sent.

[1426] Step 2: The device sends the data to the server

[1427] Specific operation: The device generates an HTTP POST request and sends it to the server in JSON format, including the work report data (e.g., "content": "Today's work involved the initial design of a new project. I'm very happy.", "language": "ja", "emotion": "happy").

[1428] Input: Business report data entered by the user.

[1429] Output: The business report data is sent from the terminal to the server as an HTTP POST request.

[1430] Step 3: The server receives and stores the data

[1431] Specific operation: The server receives the HTTP POST request sent from the device, extracts the report data, and stores it in a database.

[1432] Input: Work report data sent from the device (e.g., "content": "Today's work involved the initial design of a new project. I'm very happy.", "language": "ja", "emotion": "happy").

[1433] Output: Business report data stored in a database.

[1434] Step 4: The server performs sentiment analysis

[1435] Specific operation: The server passes business report data to the emotion analysis engine, which analyzes the report content and identifies the user's emotion. For example, it identifies "happy" from a phrase such as "I'm very happy."

[1436] Input: Business report data stored in a database.

[1437] Output: Sentiment information ("happy") from the sentiment analysis engine.

[1438] Step 5: The server does the translation

[1439] Specific operation: The server passes the business report data to the translation engine, which then translates the report content into another language. For example, when translating from Japanese to English, "Today's work involved the initial design of a new project" becomes "Today, I worked on the initial design of a new project."

[1440] Input: Business report data stored in a database, and sentiment information.

[1441] Output: Translated business report data (e.g., "Today, I worked on the initial design of a new project. I am very happy.") along with the original language and sentiment information ("happy").

[1442] Step 6: The server stores the translation results and emotion information

[1443] Specific operation: The server stores the translated business report data and emotion information in a database.

[1444] Input: Translated business report data and sentiment information.

[1445] Output: Translation results and sentiment information added to and saved in a database.

[1446] Step 7: Deliver data as requested by the user

[1447] Specific operation: The user specifies the language selected on the device and sends a request to the server so that business report data can be displayed. The server retrieves the corresponding language and emotion information from the database and delivers it to the device.

[1448] Input: HTTP GET request from user (e.g. " / report?language=en").

[1449] Output: Translation results and emotional information from the server to the device (e.g., "Today, I worked on the initial design of a new project. I am very happy.").

[1450] This series of processing steps enables users to effectively handle business reports that are multilingual and contain emotional information.

[1451] (Application example 2)

[1452] 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."

[1453] Conventional multilingual business reporting systems simply translate content without considering the user's emotions, making it difficult to respond based on emotions. Therefore, there is a need to understand user emotions and respond appropriately, especially in customer support and review management on online shopping sites. The present invention aims to solve this problem by utilizing user emotional information to provide more appropriate and prompt customer support.

[1454] The identification process by the identification 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 analyzing emotions in business report data using an emotion analysis engine, means for automatically selecting appropriate countermeasures based on the analyzed emotion information, and means for the terminal to display the translation result and the emotion information. This makes it possible to understand the user's emotions and provide appropriate countermeasures based on them.

[1455] "User" refers to a person who uses the system to input business reports.

[1456] "Native language" refers to the language with which the user is most familiar.

[1457] "Work report" refers to information that users record and report on their daily work, progress, emotions, etc.

[1458] "Terminal" refers to a device through which a user inputs business reports and communicates with a server.

[1459] "Server" refers to a computer system that has the functionality to receive, store, analyze, translate, and distribute business report data.

[1460] "Business report data" refers to data including the contents of a business report entered by a user.

[1461] "Translation Engine" means software or a system for translating business reporting data into other languages.

[1462] "Translated business report data" refers to business report data that has been converted into another language by a translation engine.

[1463] "Member" means a person or group that can receive business reporting data within the system.

[1464] "Emotion analysis engine" refers to software or a system for analyzing user emotions from business report data and extracting appropriate emotional information.

[1465] "Emotion information" refers to information that indicates the user's emotional state obtained by the emotion analysis engine.

[1466] "Appropriate countermeasures" refer to the countermeasures that the system automatically selects based on the user's emotional information.

[1467] "Translation result" refers to business report data translated into another language by a translation engine.

[1468] "Database" refers to a system or software for storing business report data, translation results, and emotional information.

[1469] "Display means" refers to a means for visually displaying the translation result and emotional information to the user.

[1470] The present invention aims to improve user response, particularly in customer support and review management, on an online shopping site that incorporates a multilingual emotion analysis system. The specific configuration and operation procedure of the system based on this embodiment are described below.

[1471] The system begins when a user inputs a business report using a device such as a smartphone, tablet, or PC. The data entered by the user in their native language is sent to a server by the device. The server first stores the received business report data in a database.

[1472] Next, the server uses an emotion analysis engine to analyze the user's emotions from the business report data. Based on this analysis, appropriate emotional information is extracted. Based on the emotional information, the server automatically selects an appropriate response. For example, if the user is determined to be "anxious," a prompt response is required.

[1473] The server then passes the business report data to a translation engine for translation into multiple languages. The translation results and emotion information are then stored in a database. The server then sends the translation results and emotion information to the terminal and displays them for the user to visually confirm.

[1474] The implementation of this system uses the following hardware and software:

[1475] Hardware: Smartphone, tablet, PC, server (cloud-based is also acceptable)

[1476] Software: Python, Flask (web server framework), Google Translate API (translation engine), nlp_emotion (emotion analysis library)

[1477] As a concrete example, consider the case where a user enters and submits the following in Japanese: "My item hasn't arrived. I'm very worried." This data is processed as follows:

[1478] 1. "Anxiety" is extracted as the result of analysis by the emotion analysis engine.

[1479] 2. The translation engine translates this into English as, "The product has not arrived. I am very anxious."

[1480] 3. The server stores this data and displays the translation results and emotion information to the user.

[1481] An example prompt for a generative AI model might look like this:

[1482] "Analyze the following sentence using an emotion recognition engine and output its emotion. Sentence: "My item hasn't arrived. I'm very worried.""

[1483] In this way, the present invention is a system that combines multilingual support and emotion analysis to understand user emotions and provide appropriate and prompt responses, particularly in customer support and review management on online shopping sites.

[1484] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1485] Step 1:

[1486] The user enters a business report

[1487] Users use devices such as smartphones, tablets, and PCs to enter business reports and reviews in their native language. The entered data includes the content of the report or review. For example: "The product hasn't arrived. I'm very worried."

[1488] Step 2:

[1489] The device sends the data to the server

[1490] The terminal transmits the business report data input by the user to the server via an HTTP POST request, and the data includes the report content and language information.

[1491] Step 3:

[1492] The server receives and stores the data

[1493] The server receives the business report data sent from the terminal. The received data is stored in a database. For example, the following data is stored:

[1494] json

[1495] {

[1496] "content": "My item hasn't arrived. I'm very worried.",

[1497] "language": "ja"

[1498] }

[1499] Step 4:

[1500] The server analyzes the sentiment using a sentiment analysis engine.

[1501] The server passes the saved business report data to the emotion analysis engine, which analyzes the emotions. The analyzed emotion information (e.g., "anxiety") is stored in a database.

[1502] Step 5:

[1503] The server automatically selects the appropriate countermeasure.

[1504] Based on the results of emotion analysis, the server automatically selects the appropriate response. For example, if the emotion analyzed is "anxiety," a prompt response is required.

[1505] Step 6:

[1506] The server translates the business report data.

[1507] The server passes the saved business report data to a translation engine, which translates it into multiple languages ​​(e.g., English). The translated content is saved back into the database. For example, "The product has not arrived. I am very anxious." is translated into "The product has not arrived. I am very anxious."

[1508] Step 7:

[1509] The server sends the translation results and emotion information

[1510] When a user checks the contents of a business report, the device requests the translation result and emotion information from the server based on the selected language (e.g., English). The server retrieves the necessary data from the database and sends it to the device.

[1511] Step 8:

[1512] The device displays the translation results and emotional information.

[1513] The device displays the translation result and emotional information it receives on the screen. For example, the user can see the translation result "The product has not arrived. I am very anxious." along with the emotional information "anxiety."

[1514] This process allows the system to understand the user's emotions and quickly respond appropriately based on those emotions.

[1515] 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.

[1516] 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.

[1517] 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.

[1518] [Fourth embodiment]

[1519] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1520] 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.

[1521] 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).

[1522] 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.

[1523] 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.

[1524] 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).

[1525] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1526] 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.

[1527] 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.

[1528] 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.

[1529] 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.

[1530] 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.

[1531] 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."

[1532] This invention aims to realize a multilingual business reporting system, and will be described in detail in the following form. The entire system consists of three main components: users, terminals, and servers. This system allows multinational members to smoothly submit business reports across language barriers.

[1533] System Overview

[1534] In this system, users input business reports in their native language, and the server receives, translates, and distributes the reports. The translated results are then displayed in the appropriate language for each member.

[1535] User operations

[1536] The user opens the work report input screen on the terminal and inputs the work report in their native language. For example, they might input "Today's work involved the initial design of a new project" in Japanese. When the user presses the send button, the terminal sends the report data to the server. At this time, the report content and the language information used by the user are included.

[1537] Device behavior

[1538] The terminal sends the business report data entered by the user to the server. Specifically, the terminal uses an HTTP POST request to send the business report data to the server. This request includes the report content and the user's language information.

[1539] Server Operation

[1540] Receiving and storing report data

[1541] The server receives the business report data sent from the terminal. The received data is stored in a database. For example, suppose the server receives the following data:

[1542] json

[1543] {

[1544] "content": "Today's work involved initial design of a new project.",

[1545] "language": "ja"

[1546] }

[1547] The server temporarily stores this data in a database.

[1548] Using a translation engine

[1549] The server passes the saved business report data to the translation engine and requests translation. The translation engine translates the business report into multiple languages ​​(e.g., English, French, and Spanish). For example, when a Japanese report is translated into English, it becomes, "Today, I worked on the initial design of a new project."

[1550] Storage and distribution of translation results

[1551] The server stores the translated business reports in a database. The stored translation results are dynamically provided in response to user requests. For example, if a user who prefers English checks a report, the server provides the report translated into English.

[1552] Specific examples

[1553] Here are some concrete examples:

[1554] 1. User report input

[1555] What the user types: "Today, I worked on the initial design for a new project."

[1556] Language: Japanese

[1557] 2. Data transmission from the device

[1558] Data sent by the device:

[1559] json

[1560] {

[1561] "content": "Today's work involved initial design of a new project.",

[1562] "language": "ja"

[1563] }

[1564] 3. Server Reception and Translation

[1565] The server received:

[1566] json

[1567] {

[1568] "content": "Today's work involved initial design of a new project.",

[1569] "language": "ja"

[1570] }

[1571] Translation result (English): "Today, I worked on the initial design of a new project."

[1572] 4. Report Distribution

[1573] If the user selects English, the server provides the user with the following data:

[1574] json

[1575] {

[1576] "content": "Today, I worked on the initial design of a new project."

[1577] }

[1578] In this way, the system allows multinational members to easily submit business reports and have the contents of those reports confirmed in a language that other members can understand.

[1579] The processing flow will be explained below.

[1580] Step 1:

[1581] The user opens the work report input screen on the terminal. The user inputs the work report in their native language. For example, they input "Today's work involved the initial design of a new project" in Japanese.

[1582] Step 2:

[1583] The user completes the input and presses the send button. The terminal structures the input business report data. Specifically, it generates the following data structure:

[1584] json

[1585] {

[1586] "content": "Today's work involved initial design of a new project.",

[1587] "language": "ja"

[1588] }

[1589] Step 3:

[1590] The device sends this data to the server as an HTTP POST request, for example to the / report endpoint.

[1591] Step 4:

[1592] The server receives the POST request and extracts the report data and language information from the request body.

[1593] Step 5:

[1594] The server stores the received data in a database, including the report content and the original language information.

[1595] Step 6:

[1596] The server invokes the translation engine, which translates the report into the specified language (e.g., English, French, Spanish, etc.).

[1597] Step 7:

[1598] The translation engine receives the translation result. For example, in English, the translation result might be "Today, I worked on the initial design of a new project."

[1599] Step 8:

[1600] The server stores the translation results in a database, which includes the original text and the translated report content in each language.

[1601] Step 9:

[1602] The user opens the application on their device to view the report. The user selects the display language (e.g., English).

[1603] Step 10:

[1604] The device sends an HTTP GET request to the server based on the selected language, including the language parameter (e.g., / report?language=en).

[1605] Step 11:

[1606] The server retrieves the report in the requested language from its database.

[1607] Step 12:

[1608] The server returns the search results to the device as an HTTP response, which includes the translated report in the selected language.

[1609] Step 13:

[1610] The device displays the received report to the user. For example, it displays "Today, I worked on the initial design of a new project." in English.

[1611] In this way, the system realizes multilingual business reports through each step.

[1612] Example 1

[1613] 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."

[1614] In a work environment involving multinational members, language barriers are a major obstacle to smooth business reporting and information sharing. This can lead to misunderstandings and reduced business efficiency. Conventional systems rely on one language or require expensive translation services. To improve this situation, it is necessary to provide a user-friendly business reporting system that supports multiple languages ​​in real time.

[1615] 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.

[1616] In this invention, the server includes a means for transmitting data including user language information from the terminal to the server, a means for the server to retrieve and provide appropriate data from a database based on a user request, and a means for the terminal to use an HTTP POST request to transmit data to the server, thereby enabling efficient and smooth business reporting and information sharing among users of multiple nationalities.

[1617] "User" means any person or organization that accesses the system to enter business reports and receive information.

[1618] A "terminal" is a device such as a computer, smartphone, or tablet that a user uses to input business reports and send and receive data.

[1619] A "server" is a central computer system that receives and stores data sent from terminals, performs the necessary processing, and provides the results.

[1620] "Business report data" is text information in which a user describes the details of business activities, and is data that is processed after being entered into the system.

[1621] "Translation Engine" means software or services for automatically translating business report data into other languages.

[1622] "Storage" means storing the received data in a storage medium such as a database so that it can be accessed later.

[1623] "Translation engine means" refers to hardware or software that provides the function of translating business report data into multiple languages.

[1624] A "member" is an individual or group who has registered with the system to use the system to submit business reports and share information with other users.

[1625] "Appropriate language" refers to a language that each user can understand and that is used by the translation engine to provide translation results.

[1626] An "HTTP POST request" is a form of Internet communication protocol used by a terminal to send data to a server.

[1627] A "database" is an information management system that stores data in an organized manner and enables efficient access when needed.

[1628] A "generative model" is an artificial intelligence model used to generate new information, translations, etc. based on input data.

[1629] A "request" is an action or content of a user requesting a system to perform a specific operation or provide information.

[1630] The present invention relates to a multilingual business report system, which will be described in detail below. In this system, users input business reports in their native language, and the server receives, translates, and distributes the report content. The translation results are displayed in the appropriate language for each member.

[1631] User operations

[1632] The user opens the work report input screen on the device and enters the work report in their native language. For example, they might enter "Today's work involved the initial design of a new project" in Japanese. When the user presses the send button, the device sends this work report data to the server. At this time, the report content and the user's language information are also included.

[1633] Device behavior

[1634] The terminal sends the business report data entered by the user to the server using an HTTP POST request. The sent data includes the report content and the user's language information. For example, the following data is sent:

[1635] json

[1636] {

[1637] "content": "Today's work involved initial design of a new project.",

[1638] "language": "ja"

[1639] }

[1640] Server Operation

[1641] Receiving and storing report data

[1642] The server receives the business report data sent from the terminal and stores it in a database. For example, the server receives the following data:

[1643] json

[1644] {

[1645] "content": "Today's work involved initial design of a new project.",

[1646] "language": "ja"

[1647] }

[1648] The server runs SQL queries to store this data in a database.

[1649] Using a translation engine

[1650] The server passes the saved business report data to a translation engine and requests translation. For example, it uses a translation service such as Google Translate API or DeepL API. It sends the following prompt to the translation engine:

[1651] "Today's work involved the initial design of a new project."

[1652] As a response from the translation engine, for example, you receive the English translation result "Today, I worked on the initial design of a new project."

[1653] Storage and distribution of translation results

[1654] The server stores the translated business reports in a database. For example, it executes the query "INSERT INTO translated_reports (report_id, content, language) VALUES (1, 'Today, I worked on the initial design of a new project.', 'en')". The stored translation results are dynamically provided in response to user requests. For example, if a user who prefers English checks a report, the server provides the report translated into English.

[1655] In this way, this system allows multinational members to easily submit work reports and have the report contents confirmed in a language that other members can understand. This system is realized by combining technologies such as HTTP POST requests, SQL databases, and translation APIs. In addition, appropriate translations are provided based on each user's language settings, enabling smooth work reports across language barriers.

[1656] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1657] Step 1:

[1658] The user inputs a business report.

[1659] The user opens the work report input screen on the device and enters the work report in the text field in their native language. For example, they might enter "Today's work involved the initial design of a new project" in Japanese. Once they have completed the input, they press the send button.

[1660] Input: User-entered business report text and selected native language

[1661] Output: Data packet created by pressing the send button

[1662] Step 2:

[1663] The terminal transmits the business report data to the server.

[1664] When the send button is pressed, the device creates an HTTP POST request and sends the business report text and native language information to the server. For example, the following JSON data is included in the packet:

[1665] json

[1666] {

[1667] "content": "Today's work involved initial design of a new project.",

[1668] "language": "ja"

[1669] }

[1670] Input: Business report text and native language information

[1671] Output: Data sent to the server as an HTTP POST request

[1672] Step 3:

[1673] The server receives and stores the transmitted business report data.

[1674] The server analyzes the data received from the device and stores it in a database, for example by executing the following SQL query:

[1675] sql

[1676] INSERT INTO reports (content, language) VALUES ('Today's work involved the initial design of a new project.', 'ja')

[1677] Input: Business report data sent via HTTP POST request

[1678] Output: Business report data stored in a database

[1679] Step 4:

[1680] The server passes the business report data to the translation engine.

[1681] The server sends the saved business report to a translation engine to translate it into other languages, for example, by using Google Translate API or DeepL API. It sends the following prompt to the translation engine:

[1682] "Today's work involved the initial design of a new project."

[1683] Input: Saved business report data

[1684] Output: Translation request data sent to the translation engine

[1685] Step 5:

[1686] The translation engine returns the translation results.

[1687] The translation engine translates the Japanese data into the specified language and returns the result to the server. For example, the English translation result "Today, I worked on the initial design of a new project." is returned.

[1688] Input: Translation request data

[1689] Output: Translation result data

[1690] Step 6:

[1691] The server stores the translation results in a database.

[1692] Once the server receives the translation results, it stores them in a database, for example by executing the following SQL query:

[1693] sql

[1694] INSERT INTO translated_reports (report_id, content, language) VALUES (1, 'Today, I worked on the initial design of a new project.', 'en')

[1695] Input: Translation result data

[1696] Output: Translation data stored in a database

[1697] Step 7:

[1698] A user requests translated business report data.

[1699] A user sends a request to the server through a terminal to obtain a business report translated in a specific language.

[1700] Input: Language specification request from user

[1701] Output: Request data to the server

[1702] Step 8:

[1703] The server provides the appropriate translation results.

[1704] The server retrieves the business report stored in the requested language from the database and provides it to the user. For example, if you request a business report in English, it will return the following data:

[1705] json

[1706] {

[1707] "content": "Today, I worked on the initial design of a new project."

[1708] }

[1709] Input: A request from the user

[1710] Output: Translation result data

[1711] Through these processing steps, the system enables multinational members to smoothly report on their work across language barriers and obtain the necessary information in the appropriate language.

[1712] (Application example 1)

[1713] 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."

[1714] In environments where multinational factory staff coexist, language barriers can make it difficult to smoothly share maintenance information and work reports. In particular, if machine maintenance reports are not properly communicated, this can lead to reduced work efficiency and safety risks. To solve this problem, there is a need to provide a reporting system that supports multiple languages.

[1715] 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.

[1716] In this invention, the server includes means for users to input work reports in their native language, means for terminals to transmit work report data to the server, means for the server to receive and store the transmitted work report data, translation engine means for the server to translate the work report data into other languages, means for the server to distribute the translated work report data in an appropriate language for each member, means for factory machines to automatically generate and transmit maintenance reports, and means for staff to check the reports on communication terminals used by staff. This enables multinational factory staff to quickly and accurately share maintenance information across language barriers.

[1717] A "multilingual business report system" is a system in which a user inputs a business report in their native language, and the report is translated into other languages ​​and distributed to each member in the appropriate language.

[1718] "Means for users to input business reports in their own native language" refers to means for providing an interface that allows users to input business details in their own native language.

[1719] The "means for the terminal to transmit business report data to the server" refers to a means that utilizes communication technology for transmitting data containing input business reports from the terminal to the server.

[1720] The "means for the server to receive and store the business report data transmitted" is a means for the server to receive the business report data transmitted from the terminal and store it in a database or the like.

[1721] The "translation engine means for the server to translate the business report data into another language" refers to the means by which the server converts the transmitted data into another language using a translation engine.

[1722] "Means for the server to deliver translated business report data in an appropriate language for each member" refers to means for providing translated data in the language selected by each member.

[1723] "Means for factory machines to automatically generate and send maintenance reports" refers to a means by which machines installed in a factory detect their own condition and the details of maintenance that is required, and automatically generate and send a report to a server.

[1724] "Means for staff to check reports on communication devices used by staff" refers to means for staff to check translated maintenance reports and work reports using communication devices such as smartphones and tablets.

[1725] This invention aims to realize a multilingual business report system, and is configured so that users, terminals, and servers can work together to share business reports across language barriers among members of various nationalities. This system is particularly notable in a factory environment in that it can automatically report machine maintenance.

[1726] This system first provides an interface for users to input business reports in their native language. For example, a text input field is displayed on the screen of a smartphone or tablet, allowing users to "input business details in their native language." The input business report data is then sent from the device to the server.

[1727] The server receives the data sent via the HTTP POST request, stores it in a database, and passes it to a translation engine to translate it into other languages, typically a generative AI model such as the Google Translate API.

[1728] The translated results are stored in a database by the server and are provided appropriately according to the language selected by the user. For example, "a report entered in Japanese can be translated into English, French, or Spanish, and members who select each language can view it on their smartphones or tablets."

[1729] Furthermore, factory machines automatically detect maintenance status and report the details. For example, "if a robot's operating temperature exceeds the normal range, a maintenance report is automatically generated and sent to the server." These automatic reports are also translated and distributed, allowing multinational staff to receive the reports in the appropriate language and respond quickly.

[1730] Examples include the following operations:

[1731] 1. The user types in Japanese, "The robot's operating temperature has exceeded the normal range. The cooling system needs to be inspected."

[1732] 2. This report data is sent from the terminal to the server, which receives it and stores it in a database.

[1733] 3. The server uses the Google Translate API to translate the message as "The robot's operating temperature has exceeded the normal range. The cooling system needs to be checked." and saves this data in the database.

[1734] 4. When staff who prefer English check the report on their smartphone, the translated English report will be displayed.

[1735] Example prompt sentence:

[1736] "The robot's operating temperature has exceeded the normal range. The cooling system needs to be inspected."

[1737] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1738] Step 1:

[1739] A user inputs a business report in his / her native language.

[1740] Input: The user types in their native language, "The robot's operating temperature has exceeded the normal range. The cooling system needs to be inspected."

[1741] How it works: The user enters a business report into the input field displayed on the screen of their smartphone or tablet.

[1742] Step 2:

[1743] The terminal transmits the business report data to the server.

[1744] Input: Business report data entered by the user and its language information.

[1745] Operation: The terminal sends business report data to the server using an HTTP POST request.

[1746] Output: Business report data is sent to the server.

[1747] Step 3:

[1748] The server receives and stores the transmitted business report data.

[1749] Input: Business report data (e.g., "The robot's operating temperature has exceeded the normal range. The cooling system needs to be inspected.") and its language information.

[1750] Operation: The server stores the received data in a database.

[1751] Output: Business report data stored in a database.

[1752] Step 4:

[1753] The server uses a translation engine means for translating the business report data into other languages.

[1754] Input: Business report data and its language information stored in the database.

[1755] How it works: The server uses the Google Translate API to translate business report data into other languages.

[1756] Output: Translated operational report data (e.g., "The robot's operating temperature has exceeded the normal range. The cooling system needs to be checked.").

[1757] Step 5:

[1758] The server stores the translated business report data in a database.

[1759] Input: Translated business report data.

[1760] How it works: The server stores the translated data in a database.

[1761] Output: Business report data stored in a database in multiple languages.

[1762] Step 6:

[1763] The server distributes the translated business report data to each member in the appropriate language.

[1764] Input: User's language preference and translation data stored in the database.

[1765] Operation: The server provides business report data in the appropriate language based on the user's language preference.

[1766] Output: Translated business report data displayed on the user's communication terminal.

[1767] Step 7:

[1768] Factory machines automatically generate and transmit maintenance reports.

[1769] Input: Machine status information (e.g. operating temperature, error codes, etc.).

[1770] How it works: Factory machines automatically detect their own condition, generate maintenance reports, and send them to a server.

[1771] Output: Auto-generated maintenance report data sent to the server.

[1772] Step 8:

[1773] Check the report on the communication device used by the staff.

[1774] Input: Staff communication terminals and translated business report data provided by the server.

[1775] Operation: A staff member accesses the server using a communication terminal and checks the translated business report data.

[1776] Output: Translated business report data displayed on the staff's communication terminal screen.

[1777] 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.

[1778] This invention further improves the multilingual business reporting system by incorporating an emotion engine that recognizes the user's emotions. This not only overcomes language barriers, but also understands the user's emotions expressed in the report content, making it possible to support richer communication.

[1779] System Overview

[1780] In this system, users input business reports in their native language, and the server receives, translates, analyzes emotions, and distributes the reports. By incorporating an emotion engine, the system can also analyze the emotional elements of the report content and reflect them in the translation and display results.

[1781] User operations

[1782] The user opens the work report input screen on the terminal and inputs the work report in their native language. For example, they might input "Today's work involved the initial design of a new project. I'm very happy" in Japanese. When the user presses the send button, the terminal sends the report data and the user's emotional information to the server. This includes the report content, the language used by the user, and the emotional information.

[1783] Device behavior

[1784] The terminal sends the business report data entered by the user to the server. Specifically, it sends the business report data and emotion information to the server using an HTTP POST request. This request includes the report content, user language information, and emotion information.

[1785] Server Operation

[1786] Receiving and storing report data

[1787] The server receives the business report data sent from the terminal. The received data is stored in a database. For example, suppose the server receives the following data:

[1788] json

[1789] {

[1790] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[1791] "language": "ja",

[1792] "emotion": "happy"

[1793] }

[1794] The server temporarily stores this data in a database.

[1795] Using a sentiment analysis engine

[1796] The server uses an emotion engine to analyze emotions from the report content. The emotion engine analyzes the report content and identifies and tags the emotions contained in the content.

[1797] Using a translation engine

[1798] The server passes the saved business report data to the translation engine and requests translation. The translation engine translates the business report into multiple languages ​​(e.g., English, French, and Spanish). For example, when a Japanese report is translated into English, it becomes, "Today, I worked on the initial design of a new project. I am very happy."

[1799] Saving translation results and emotional information

[1800] The server stores the translated business reports and emotional information in a database. The stored data includes the report content translated into each language and the associated emotional information.

[1801] Delivery of translation results and emotional information

[1802] The user opens the application on their device to view the report. The user selects the display language (e.g., English). The device sends an HTTP GET request to the server based on the selected language. The request includes a language parameter (e.g., / report?language=en).

[1803] The server searches the database for the report in the requested language and sends it to the device along with emotional information. The report received by the device is displayed as emotional information (joy), for example, along with the content, "Today, I worked on the initial design of a new project. I am very happy."

[1804] Specific examples

[1805] Here are some concrete examples:

[1806] 1. User report input

[1807] What you type: "Today I worked on the initial design for a new project. I'm very happy."

[1808] Language: Japanese

[1809] Emotion: Joy (User can specify emotion when typing or the system will recognize it automatically)

[1810] 2. Data transmission from the device

[1811] Data sent by the device:

[1812] json

[1813] {

[1814] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[1815] "language": "ja",

[1816] "emotion": "happy"

[1817] }

[1818] 3. Server Reception and Translation

[1819] The server received:

[1820] json

[1821] {

[1822] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[1823] "language": "ja",

[1824] "emotion": "happy"

[1825] }

[1826] Translation result (English): "Today, I worked on the initial design of a new project. I am very happy."

[1827] 4. Reporting and emotional information distribution

[1828] If the user selects English, the server provides the user with the following data:

[1829] json

[1830] {

[1831] "content": "Today, I worked on the initial design of a new project. I am very happy.",

[1832] "emotion": "happy"

[1833] }

[1834] In this way, through each step, the system realizes business reports that are multilingual and emotionally aware.

[1835] The processing flow will be explained below.

[1836] Step 1:

[1837] The user opens the work report input screen on the terminal. The user inputs the work report in their native language. For example, they might input "Today's work involved the initial design of a new project. I'm very happy" in Japanese.

[1838] Step 2:

[1839] The user completes the input and presses the send button. The terminal structures the input business report data. Specifically, it generates the following data structure:

[1840] json

[1841] {

[1842] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[1843] "language": "ja"

[1844] }

[1845] Step 3:

[1846] The device calls the emotion engine and analyzes the emotion from the user's report. The emotion engine identifies the emotion "happy" from "I'm very happy."

[1847] Step 4:

[1848] The device builds the data with added emotion information. Specifically, it generates the following data structure:

[1849] json

[1850] {

[1851] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[1852] "language": "ja",

[1853] "emotion": "happy"

[1854] }

[1855] Step 5:

[1856] The device sends this data to the server as an HTTP POST request, for example to the / report endpoint.

[1857] Step 6:

[1858] The server receives the POST request and extracts the report data, language information, and sentiment information from the request body.

[1859] Step 7:

[1860] The server stores the received data in a database, for example:

[1861] json

[1862] {

[1863] "content": "Today's work involved the initial design of a new project. I'm very happy about that.",

[1864] "language": "ja",

[1865] "emotion": "happy"

[1866] }

[1867] Step 8:

[1868] The server passes the saved data to a translation engine and requests a translation, which then translates the Japanese report into English.

[1869] Step 9:

[1870] Receive translation results from the translation engine. For example, receive the following translation results:

[1871] json

[1872] {

[1873] "translatedText": "Today, I worked on the initial design of a new project. I am very happy."

[1874] }

[1875] Step 10:

[1876] The server adds emotion information to the translation results and stores them in a database. Specifically, it stores them as follows:

[1877] json

[1878] {

[1879] "originalContent": "Today's work involved the initial design of a new project. I'm very happy about that.",

[1880] "originalLanguage": "ja",

[1881] "translatedContent": "Today, I worked on the initial design of a new project. I am very happy.",

[1882] "translatedLanguage": "en",

[1883] "emotion": "happy"

[1884] }

[1885] Step 11:

[1886] The user opens the application on their device to view the report. The user selects the display language (e.g., English).

[1887] Step 12:

[1888] The device sends an HTTP GET request to the server based on the selected language, including the language parameter (e.g., / report?language=en).

[1889] Step 13:

[1890] The server searches the database for the report content and emotion information in the requested language and returns it to the device as an HTTP response. Specifically, it returns the following data:

[1891] json

[1892] {

[1893] "translatedContent": "Today, I worked on the initial design of a new project. I am very happy.",

[1894] "emotion": "happy"

[1895] }

[1896] Step 14:

[1897] The device displays the received report and emotional information to the user. For example, "Today, I worked on the initial design of a new project. I am very happy." along with the emotional information (joy).

[1898] In this way, through each step, the system realizes business reports that are multilingual and emotionally aware.

[1899] Example 2

[1900] 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."

[1901] Conventional multilingual business reporting systems have the ability to translate report content into other languages, but lack the ability to understand the user's emotions and enrich communication. This has led to problems such as users' emotions not being reflected in the report, making accurate communication difficult.

[1902] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an emotion analysis engine means for analyzing business report data and identifying emotion information, a translation engine means for translating the business report data into another language, and a means for saving the translated business report data and emotion information. This enables richer communication by identifying the user's emotion contained in the business report and reflecting it in the translation result.

[1903] "User" refers to a person who uses the system to input and review business reports.

[1904] "Terminal" refers to a computer that a user uses to input business reports and send them to a server.

[1905] "Business report data" refers to information including the content of a business report entered by a user in their native language.

[1906] "Server" refers to a computer that has the functions of receiving, analyzing, translating, storing, and distributing business report data sent from a terminal.

[1907] "Sentiment Analysis Engine" refers to software for identifying and tagging user emotions from received business report data.

[1908] "Translation Engine" means software for translating business report data into multiple languages.

[1909] "Database" refers to a structured data storage system for the server to store business report data, translation results, and emotion information.

[1910] "Emotion information" refers to information indicating the user's emotions identified by the emotion analysis engine.

[1911] This invention incorporates an emotion analysis function into a multilingual business report system, enabling it to understand users' emotions and realize richer communication. In this system, users input business reports in their native language, and the server receives, translates, analyzes emotions, and distributes the reports.

[1912] The process begins with the user entering and sending a work report via a terminal. The user opens the work report input screen on the terminal and enters specific details in their native language, such as, "Today's work involved the initial design of a new project. I'm very happy about that." Once the input is complete, the user presses the send button. At this time, the work report content, the language used by the user, and emotional information are included.

[1913] The device sends business report data to the server using an HTTP POST request. This request includes the report content, language information, and emotion information. The data sent from the device is received by the server and stored in a database.

[1914] The server passes the saved business report data to a sentiment analysis engine to identify emotional information. For example, if the report contains the phrase "I'm very happy," the sentiment analysis engine tags it as "happy." The server then passes the business report content to a translation engine to translate it into other languages. For example, when a Japanese report content is translated into English, it becomes "Today, I worked on the initial design of a new project. I am very happy."

[1915] The translated business report data and emotion information are then saved back into the database. To check the report content, the user opens the application on their device and selects the display language. The device then sends an HTTP GET request to the server based on the selected language, and the server retrieves the report content and emotion information in the requested language from the database and sends it to the device. For example, if the user selects English, the content displayed will be "Today, I worked on the initial design of a new project. I am very happy.", with "happy" displayed as the emotion information.

[1916] As an example of a specific operation, suppose the user enters the following prompt:

[1917] "Today's work involved the initial design of a new project. I'm very happy."

[1918] The server analyzes this, identifies the emotional information as "happy," and translates it into "Today, I worked on the initial design of a new project. I am very happy." In this way, this system realizes business reports that are both multilingual and emotionally recognizable.

[1919] In practicing the invention, a general generative AI model can be used as the engine for sentiment analysis and translation, enabling advanced analysis of user input and supporting more accurate and richer communication.

[1920] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1921] Step 1: User enters business report

[1922] Specific operation: The user opens the work report input screen on the terminal, enters, for example, "Today's work involved the initial design of a new project. I'm very happy," and presses the send button.

[1923] Input: The business report content entered by the user, and the language information used (e.g., Japanese).

[1924] Output: Business report data (content, language information, emotion information) is generated on the device and ready to be sent.

[1925] Step 2: The device sends the data to the server

[1926] Specific operation: The device generates an HTTP POST request and sends it to the server in JSON format, including the work report data (e.g., "content": "Today's work involved the initial design of a new project. I'm very happy.", "language": "ja", "emotion": "happy").

[1927] Input: Business report data entered by the user.

[1928] Output: The business report data is sent from the terminal to the server as an HTTP POST request.

[1929] Step 3: The server receives and stores the data

[1930] Specific operation: The server receives the HTTP POST request sent from the device, extracts the report data, and stores it in a database.

[1931] Input: Work report data sent from the device (e.g., "content": "Today's work involved the initial design of a new project. I'm very happy.", "language": "ja", "emotion": "happy").

[1932] Output: Business report data stored in a database.

[1933] Step 4: The server performs sentiment analysis

[1934] Specific operation: The server passes business report data to the emotion analysis engine, which analyzes the report content and identifies the user's emotion. For example, it identifies "happy" from a phrase such as "I'm very happy."

[1935] Input: Business report data stored in a database.

[1936] Output: Sentiment information ("happy") from the sentiment analysis engine.

[1937] Step 5: The server does the translation

[1938] Specific operation: The server passes the business report data to the translation engine, which then translates the report content into another language. For example, when translating from Japanese to English, "Today's work involved the initial design of a new project" becomes "Today, I worked on the initial design of a new project."

[1939] Input: Business report data stored in a database, and sentiment information.

[1940] Output: Translated business report data (e.g., "Today, I worked on the initial design of a new project. I am very happy.") along with the original language and sentiment information ("happy").

[1941] Step 6: The server stores the translation results and emotion information

[1942] Specific operation: The server stores the translated business report data and emotion information in a database.

[1943] Input: Translated business report data and sentiment information.

[1944] Output: Translation results and sentiment information added to and saved in a database.

[1945] Step 7: Deliver data as requested by the user

[1946] Specific operation: The user specifies the language selected on the device and sends a request to the server so that business report data can be displayed. The server retrieves the corresponding language and emotion information from the database and delivers it to the device.

[1947] Input: HTTP GET request from user (e.g. " / report?language=en").

[1948] Output: Translation results and emotional information from the server to the device (e.g., "Today, I worked on the initial design of a new project. I am very happy.").

[1949] This series of processing steps enables users to effectively handle business reports that are multilingual and contain emotional information.

[1950] (Application example 2)

[1951] 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."

[1952] Conventional multilingual business reporting systems simply translate content without considering the user's emotions, making it difficult to respond based on emotions. Therefore, there is a need to understand user emotions and respond appropriately, especially in customer support and review management on online shopping sites. The present invention aims to solve this problem by utilizing user emotional information to provide more appropriate and prompt customer support.

[1953] The identification process by the identification 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 analyzing emotions in business report data using an emotion analysis engine, means for automatically selecting appropriate countermeasures based on the analyzed emotion information, and means for the terminal to display the translation result and the emotion information. This makes it possible to understand the user's emotions and provide appropriate countermeasures based on them.

[1954] "User" refers to a person who uses the system to input business reports.

[1955] "Native language" refers to the language with which the user is most familiar.

[1956] "Work report" refers to information that users record and report on their daily work, progress, emotions, etc.

[1957] "Terminal" refers to a device through which a user inputs business reports and communicates with a server.

[1958] "Server" refers to a computer system that has the functionality to receive, store, analyze, translate, and distribute business report data.

[1959] "Business report data" refers to data including the contents of a business report entered by a user.

[1960] "Translation Engine" means software or a system for translating business reporting data into other languages.

[1961] "Translated business report data" refers to business report data that has been converted into another language by a translation engine.

[1962] "Member" means a person or group that can receive business reporting data within the system.

[1963] "Emotion analysis engine" refers to software or a system for analyzing user emotions from business report data and extracting appropriate emotional information.

[1964] "Emotion information" refers to information that indicates the user's emotional state obtained by the emotion analysis engine.

[1965] "Appropriate countermeasures" refer to the countermeasures that the system automatically selects based on the user's emotional information.

[1966] "Translation result" refers to business report data translated into another language by a translation engine.

[1967] "Database" refers to a system or software for storing business report data, translation results, and emotional information.

[1968] "Display means" refers to a means for visually displaying the translation result and emotional information to the user.

[1969] The present invention aims to improve user response, particularly in customer support and review management, on an online shopping site that incorporates a multilingual emotion analysis system. The specific configuration and operation procedure of the system based on this embodiment are described below.

[1970] The system begins when a user inputs a business report using a device such as a smartphone, tablet, or PC. The data entered by the user in their native language is sent to a server by the device. The server first stores the received business report data in a database.

[1971] Next, the server uses an emotion analysis engine to analyze the user's emotions from the business report data. Based on this analysis, appropriate emotional information is extracted. Based on the emotional information, the server automatically selects an appropriate response. For example, if the user is determined to be "anxious," a prompt response is required.

[1972] The server then passes the business report data to a translation engine for translation into multiple languages. The translation results and emotion information are then stored in a database. The server then sends the translation results and emotion information to the terminal and displays them for the user to visually confirm.

[1973] The implementation of this system uses the following hardware and software:

[1974] Hardware: Smartphone, tablet, PC, server (cloud-based is also acceptable)

[1975] Software: Python, Flask (web server framework), Google Translate API (translation engine), nlp_emotion (emotion analysis library)

[1976] As a concrete example, consider the case where a user enters and submits the following in Japanese: "My item hasn't arrived. I'm very worried." This data is processed as follows:

[1977] 1. "Anxiety" is extracted as the result of analysis by the emotion analysis engine.

[1978] 2. The translation engine translates this into English as, "The product has not arrived. I am very anxious."

[1979] 3. The server stores this data and displays the translation results and emotion information to the user.

[1980] An example prompt for a generative AI model might look like this:

[1981] "Analyze the following sentence using an emotion recognition engine and output its emotion. Sentence: "My item hasn't arrived. I'm very worried.""

[1982] In this way, the present invention is a system that combines multilingual support and emotion analysis to understand user emotions and provide appropriate and prompt responses, particularly in customer support and review management on online shopping sites.

[1983] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1984] Step 1:

[1985] The user enters a business report

[1986] Users use devices such as smartphones, tablets, and PCs to enter business reports and reviews in their native language. The entered data includes the content of the report or review. For example: "The product hasn't arrived. I'm very worried."

[1987] Step 2:

[1988] The device sends the data to the server

[1989] The terminal transmits the business report data input by the user to the server via an HTTP POST request, and the data includes the report content and language information.

[1990] Step 3:

[1991] The server receives and stores the data

[1992] The server receives the business report data sent from the terminal. The received data is stored in a database. For example, the following data is stored:

[1993] json

[1994] {

[1995] "content": "My item hasn't arrived. I'm very worried.",

[1996] "language": "ja"

[1997] }

[1998] Step 4:

[1999] The server analyzes the sentiment using a sentiment analysis engine.

[2000] The server passes the saved business report data to the emotion analysis engine, which analyzes the emotions. The analyzed emotion information (e.g., "anxiety") is stored in a database.

[2001] Step 5:

[2002] The server automatically selects the appropriate countermeasure.

[2003] Based on the results of emotion analysis, the server automatically selects the appropriate response. For example, if the emotion analyzed is "anxiety," a prompt response is required.

[2004] Step 6:

[2005] The server translates the business report data.

[2006] The server passes the saved business report data to a translation engine, which translates it into multiple languages ​​(e.g., English). The translated content is saved back into the database. For example, "The product has not arrived. I am very anxious." is translated into "The product has not arrived. I am very anxious."

[2007] Step 7:

[2008] The server sends the translation results and emotion information

[2009] When a user checks the contents of a business report, the device requests the translation result and emotion information from the server based on the selected language (e.g., English). The server retrieves the necessary data from the database and sends it to the device.

[2010] Step 8:

[2011] The device displays the translation results and emotional information.

[2012] The device displays the translation result and emotional information it receives on the screen. For example, the user can see the translation result "The product has not arrived. I am very anxious." along with the emotional information "anxiety."

[2013] This process allows the system to understand the user's emotions and quickly respond appropriately based on those emotions.

[2014] 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.

[2015] 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.

[2016] 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.

[2017] 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.

[2018] 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.

[2019] 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.

[2020] 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).

[2021] 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.

[2022] 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."

[2023] 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.

[2024] 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).

[2025] 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.

[2026] 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.

[2027] 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.

[2028] 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.

[2029] 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.

[2030] 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.

[2031] 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.

[2032] 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.

[2033] 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.

[2034] 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.

[2035] The following is further disclosed regarding the above embodiment.

[2036] (Claim 1)

[2037] A multilingual business reporting system,

[2038] a means for users to input business reports in their native language;

[2039] A means for the terminal to transmit business report data to a server;

[2040] A server receives and stores the transmitted business report data;

[2041] a translation engine means for the server to translate the business report data into other languages;

[2042] A means for the server to distribute the translated business report data in the appropriate language for each member;

[2043] A system including:

[2044] (Claim 2)

[2045] 10. The system of claim 1, further comprising: means for obtaining business report data based on a language selected by a user.

[2046] (Claim 3)

[2047] 10. The system of claim 1, wherein the server comprises database means for storing the translation results.

[2048] "Example 1"

[2049] (Claim 1)

[2050] a means for users to input business reports in their native language;

[2051] A means for the terminal to transmit business report data to a server;

[2052] A server receives and stores the transmitted business report data;

[2053] a translation engine means for the server to translate the business report data into other languages;

[2054] A means for the server to distribute the translated business report data in the appropriate language for each member;

[2055] means for transmitting data including user language information from the terminal to the server;

[2056] A means for the server to retrieve and provide appropriate data from a database based on a user request;

[2057] A means for the terminal to use an HTTP POST request to send data to a server;

[2058] A means for the server to store the translation results in a database;

[2059] A system including:

[2060] (Claim 2)

[2061] 10. The system of claim 1, further comprising: means for obtaining business report data based on a language selected by a user.

[2062] (Claim 3)

[2063] 10. The system of claim 1, wherein the translation engine uses a generative model.

[2064] "Application Example 1"

[2065] (Claim 1)

[2066] a means for users to input business reports in their native language;

[2067] A means for the terminal to transmit business report data to a server;

[2068] A server receives and stores the transmitted business report data;

[2069] a translation engine means for the server to translate the business report data into other languages;

[2070] A means for the server to distribute the translated business report data in the appropriate language for each member;

[2071] a means for the factory machines to automatically generate and transmit maintenance reports;

[2072] A means for checking reports on communication devices used by staff;

[2073] A system including:

[2074] (Claim 2)

[2075] 10. The system of claim 1, further comprising: means for obtaining business report data based on a language selected by a user.

[2076] (Claim 3)

[2077] 10. The system of claim 1, wherein the server comprises database means for storing the translation results.

[2078] "Example 2: Combining Emotion Engines"

[2079] (Claim 1)

[2080] a means for users to input business reports in their native language;

[2081] A means for the terminal to transmit business report data to a server;

[2082] A server receives and stores the transmitted business report data;

[2083] an emotion analysis engine means for the server to analyze the business report data and identify emotion information;

[2084] a translation engine means for the server to translate the business report data into other languages;

[2085] A server stores the translated business report data and emotion information;

[2086] A means for the server to deliver the translated business report data and emotion information in an appropriate language for each member;

[2087] A system including:

[2088] (Claim 2)

[2089] 10. The system of claim 1, further comprising means for obtaining business report data based on user-selected language and sentiment information.

[2090] (Claim 3)

[2091] 10. The system of claim 1, wherein the server comprises database means for storing the translation results and the emotion information.

[2092] "Application example 2 when combining emotion engines"

[2093] (Claim 1)

[2094] a means for users to input business reports in their native language;

[2095] A means for the terminal to transmit business report data to a server;

[2096] A server receives and stores the transmitted business report data;

[2097] a translation engine means for the server to translate the business report data into other languages;

[2098] A means for the server to distribute the translated business report data in the appropriate language for each member;

[2099] a means for analyzing sentiment of business report data using a sentiment analysis engine;

[2100] A means to automatically select appropriate countermeasures based on the analyzed emotional information,

[2101] a means for displaying the translation result and emotion information on the terminal;

[2102] A system including:

[2103] (Claim 2)

[2104] 10. The system of claim 1, further comprising: means for obtaining business report data based on a language selected by a user.

[2105] (Claim 3)

[2106] 10. The system of claim 1, wherein the server comprises database means for storing the translation results and the emotion information. [Explanation of symbols]

[2107] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A multilingual business reporting system, a means for users to input business reports in their native language; A means for the terminal to transmit business report data to a server; A server receives and stores the transmitted business report data; a translation engine means for the server to translate the business report data into other languages; A means for the server to distribute the translated business report data in the appropriate language for each member; A system including:

2. The system of claim 1 , further comprising: means for obtaining business report data based on a language selected by a user.

3. 2. The system of claim 1, wherein the server comprises database means for storing the translation results.

Images