system

Abstract

The system according to this embodiment aims to enable smooth international business communication even for people who are not fluent in English. [Solution] The system according to the embodiment comprises a reception unit, a summarization unit, a provision unit, an input unit, and a generation unit. The reception unit receives business emails in English. The summarization unit analyzes the business emails in English received by the reception unit and summarizes them in Japanese. The provision unit provides the content summarized by the summarization unit. The input unit inputs the reply content in Japanese based on the summary content provided by the provision unit. The generation unit analyzes the Japanese content input by the input unit and generates a reply email in business English.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for people who are not familiar with English to understand and reply to the content of emails when conducting international business communication.

[0005] The system according to the embodiment aims to enable people who are not familiar with English to smoothly conduct international business communication.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a summarization unit, a provision unit, an input unit, and a generation unit. The reception unit receives business emails in English. The summarization unit analyzes the English business emails received by the reception unit and summarizes them in Japanese. The provision unit provides the content summarized by the summarization unit. The input unit inputs the reply content in Japanese based on the summary content provided by the provision unit. The generation unit analyzes the Japanese content input by the input unit and generates a reply email in business English. [Effects of the Invention]

[0007] The system according to this embodiment enables even those unfamiliar with English to engage in smooth international business communication. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The business email support system according to an embodiment of the present invention is a system that summarizes business emails received in English concisely in Japanese and generates a reply in polite business English based on the Japanese input. The business email support system works by having the user paste a business email received in English into the tool, which then uses AI to analyze its content and summarize it concisely in Japanese. This summary helps the user quickly understand the email's content. Next, the user inputs the content of their reply in Japanese using bullet points. The AI ​​analyzes the input Japanese content and generates a reply email in polite business English. This tool allows even those unfamiliar with English to smoothly engage in international business communication. First, the user pastes a business email received in English into the tool. This simply requires copying and pasting the entire email content. Examples include customer inquiry emails and business partner proposal emails. This information is input into the AI. Next, the AI ​​analyzes the input email content and summarizes it concisely in Japanese. The AI ​​extracts the key points of the email and summarizes them concisely in Japanese. For example, in the case of a customer inquiry email, the AI ​​concisely summarizes the inquiry and requests. This summary allows users to quickly understand the content of the email. Next, the user enters the content they want to reply to in Japanese in bullet points. For example, they might enter bullet points such as "Please tell me more about the product" or "Please confirm the delivery date." This information is then entered back into the AI. The AI ​​analyzes the entered Japanese content and generates a reply email in polite business English. The AI ​​considers the grammar and expressions of business emails to generate appropriate English sentences. For example, the Japanese sentence "Please tell me more about the product" is converted to the English sentence "Could you please provide more details about the product?". This reply email allows users to communicate smoothly in business even if they are not fluent in English. This tool enables even those unfamiliar with English to communicate smoothly in international business. Users can easily understand English emails and reply in appropriate English.This improves business efficiency and prevents missed international business opportunities. The business email support system can concisely summarize English business emails in Japanese and generate polite business English replies to Japanese input.

[0029] The business email support system according to this embodiment comprises a reception unit, a summarization unit, a provision unit, an input unit, and a generation unit. The reception unit receives business emails in English. For example, the user can simply copy and paste the entire content of a business email they have received in English into the tool. The summarization unit analyzes the English business email received by the reception unit and summarizes it in Japanese. For example, the summarization unit uses AI to extract the important points of the email and summarizes them concisely in Japanese. The provision unit provides the content summarized by the summarization unit. For example, the provision unit displays the summarized content to the user. The input unit inputs the reply content in Japanese based on the summary content provided by the provision unit. For example, the input unit inputs the content the user wants to reply in bullet points in Japanese. The generation unit analyzes the Japanese content entered by the input unit and generates a reply email in business English. For example, the generation unit uses AI to analyze the Japanese content entered, considers the grammar and expressions of the business email, and generates an appropriate English sentence. This allows the business email support system to concisely summarize English business emails in Japanese and generate polite business English replies to emails entered in Japanese.

[0030] The reception department receives business emails in English. For example, users can simply copy and paste the entire content of an email they receive in English into the tool. Specifically, users copy an email they receive and paste it into a dedicated web interface or desktop application, and the reception department automatically imports the content. The reception department can accurately receive all information, including the email format and attachments. Furthermore, the reception department analyzes email metadata (sender, recipient, date and time, etc.) and uses this information for subsequent processing. For example, based on the sender's information, it can identify past correspondence and related projects. The reception department temporarily stores received emails, making them accessible to the summarization and generation departments. This allows users to easily import English business emails into the system, ensuring smooth subsequent processing.

[0031] The summarization unit analyzes English business emails received by the reception unit and summarizes them in Japanese. For example, the summarization unit uses AI to extract key points from emails and summarize them concisely in Japanese. Specifically, it uses natural language processing technology to analyze the content of emails and extract important keywords and phrases. The AI ​​has an algorithm to understand the context and tone of emails and select important information. For example, it identifies important elements in business emails (meeting dates and times, action items, important decisions, etc.) and expresses them concisely in Japanese. Based on the extracted information, the summarization unit creates a summary in a format that is easy for users to understand. The summary consists of bullet points and short sentences, allowing users to quickly grasp the content. Furthermore, the summarization unit utilizes past email data and user feedback to improve the accuracy of the summaries. As a result, the summarization unit can quickly and accurately summarize English business emails in Japanese and provide them to users.

[0032] The service provider provides the content summarized by the summarization service provider. The service provider displays the summarized content to the user, for example. Specifically, it displays the summarized content on the user's device in real time so that the user can check it immediately. The service provider displays the summarized content visually and clearly through a web interface or mobile application. For example, the summarized content is displayed in a card format, with each card containing concise information on the key points. The service provider can also send the summarized content to the user via email or notification. This allows the user to check the summarized content anytime, anywhere. Furthermore, the service provider collects user feedback on the summarized content and provides this feedback to the summarization service provider, continuously improving the accuracy of the summaries. This enables the service provider to provide users with summarized content quickly and accurately, and streamline the processing of business emails.

[0033] The input unit inputs the reply content in Japanese based on the summary provided by the supply unit. For example, the input unit allows the user to input the content they wish to reply in Japanese using bullet points. Specifically, it provides an interface for the user to review the summary content and input their reply in Japanese. The input unit has an intuitive user interface to allow users to input easily. For example, it uses text boxes, checkboxes, and dropdown menus to allow users to quickly input their reply content. Furthermore, the input unit automatically saves the content entered by the user, allowing for later editing and modification. The input unit also has a function to analyze the content entered by the user and check the structure and grammar of the reply content. This allows the input unit to enable users to input reply content easily and efficiently, and for the subsequent generation unit to accurately generate the reply email.

[0034] The generation unit analyzes the Japanese content entered by the input unit and generates a reply email in business English. For example, the generation unit uses AI to analyze the Japanese content entered, taking into account the grammar and expressions of business emails, and generates an appropriate English sentence. Specifically, it uses natural language processing technology to analyze the Japanese content and generate corresponding English expressions. The AI ​​understands the grammar and expressions specific to business emails and creates a reply email with an appropriate tone and style. For example, it generates a professional business email using honorifics and polite expressions. The generation unit displays the generated English reply email to the user, allowing them to review and correct it. Furthermore, the generation unit continuously improves its generation algorithm by utilizing past email data and user feedback. As a result, the generation unit can convert the Japanese content entered by the user into an accurate and polite business English reply email, making business communication more efficient.

[0035] The summarization function can extract the key points of an English business email and summarize them concisely in Japanese. For example, the summarization function can use AI to extract the key points of an email and summarize them concisely in Japanese. The summarization function can extract key points based on frequently occurring keywords or the importance of the context. It can also analyze the content of an email, extract important information, and summarize it. This allows users to quickly understand the content of an email by extracting the key points of an English business email and summarizing them concisely in Japanese. Some or all of the above processing in the summarization function may be performed using AI, for example, or without AI. For example, the summarization function can perform summarization using an AI model that takes an English business email as input and outputs a Japanese summary.

[0036] The generation unit can generate appropriate English sentences by considering the grammar and expressions of business emails. For example, the generation unit can use AI to generate appropriate English sentences by considering the grammar and expressions of business emails. The generation unit can generate English sentences by considering, for example, tense, honorifics, and formal expressions. Furthermore, the generation unit can generate English sentences by considering, for example, the grammatical accuracy and formal expressions of business emails. As a result, by considering the grammar and expressions of business emails and generating appropriate English sentences, users can create reply emails in polite business English. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can generate reply emails using an AI model that takes Japanese reply content as input and outputs a reply email in business English.

[0037] The service provider can provide the user with summarized content. The service provider can, for example, display the summarized content to the user. The service provider can also, for example, notify the user of the summarized content. Furthermore, the service provider can, for example, send the summarized content to the user via email. By providing the user with summarized content, the user can quickly understand the content of the email. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide the summarized content using an AI model that takes the summarized content as input and outputs data for display to the user.

[0038] The input section allows the user to input the content they wish to reply in Japanese as a bulleted list. For example, the input section can determine the format of the bulleted list based on the number of items and the length of each item. The input section can also analyze the content entered by the user and convert it into a bulleted list format. This allows the AI ​​to generate a reply email in appropriate business English by having the user input the content they wish to reply in Japanese as a bulleted list. Some or all of the above processing in the input section may be performed using AI, or not. For example, the input section can process the input content using an AI model that takes the Japanese content entered by the user as input and converts it into a bulleted list format.

[0039] The generation unit can analyze the input Japanese content and generate a reply email in polite business English. For example, the generation unit can use AI to analyze the input Japanese content and generate a reply email in polite business English. The generation unit can generate English sentences considering, for example, the use of honorifics and formal expressions. Furthermore, the generation unit can generate English sentences considering, for example, the grammatical accuracy and formal expressions of business emails. This allows users to smoothly engage in international business communication by analyzing the input Japanese content and generating a reply email in polite business English. Some or all of the above processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can generate a reply email using an AI model that takes Japanese reply content as input and outputs a reply email in business English.

[0040] The reception system can analyze a user's past email reception history and propose the optimal reception method. For example, the reception system can automatically recognize the format of emails the user has frequently received in the past and propose the optimal reception method. The reception system can also prioritize suggesting reception methods the user has used in the past (such as copy and paste or file upload). Furthermore, the reception system can predict and propose reception methods to be used during specific time periods based on the user's past reception history. In this way, the reception system can propose the optimal reception method by analyzing the user's past email reception history. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can propose a reception method using an AI model that takes past email reception history as input and outputs the optimal reception method.

[0041] The reception system can adjust the reception method when receiving business emails in English, taking into account the user's current situation. For example, if the user is on a business trip, the reception system can provide a reception method optimized for mobile devices. It can also provide a reception method optimized for desktop environments if the user is in the office. Furthermore, if the user is in a meeting, the reception system can provide a method for receiving emails using voice input or simple taps. This allows the user to receive emails in the most optimal way by adjusting the reception method based on their current situation. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can adjust the reception method using an AI model that takes user's current situation data as input and outputs the optimal reception method.

[0042] The reception system can suggest the optimal reception method when receiving business emails in English, by referring to the user's past email history. For example, the reception system can automatically recognize the format of emails the user has frequently received in the past and suggest the optimal reception method. It can also predict the format of emails the user will receive on specific days or times and suggest the optimal reception method. Furthermore, the reception system can analyze the user's past email history and suggest the optimal reception method. This allows the system to suggest the optimal reception method by referring to the user's past email history. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can suggest a reception method using an AI model that takes past email history as input and outputs the optimal reception method.

[0043] The reception desk can suggest the optimal reception method when receiving business emails in English, by referring to the user's calendar information. For example, the reception desk can refer to appointments registered in the user's calendar and suggest the optimal reception method. The reception desk can also suggest reception methods for emails related to specific events based on the user's calendar information. Furthermore, the reception desk can suggest the optimal reception method tailored to the appointment based on the user's calendar information. In this way, the reception desk can suggest the optimal reception method by referring to the user's calendar information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can suggest a reception method using an AI model that takes calendar information as input and outputs the optimal reception method.

[0044] The summarization unit can adjust the level of detail in the summary based on the importance of the English business email. For example, it can provide a detailed summary for high-importance emails, and a concise summary for less important emails. Furthermore, it can adjust the length and level of detail of the summary according to its importance. This allows for a detailed understanding of important email content by adjusting the level of detail in the summary based on the importance of the English business email. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not. For example, the summarization unit can use an AI model that takes email importance data as input and adjusts the level of detail in the summary.

[0045] The summarization unit can apply different summarization algorithms depending on the category of the English business email during the summarization process. For example, in the case of an inquiry email from a customer, the summarization unit will focus on the inquiry content. Similarly, in the case of a proposal email from a business partner, the summarization unit can focus on the proposal content. Furthermore, in the case of an internal communication email, the summarization unit can focus on the important information. This allows for appropriate summarization of email content by applying different summarization algorithms depending on the category of the English business email. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can perform summarization using an AI model that takes email category data as input and applies different summarization algorithms.

[0046] The summarization unit can prioritize summarizing based on when the English business emails were sent. For example, it might prioritize summarizing recently sent emails. It can also prioritize summarizing emails sent at important times. Furthermore, it can prioritize summarizing according to the user's schedule. This allows important emails to be prioritized by prioritizing summarization based on when the English business emails were sent. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not. For example, the summarization unit can take email sending time data as input and perform summarization using an AI model that determines the priority of summarization.

[0047] The summarization unit can adjust the order of summaries based on the relevance of the English business emails during the summarization process. For example, the summarization unit can prioritize summarizing highly relevant emails. It can also postpone summarizing less relevant emails. Furthermore, the summarization unit can adjust the order of summaries according to their relevance. This allows for prioritizing the summarization of highly relevant emails by adjusting the order of summaries based on the relevance of the English business emails. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can perform summaries using an AI model that takes email relevance data as input and adjusts the order of summaries.

[0048] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods that the user has previously preferred. The service provider can also suggest the optimal display method based on the user's past operation history. Furthermore, the service provider can analyze the user's past operation history and provide the most efficient display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can select a display method using an AI model that takes past operation history data as input and outputs the optimal display method.

[0049] The service provider can set display priorities based on the importance of the summary content at the time of delivery. For example, the service provider can prioritize the display of summaries with high importance. It can also postpone the display of summaries with low importance. Furthermore, the service provider can set display priorities according to importance. This allows important information to be displayed preferentially by setting display priorities based on the importance of the summary content. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use an AI model that takes summary content importance data as input and sets display priorities to perform the display.

[0050] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. In addition, if the user is using a desktop, the service provider can provide a display method that includes detailed information. This allows the service provider to provide the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can select a display method using an AI model that takes device information as input and outputs the optimal display method.

[0051] The service provider can automatically display additional information related to the summary content at the time of delivery. For example, the service provider can automatically display past emails related to the summary content. It can also automatically display documents and files related to the summary content. Furthermore, the service provider can automatically display web links and reference materials related to the summary content. This allows users to quickly obtain the information they need by automatically displaying additional information related to the summary content. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can display additional information using an AI model that takes the summary content as input and outputs related additional information.

[0052] The input unit can suggest the optimal input method by referring to the user's past input history during input. For example, the input unit can automatically display as suggestions the user has frequently entered reply content in the past. The input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest reply content to be used during specific time periods based on the user's past input history. In this way, the optimal input method can be suggested by referring to the user's past input history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can suggest an input method using an AI model that takes past input history data as input and outputs the optimal input method.

[0053] The input unit can automatically complete the input content based on the user's current situation. For example, if the user is on a business trip, the input unit can automatically complete the reply content related to the destination. Furthermore, if the user is in a meeting, the input unit can automatically complete the reply content related to the meeting's content. Additionally, if the user is in the office, the input unit can automatically complete the reply content based on the office situation. This allows the user to quickly input replies by automatically completing the input content based on their current situation. Some or all of the above processing in the input unit may be performed using AI, or without AI. For example, the input unit can use current situation data as input and complete the input using an AI model that automatically completes the input content.

[0054] The input unit can suggest the optimal input method when the user is inputting data, taking into account the user's geographical location. For example, if the user is overseas, the input unit can suggest an input method that suits the local conditions. Furthermore, if the user is at home, the input unit can suggest an input method that suits the home environment. Additionally, if the user is on the move, the input unit can suggest an input method optimized for mobile devices. This allows the input unit to suggest the optimal input method by considering the user's geographical location. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can suggest an input method using an AI model that takes geographical location information as input and outputs the optimal input method.

[0055] The input unit can analyze the user's social media activity during input and suggest relevant input content. For example, the input unit can suggest relevant replies based on what the user has recently mentioned on social media. It can also analyze the user's social media activity history and suggest the most appropriate replies. Furthermore, the input unit can suggest replies related to topics the user follows on social media. In this way, relevant input content can be suggested by analyzing the user's social media activity. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can suggest input content using an AI model that takes social media activity data as input and outputs relevant input content.

[0056] The generation unit can adjust the level of detail in reply emails based on the importance of the email when generating them. For example, the generation unit can generate a detailed reply email for high-importance emails. It can also generate a concise reply email for low-importance emails. Furthermore, the generation unit can adjust the length and level of detail of the reply email according to its importance. This allows for appropriate replies to important emails by adjusting the level of detail of the reply email based on its importance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take email importance data as input and generate reply emails using an AI model that adjusts the level of detail of the reply email.

[0057] The generation unit can apply different generation algorithms depending on the email category when generating reply emails. For example, in the case of an inquiry email from a customer, the generation unit can generate a polite and detailed reply email. It can also generate a reply email tailored to the specific proposal content in the case of a proposal email from a business partner. Furthermore, it can generate a concise and to-the-point reply email in the case of an internal communication email. Thus, by applying different generation algorithms depending on the email category, appropriate reply emails can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate reply emails using an AI model that takes email category data as input and applies different generation algorithms.

[0058] The generation unit can determine the priority of reply emails based on when the emails were sent when generating reply emails. For example, the generation unit may prioritize replying to recently sent emails. It can also prioritize replying to emails sent at important times. Furthermore, the generation unit can determine the priority of reply emails according to the user's schedule. This allows important emails to be replied to preferentially by determining the priority of reply emails based on when they were sent. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take email sending time data as input and generate reply emails using an AI model that determines the priority of reply emails.

[0059] The generation unit can adjust the order of reply emails based on their relevance when generating reply emails. For example, the generation unit can prioritize replying to highly relevant emails. It can also postpone replying to less relevant emails. Furthermore, the generation unit can adjust the order of reply emails according to their relevance. This allows for prioritizing replies to highly relevant emails by adjusting the order of reply emails based on their relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can use an AI model that takes email relevance data as input and adjusts the order of reply emails to generate reply emails.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception system can analyze a user's past email reception history and propose the optimal reception method. For example, the reception system can automatically recognize the format of emails the user has frequently received in the past and propose the optimal reception method. The reception system can also prioritize suggesting reception methods the user has used in the past (such as copy and paste or file upload). Furthermore, the reception system can predict and propose reception methods to be used during specific time periods based on the user's past reception history. In this way, the reception system can propose the optimal reception method by analyzing the user's past email reception history. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can propose a reception method using an AI model that takes past email reception history as input and outputs the optimal reception method.

[0062] The summarization unit can adjust the level of detail in the summary based on the importance of the English business email. For example, it can provide a detailed summary for high-importance emails, and a concise summary for less important emails. Furthermore, it can adjust the length and level of detail of the summary according to its importance. This allows for a detailed understanding of important email content by adjusting the level of detail in the summary based on the importance of the English business email. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not. For example, the summarization unit can use an AI model that takes email importance data as input and adjusts the level of detail in the summary.

[0063] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods that the user has previously preferred. The service provider can also suggest the optimal display method based on the user's past operation history. Furthermore, the service provider can analyze the user's past operation history and provide the most efficient display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can select a display method using an AI model that takes past operation history data as input and outputs the optimal display method.

[0064] The input unit can suggest the optimal input method by referring to the user's past input history during input. For example, the input unit can automatically display as suggestions the user has frequently entered reply content in the past. The input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest reply content to be used during specific time periods based on the user's past input history. In this way, the optimal input method can be suggested by referring to the user's past input history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can suggest an input method using an AI model that takes past input history data as input and outputs the optimal input method.

[0065] The generation unit can adjust the level of detail in reply emails based on the importance of the email when generating them. For example, the generation unit can generate a detailed reply email for high-importance emails. It can also generate a concise reply email for low-importance emails. Furthermore, the generation unit can adjust the length and level of detail of the reply email according to its importance. This allows for appropriate replies to important emails by adjusting the level of detail of the reply email based on its importance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take email importance data as input and generate reply emails using an AI model that adjusts the level of detail of the reply email.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk accepts business emails in English. For example, users can simply copy and paste the entire content of a business email they received in English into the tool. Step 2: The summarization department analyzes the English business emails received by the reception department and summarizes them in Japanese. For example, AI extracts the key points of the email and summarizes them concisely in Japanese. Step 3: The providing section provides the content summarized by the summarizing section. For example, it displays the summarized content to the user. Step 4: The input section will enter the reply content in Japanese based on the summary content provided by the provider section. For example, the user will enter the content they want to reply in Japanese using bullet points. Step 5: The generation unit analyzes the Japanese content entered by the input unit and generates a reply email in business English. For example, the AI ​​analyzes the entered Japanese content, takes into account the grammar and expressions of a business email, and generates an appropriate English sentence.

[0068] (Example of form 2) The business email support system according to an embodiment of the present invention is a system that summarizes business emails received in English concisely in Japanese and generates a reply in polite business English based on the Japanese input. The business email support system works by having the user paste a business email received in English into the tool, which then uses AI to analyze its content and summarize it concisely in Japanese. This summary helps the user quickly understand the email's content. Next, the user inputs the content of their reply in Japanese using bullet points. The AI ​​analyzes the input Japanese content and generates a reply email in polite business English. This tool allows even those unfamiliar with English to smoothly engage in international business communication. First, the user pastes a business email received in English into the tool. This simply requires copying and pasting the entire email content. Examples include customer inquiry emails and business partner proposal emails. This information is input into the AI. Next, the AI ​​analyzes the input email content and summarizes it concisely in Japanese. The AI ​​extracts the key points of the email and summarizes them concisely in Japanese. For example, in the case of a customer inquiry email, the AI ​​concisely summarizes the inquiry and requests. This summary allows users to quickly understand the content of the email. Next, the user enters the content they want to reply to in Japanese in bullet points. For example, they might enter bullet points such as "Please tell me more about the product" or "Please confirm the delivery date." This information is then entered back into the AI. The AI ​​analyzes the entered Japanese content and generates a reply email in polite business English. The AI ​​considers the grammar and expressions of business emails to generate appropriate English sentences. For example, the Japanese sentence "Please tell me more about the product" is converted to the English sentence "Could you please provide more details about the product?". This reply email allows users to communicate smoothly in business even if they are not fluent in English. This tool enables even those unfamiliar with English to communicate smoothly in international business. Users can easily understand English emails and reply in appropriate English.This improves business efficiency and prevents missed international business opportunities. The business email support system can concisely summarize English business emails in Japanese and generate polite business English replies to Japanese input.

[0069] The business email support system according to this embodiment comprises a reception unit, a summarization unit, a provision unit, an input unit, and a generation unit. The reception unit receives business emails in English. For example, the user can simply copy and paste the entire content of a business email they have received in English into the tool. The summarization unit analyzes the English business email received by the reception unit and summarizes it in Japanese. For example, the summarization unit uses AI to extract the important points of the email and summarizes them concisely in Japanese. The provision unit provides the content summarized by the summarization unit. For example, the provision unit displays the summarized content to the user. The input unit inputs the reply content in Japanese based on the summary content provided by the provision unit. For example, the input unit inputs the content the user wants to reply in bullet points in Japanese. The generation unit analyzes the Japanese content entered by the input unit and generates a reply email in business English. For example, the generation unit uses AI to analyze the Japanese content entered, considers the grammar and expressions of the business email, and generates an appropriate English sentence. This allows the business email support system to concisely summarize English business emails in Japanese and generate polite business English replies to emails entered in Japanese.

[0070] The reception department receives business emails in English. For example, users can simply copy and paste the entire content of an email they receive in English into the tool. Specifically, users copy an email they receive and paste it into a dedicated web interface or desktop application, and the reception department automatically imports the content. The reception department can accurately receive all information, including the email format and attachments. Furthermore, the reception department analyzes email metadata (sender, recipient, date and time, etc.) and uses this information for subsequent processing. For example, based on the sender's information, it can identify past correspondence and related projects. The reception department temporarily stores received emails, making them accessible to the summarization and generation departments. This allows users to easily import English business emails into the system, ensuring smooth subsequent processing.

[0071] The summarization unit analyzes English business emails received by the reception unit and summarizes them in Japanese. For example, the summarization unit uses AI to extract key points from emails and summarize them concisely in Japanese. Specifically, it uses natural language processing technology to analyze the content of emails and extract important keywords and phrases. The AI ​​has an algorithm to understand the context and tone of emails and select important information. For example, it identifies important elements in business emails (meeting dates and times, action items, important decisions, etc.) and expresses them concisely in Japanese. Based on the extracted information, the summarization unit creates a summary in a format that is easy for users to understand. The summary consists of bullet points and short sentences, allowing users to quickly grasp the content. Furthermore, the summarization unit utilizes past email data and user feedback to improve the accuracy of the summaries. As a result, the summarization unit can quickly and accurately summarize English business emails in Japanese and provide them to users.

[0072] The service provider provides the content summarized by the summarization service provider. The service provider displays the summarized content to the user, for example. Specifically, it displays the summarized content on the user's device in real time so that the user can check it immediately. The service provider displays the summarized content visually and clearly through a web interface or mobile application. For example, the summarized content is displayed in a card format, with each card containing concise information on the key points. The service provider can also send the summarized content to the user via email or notification. This allows the user to check the summarized content anytime, anywhere. Furthermore, the service provider collects user feedback on the summarized content and provides this feedback to the summarization service provider, continuously improving the accuracy of the summaries. This enables the service provider to provide users with summarized content quickly and accurately, and streamline the processing of business emails.

[0073] The input unit inputs the reply content in Japanese based on the summary provided by the supply unit. For example, the input unit allows the user to input the content they wish to reply in Japanese using bullet points. Specifically, it provides an interface for the user to review the summary content and input their reply in Japanese. The input unit has an intuitive user interface to allow users to input easily. For example, it uses text boxes, checkboxes, and dropdown menus to allow users to quickly input their reply content. Furthermore, the input unit automatically saves the content entered by the user, allowing for later editing and modification. The input unit also has a function to analyze the content entered by the user and check the structure and grammar of the reply content. This allows the input unit to enable users to input reply content easily and efficiently, and for the subsequent generation unit to accurately generate the reply email.

[0074] The generation unit analyzes the Japanese content entered by the input unit and generates a reply email in business English. For example, the generation unit uses AI to analyze the Japanese content entered, taking into account the grammar and expressions of business emails, and generates an appropriate English sentence. Specifically, it uses natural language processing technology to analyze the Japanese content and generate corresponding English expressions. The AI ​​understands the grammar and expressions specific to business emails and creates a reply email with an appropriate tone and style. For example, it generates a professional business email using honorifics and polite expressions. The generation unit displays the generated English reply email to the user, allowing them to review and correct it. Furthermore, the generation unit continuously improves its generation algorithm by utilizing past email data and user feedback. As a result, the generation unit can convert the Japanese content entered by the user into an accurate and polite business English reply email, making business communication more efficient.

[0075] The summarization function can extract the key points of an English business email and summarize them concisely in Japanese. For example, the summarization function can use AI to extract the key points of an email and summarize them concisely in Japanese. The summarization function can extract key points based on frequently occurring keywords or the importance of the context. It can also analyze the content of an email, extract important information, and summarize it. This allows users to quickly understand the content of an email by extracting the key points of an English business email and summarizing them concisely in Japanese. Some or all of the above processing in the summarization function may be performed using AI, for example, or without AI. For example, the summarization function can perform summarization using an AI model that takes an English business email as input and outputs a Japanese summary.

[0076] The generation unit can generate appropriate English sentences by considering the grammar and expressions of business emails. For example, the generation unit can use AI to generate appropriate English sentences by considering the grammar and expressions of business emails. The generation unit can generate English sentences by considering, for example, tense, honorifics, and formal expressions. Furthermore, the generation unit can generate English sentences by considering, for example, the grammatical accuracy and formal expressions of business emails. As a result, by considering the grammar and expressions of business emails and generating appropriate English sentences, users can create reply emails in polite business English. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can generate reply emails using an AI model that takes Japanese reply content as input and outputs a reply email in business English.

[0077] The service provider can provide the user with summarized content. The service provider can, for example, display the summarized content to the user. The service provider can also, for example, notify the user of the summarized content. Furthermore, the service provider can, for example, send the summarized content to the user via email. By providing the user with summarized content, the user can quickly understand the content of the email. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide the summarized content using an AI model that takes the summarized content as input and outputs data for display to the user.

[0078] The input section allows the user to input the content they wish to reply in Japanese as a bulleted list. For example, the input section can determine the format of the bulleted list based on the number of items and the length of each item. The input section can also analyze the content entered by the user and convert it into a bulleted list format. This allows the AI ​​to generate a reply email in appropriate business English by having the user input the content they wish to reply in Japanese as a bulleted list. Some or all of the above processing in the input section may be performed using AI, or not. For example, the input section can process the input content using an AI model that takes the Japanese content entered by the user as input and converts it into a bulleted list format.

[0079] The generation unit can analyze the input Japanese content and generate a reply email in polite business English. For example, the generation unit can use AI to analyze the input Japanese content and generate a reply email in polite business English. The generation unit can generate English sentences considering, for example, the use of honorifics and formal expressions. Furthermore, the generation unit can generate English sentences considering, for example, the grammatical accuracy and formal expressions of business emails. This allows users to smoothly engage in international business communication by analyzing the input Japanese content and generating a reply email in polite business English. Some or all of the above processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can generate a reply email using an AI model that takes Japanese reply content as input and outputs a reply email in business English.

[0080] The reception system can estimate the user's emotions and adjust the way English business emails are received based on those emotions. For example, if the user is stressed, the reception system can provide a simple interface and minimize the steps required to paste the email. If the user is relaxed, the reception system can also provide detailed options and suggest a customizable reception method. Furthermore, if the user is in a hurry, the reception system can prioritize voice input to allow for quick reception of English business emails. This allows users to receive emails without stress by adjusting the reception method according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception system may be performed using AI or not. For example, the reception system can take user emotion data as input and adjust the reception method using an AI model that adjusts the reception method based on the emotion.

[0081] The reception system can analyze a user's past email reception history and propose the optimal reception method. For example, the reception system can automatically recognize the format of emails the user has frequently received in the past and propose the optimal reception method. The reception system can also prioritize suggesting reception methods the user has used in the past (such as copy and paste or file upload). Furthermore, the reception system can predict and propose reception methods to be used during specific time periods based on the user's past reception history. In this way, the reception system can propose the optimal reception method by analyzing the user's past email reception history. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can propose a reception method using an AI model that takes past email reception history as input and outputs the optimal reception method.

[0082] The reception system can adjust the reception method when receiving business emails in English, taking into account the user's current situation. For example, if the user is on a business trip, the reception system can provide a reception method optimized for mobile devices. It can also provide a reception method optimized for desktop environments if the user is in the office. Furthermore, if the user is in a meeting, the reception system can provide a method for receiving emails using voice input or simple taps. This allows the user to receive emails in the most optimal way by adjusting the reception method based on their current situation. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can adjust the reception method using an AI model that takes user's current situation data as input and outputs the optimal reception method.

[0083] The reception desk can estimate the user's emotions and adjust the design of the reception interface based on the estimated emotions. For example, if the user is nervous, the reception desk can provide an interface with calming colors to reduce visual stress. Conversely, if the user is in a good mood, the reception desk can provide an interface with bright colors to make the reception process more enjoyable. Furthermore, if the user is tired, the reception desk can provide a simple and highly visible interface to facilitate the reception process. This allows users to comfortably receive emails by adjusting the reception interface design according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can adjust the interface using an AI model that takes user emotion data as input and adjusts the interface design.

[0084] The reception system can suggest the optimal reception method when receiving business emails in English, by referring to the user's past email history. For example, the reception system can automatically recognize the format of emails the user has frequently received in the past and suggest the optimal reception method. It can also predict the format of emails the user will receive on specific days or times and suggest the optimal reception method. Furthermore, the reception system can analyze the user's past email history and suggest the optimal reception method. This allows the system to suggest the optimal reception method by referring to the user's past email history. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can suggest a reception method using an AI model that takes past email history as input and outputs the optimal reception method.

[0085] The reception desk can suggest the optimal reception method when receiving business emails in English, by referring to the user's calendar information. For example, the reception desk can refer to appointments registered in the user's calendar and suggest the optimal reception method. The reception desk can also suggest reception methods for emails related to specific events based on the user's calendar information. Furthermore, the reception desk can suggest the optimal reception method tailored to the appointment based on the user's calendar information. In this way, the reception desk can suggest the optimal reception method by referring to the user's calendar information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can suggest a reception method using an AI model that takes calendar information as input and outputs the optimal reception method.

[0086] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user is relaxed, the summarization unit can provide a detailed summary. If the user is in a hurry, it can provide a concise summary that gets straight to the point. Furthermore, if the user is excited, it can provide a visually stimulating summary. In this way, by adjusting the way the summary is presented according to the user's emotions, a summary that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can take user emotion data as input and adjust the summary using an AI model that adjusts the way the summary is presented.

[0087] The summarization unit can adjust the level of detail in the summary based on the importance of the English business email. For example, it can provide a detailed summary for high-importance emails, and a concise summary for less important emails. Furthermore, it can adjust the length and level of detail of the summary according to its importance. This allows for a detailed understanding of important email content by adjusting the level of detail in the summary based on the importance of the English business email. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not. For example, the summarization unit can use an AI model that takes email importance data as input and adjusts the level of detail in the summary.

[0088] The summarization unit can apply different summarization algorithms depending on the category of the English business email during the summarization process. For example, in the case of an inquiry email from a customer, the summarization unit will focus on the inquiry content. Similarly, in the case of a proposal email from a business partner, the summarization unit can focus on the proposal content. Furthermore, in the case of an internal communication email, the summarization unit can focus on the important information. This allows for appropriate summarization of email content by applying different summarization algorithms depending on the category of the English business email. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can perform summarization using an AI model that takes email category data as input and applies different summarization algorithms.

[0089] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. For example, if the user is in a hurry, the summarization unit can provide a short, concise summary. If the user is relaxed, the summarization unit can provide a longer summary with more detailed explanations. Furthermore, if the user is excited, the summarization unit can provide a visually stimulating summary. By adjusting the length of the summary according to the user's emotions, a summary that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can take user emotion data as input and adjust the summary using an AI model that adjusts the length of the summary.

[0090] The summarization unit can prioritize summarizing based on when the English business emails were sent. For example, it might prioritize summarizing recently sent emails. It can also prioritize summarizing emails sent at important times. Furthermore, it can prioritize summarizing according to the user's schedule. This allows important emails to be prioritized by prioritizing summarization based on when the English business emails were sent. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not. For example, the summarization unit can take email sending time data as input and perform summarization using an AI model that determines the priority of summarization.

[0091] The summarization unit can adjust the order of summaries based on the relevance of the English business emails during the summarization process. For example, the summarization unit can prioritize summarizing highly relevant emails. It can also postpone summarizing less relevant emails. Furthermore, the summarization unit can adjust the order of summaries according to their relevance. This allows for prioritizing the summarization of highly relevant emails by adjusting the order of summaries based on the relevance of the English business emails. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can perform summaries using an AI model that takes email relevance data as input and adjusts the order of summaries.

[0092] The service provider can estimate the user's emotions and adjust the display method of the summary content based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a display method that gets straight to the point. In this way, by adjusting the display method of the summary content according to the user's emotions, a display that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can take user emotion data as input and adjust the display using an AI model that adjusts the display method.

[0093] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods that the user has previously preferred. The service provider can also suggest the optimal display method based on the user's past operation history. Furthermore, the service provider can analyze the user's past operation history and provide the most efficient display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can select a display method using an AI model that takes past operation history data as input and outputs the optimal display method.

[0094] The service provider can set display priorities based on the importance of the summary content at the time of delivery. For example, the service provider can prioritize the display of summaries with high importance. It can also postpone the display of summaries with low importance. Furthermore, the service provider can set display priorities according to importance. This allows important information to be displayed preferentially by setting display priorities based on the importance of the summary content. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use an AI model that takes summary content importance data as input and sets display priorities to perform the display.

[0095] The information provider can estimate the user's emotions and adjust the display order of the summary content based on the estimated emotions. For example, if the user is nervous, the information provider can display important summary content first. If the user is relaxed, the information provider can also display detailed summary content first. Furthermore, if the user is in a hurry, the information provider can display concise summary content first. In this way, by adjusting the display order of the summary content according to the user's emotions, information can be provided in an order that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can adjust the display using an AI model that takes user emotion data as input and adjusts the display order.

[0096] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. In addition, if the user is using a desktop, the service provider can provide a display method that includes detailed information. This allows the service provider to provide the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can select a display method using an AI model that takes device information as input and outputs the optimal display method.

[0097] The service provider can automatically display additional information related to the summary content at the time of delivery. For example, the service provider can automatically display past emails related to the summary content. It can also automatically display documents and files related to the summary content. Furthermore, the service provider can automatically display web links and reference materials related to the summary content. This allows users to quickly obtain the information they need by automatically displaying additional information related to the summary content. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can display additional information using an AI model that takes the summary content as input and outputs related additional information.

[0098] The input unit can estimate the user's emotions and adjust the input method based on the estimated emotions. For example, if the user is stressed, the input unit can provide a simple interface and minimize the input steps. If the user is relaxed, the input unit can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the input unit can prioritize voice input to allow for quick input of replies. This allows the user to comfortably input replies by adjusting the input method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI or not. For example, the input unit can take user emotion data as input and adjust the input using an AI model that adjusts the input method.

[0099] The input unit can suggest the optimal input method by referring to the user's past input history during input. For example, the input unit can automatically display as suggestions the user has frequently entered reply content in the past. The input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest reply content to be used during specific time periods based on the user's past input history. In this way, the optimal input method can be suggested by referring to the user's past input history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can suggest an input method using an AI model that takes past input history data as input and outputs the optimal input method.

[0100] The input unit can automatically complete the input content based on the user's current situation. For example, if the user is on a business trip, the input unit can automatically complete the reply content related to the destination. Furthermore, if the user is in a meeting, the input unit can automatically complete the reply content related to the meeting's content. Additionally, if the user is in the office, the input unit can automatically complete the reply content based on the office situation. This allows the user to quickly input replies by automatically completing the input content based on their current situation. Some or all of the above processing in the input unit may be performed using AI, or without AI. For example, the input unit can use current situation data as input and complete the input using an AI model that automatically completes the input content.

[0101] The input unit can estimate the user's emotions and prioritize the input content based on the estimated emotions. For example, if the user is nervous, the input unit will prioritize inputting important replies. If the user is relaxed, the input unit can also prioritize inputting detailed replies. Furthermore, if the user is in a hurry, the input unit can prioritize inputting concise replies. This allows for the prioritization of important content by determining the input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can take user emotion data as input and adjust the input using an AI model that determines the priority of the input content.

[0102] The input unit can suggest the optimal input method when the user is inputting data, taking into account the user's geographical location. For example, if the user is overseas, the input unit can suggest an input method that suits the local conditions. Furthermore, if the user is at home, the input unit can suggest an input method that suits the home environment. Additionally, if the user is on the move, the input unit can suggest an input method optimized for mobile devices. This allows the input unit to suggest the optimal input method by considering the user's geographical location. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can suggest an input method using an AI model that takes geographical location information as input and outputs the optimal input method.

[0103] The input unit can analyze the user's social media activity during input and suggest relevant input content. For example, the input unit can suggest relevant replies based on what the user has recently mentioned on social media. It can also analyze the user's social media activity history and suggest the most appropriate replies. Furthermore, the input unit can suggest replies related to topics the user follows on social media. In this way, relevant input content can be suggested by analyzing the user's social media activity. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can suggest input content using an AI model that takes social media activity data as input and outputs relevant input content.

[0104] The generation unit can estimate the user's emotions and adjust the expression of the reply email based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a reply email using polite and detailed language. If the user is in a hurry, the generation unit can also generate a concise and to-the-point reply email. Furthermore, if the user is excited, the generation unit can generate a reply email using visually stimulating language. This allows for the generation of appropriately worded reply emails by adjusting the expression according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can generate reply emails using an AI model that takes user emotion data as input and adjusts the expression of the reply email.

[0105] The generation unit can adjust the level of detail in reply emails based on the importance of the email when generating them. For example, the generation unit can generate a detailed reply email for high-importance emails. It can also generate a concise reply email for low-importance emails. Furthermore, the generation unit can adjust the length and level of detail of the reply email according to its importance. This allows for appropriate replies to important emails by adjusting the level of detail of the reply email based on its importance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take email importance data as input and generate reply emails using an AI model that adjusts the level of detail of the reply email.

[0106] The generation unit can apply different generation algorithms depending on the email category when generating reply emails. For example, in the case of an inquiry email from a customer, the generation unit can generate a polite and detailed reply email. It can also generate a reply email tailored to the specific proposal content in the case of a proposal email from a business partner. Furthermore, it can generate a concise and to-the-point reply email in the case of an internal communication email. Thus, by applying different generation algorithms depending on the email category, appropriate reply emails can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate reply emails using an AI model that takes email category data as input and applies different generation algorithms.

[0107] The generation unit can estimate the user's emotions and adjust the length of the reply email based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point reply email. If the user is relaxed, the generation unit can also generate a longer reply email with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a reply email using visually stimulating language. This allows for the generation of reply emails of appropriate length by adjusting the length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can take user emotion data as input and generate a reply email using an AI model that adjusts the length of the reply email.

[0108] The generation unit can determine the priority of reply emails based on when the emails were sent when generating reply emails. For example, the generation unit may prioritize replying to recently sent emails. It can also prioritize replying to emails sent at important times. Furthermore, the generation unit can determine the priority of reply emails according to the user's schedule. This allows important emails to be replied to preferentially by determining the priority of reply emails based on when they were sent. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take email sending time data as input and generate reply emails using an AI model that determines the priority of reply emails.

[0109] The generation unit can adjust the order of reply emails based on their relevance when generating reply emails. For example, the generation unit can prioritize replying to highly relevant emails. It can also postpone replying to less relevant emails. Furthermore, the generation unit can adjust the order of reply emails according to their relevance. This allows for prioritizing replies to highly relevant emails by adjusting the order of reply emails based on their relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can use an AI model that takes email relevance data as input and adjusts the order of reply emails to generate reply emails.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The reception system can estimate the user's emotions and adjust the way English business emails are received based on those emotions. For example, if the user is stressed, the reception system can provide a simple interface and minimize the steps required to paste the email. If the user is relaxed, the reception system can also provide detailed options and suggest a customizable reception method. Furthermore, if the user is in a hurry, the reception system can prioritize voice input to allow for quick reception of English business emails. This allows users to receive emails without stress by adjusting the reception method according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception system may be performed using AI or not. For example, the reception system can take user emotion data as input and adjust the reception method using an AI model that adjusts the reception method based on the emotion.

[0112] The reception system can analyze a user's past email reception history and propose the optimal reception method. For example, the reception system can automatically recognize the format of emails the user has frequently received in the past and propose the optimal reception method. The reception system can also prioritize suggesting reception methods the user has used in the past (such as copy and paste or file upload). Furthermore, the reception system can predict and propose reception methods to be used during specific time periods based on the user's past reception history. In this way, the reception system can propose the optimal reception method by analyzing the user's past email reception history. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can propose a reception method using an AI model that takes past email reception history as input and outputs the optimal reception method.

[0113] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user is relaxed, the summarization unit can provide a detailed summary. If the user is in a hurry, it can provide a concise summary that gets straight to the point. Furthermore, if the user is excited, it can provide a visually stimulating summary. In this way, by adjusting the way the summary is presented according to the user's emotions, a summary that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can take user emotion data as input and adjust the summary using an AI model that adjusts the way the summary is presented.

[0114] The summarization unit can adjust the level of detail in the summary based on the importance of the English business email. For example, it can provide a detailed summary for high-importance emails, and a concise summary for less important emails. Furthermore, it can adjust the length and level of detail of the summary according to its importance. This allows for a detailed understanding of important email content by adjusting the level of detail in the summary based on the importance of the English business email. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not. For example, the summarization unit can use an AI model that takes email importance data as input and adjusts the level of detail in the summary.

[0115] The service provider can estimate the user's emotions and adjust the display method of the summary content based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a display method that gets straight to the point. In this way, by adjusting the display method of the summary content according to the user's emotions, a display that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can take user emotion data as input and adjust the display using an AI model that adjusts the display method.

[0116] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods that the user has previously preferred. The service provider can also suggest the optimal display method based on the user's past operation history. Furthermore, the service provider can analyze the user's past operation history and provide the most efficient display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can select a display method using an AI model that takes past operation history data as input and outputs the optimal display method.

[0117] The input unit can estimate the user's emotions and adjust the input method based on the estimated emotions. For example, if the user is stressed, the input unit can provide a simple interface and minimize the input steps. If the user is relaxed, the input unit can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the input unit can prioritize voice input to allow for quick input of replies. This allows the user to comfortably input replies by adjusting the input method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI or not. For example, the input unit can take user emotion data as input and adjust the input using an AI model that adjusts the input method.

[0118] The input unit can suggest the optimal input method by referring to the user's past input history during input. For example, the input unit can automatically display as suggestions the user has frequently entered reply content in the past. The input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest reply content to be used during specific time periods based on the user's past input history. In this way, the optimal input method can be suggested by referring to the user's past input history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can suggest an input method using an AI model that takes past input history data as input and outputs the optimal input method.

[0119] The generation unit can estimate the user's emotions and adjust the expression of the reply email based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a reply email using polite and detailed language. If the user is in a hurry, the generation unit can also generate a concise and to-the-point reply email. Furthermore, if the user is excited, the generation unit can generate a reply email using visually stimulating language. This allows for the generation of appropriately worded reply emails by adjusting the expression according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can generate reply emails using an AI model that takes user emotion data as input and adjusts the expression of the reply email.

[0120] The generation unit can adjust the level of detail in reply emails based on the importance of the email when generating them. For example, the generation unit can generate a detailed reply email for high-importance emails. It can also generate a concise reply email for low-importance emails. Furthermore, the generation unit can adjust the length and level of detail of the reply email according to its importance. This allows for appropriate replies to important emails by adjusting the level of detail of the reply email based on its importance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take email importance data as input and generate reply emails using an AI model that adjusts the level of detail of the reply email.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk accepts business emails in English. For example, users can simply copy and paste the entire content of a business email they received in English into the tool. Step 2: The summarization department analyzes the English business emails received by the reception department and summarizes them in Japanese. For example, AI extracts the key points of the email and summarizes them concisely in Japanese. Step 3: The providing section provides the content summarized by the summarizing section. For example, it displays the summarized content to the user. Step 4: The input section will enter the reply content in Japanese based on the summary content provided by the provider section. For example, the user will enter the content they want to reply in Japanese using bullet points. Step 5: The generation unit analyzes the Japanese content entered by the input unit and generates a reply email in business English. For example, the AI ​​analyzes the entered Japanese content, takes into account the grammar and expressions of a business email, and generates an appropriate English sentence.

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the reception unit, summarization unit, provision unit, input unit, and generation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, which receives the content of a business email received in English by the user pasting the email into the tool. The summarization unit is implemented by the specific processing unit 290 of the data processing unit 12, where AI extracts the key points of the email and summarizes them concisely in Japanese. The provision unit is implemented by the output device 40 of the smart device 14, which displays the summarized content to the user. The input unit is implemented by the reception device 38 of the smart device 14, where the user inputs the content they wish to reply in Japanese in bullet points. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where AI analyzes the input Japanese content and generates a reply email in business English. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, summarization unit, provision unit, input unit, and generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, and the user receives the content of a business email received in English by pasting it into the tool. The summarization unit is implemented by the identification processing unit 290 of the data processing unit 12, and the AI ​​extracts the important points of the email and summarizes them concisely in Japanese. The provision unit is implemented by the speaker 240 of the smart glasses 214, and displays the summarized content to the user. The input unit is implemented by the microphone 238 of the smart glasses 214, and the user inputs the content they wish to reply in Japanese in bullet points. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, and the AI ​​analyzes the input Japanese content and generates a reply email in business English. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the reception unit, summarization unit, provision unit, input unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, and the user receives the content of a business email received in English by pasting it into the tool. The summarization unit is implemented by the identification processing unit 290 of the data processing unit 12, and the AI ​​extracts the important points of the email and summarizes them concisely in Japanese. The provision unit is implemented by the display 343 of the headset terminal 314, and displays the summarized content to the user. The input unit is implemented by the microphone 238 of the headset terminal 314, and the user inputs the content they wish to reply in Japanese in bullet points. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, and the AI ​​analyzes the input Japanese content and generates a reply email in business English. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the 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.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the reception unit, summarization unit, provision unit, input unit, and generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, and the user receives the content of a business email received in English by pasting it into the tool. The summarization unit is implemented by the identification processing unit 290 of the data processing unit 12, and the AI ​​extracts the important points of the email and summarizes them concisely in Japanese. The provision unit is implemented by the speaker 240 of the robot 414, and displays the summarized content to the user. The input unit is implemented by the microphone 238 of the robot 414, and the user inputs the content they wish to reply in Japanese in bullet points. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, and the AI ​​analyzes the input Japanese content and generates a reply email in business English. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0176] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A reception desk that handles business emails in English, The summarization unit analyzes the English business emails received by the reception unit and summarizes them in Japanese. A providing unit that provides the content summarized by the summarizing unit, An input unit for inputting the reply content in Japanese based on the summary content provided by the aforementioned provision unit, The system comprises: an input unit that analyzes the Japanese content entered by the input unit and generates a reply email in business English; and A system characterized by the following features. (Note 2) The summary section above is, Extract the key points from English business emails and summarize them concisely in Japanese. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is It generates appropriate English sentences, taking into account the grammar and expressions used in business emails. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide users with a summarized version of the content. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned input unit is Enter the user's reply in Japanese using bullet points. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is The system analyzes the entered Japanese text and generates a reply email in polite business English. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's sentiment and adjusts how English business emails are received based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the user's past email reception history and suggest the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving business emails in English, the reception method is adjusted to take into account the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and adjusts the reception interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving business emails in English, the system suggests the most suitable receiving method based on the user's past email history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving business emails in English, the system refers to the user's calendar information to suggest the most suitable method of receiving them. The system described in Appendix 1, characterized by the features described herein. (Note 13) The summary section above is, It estimates the user's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The summary section above is, When summarizing, adjust the level of detail in the summary based on the importance of the English business email. The system described in Appendix 1, characterized by the features described herein. (Note 15) The summary section above is, When summarizing, different summarization algorithms are applied depending on the category of the English business email. The system described in Appendix 1, characterized by the features described herein. (Note 16) The summary section above is, It estimates the user's sentiment and adjusts the length of the summary based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The summary section above is, When summarizing, prioritize summaries based on when the English business emails were sent. The system described in Appendix 1, characterized by the features described herein. (Note 18) The summary section above is, When summarizing, adjust the order of the summaries based on the relevance of the English business emails. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the summary content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the summary, the display priority is set based on the importance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the display order of the summary content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When provided, additional information related to the summary content will be automatically displayed. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned input unit is It estimates the user's emotions and adjusts the input method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned input unit is During input, the system refers to the user's past input history to suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned input unit is The system automatically completes input based on the user's current situation during input. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned input unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned input unit is When inputting data, the system suggests the optimal input method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned input unit is During input, the system analyzes the user's social media activity and suggests relevant input content. The system described in Appendix 1, characterized by the features described herein. (Note 31) The generating unit is The system estimates the user's emotions and adjusts the wording of the reply email based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The generating unit is When generating a reply email, adjust the level of detail in the reply email based on the importance of the email. The system described in Appendix 1, characterized by the features described herein. (Note 33) The generating unit is When generating reply emails, different generation algorithms are applied depending on the email category. The system described in Appendix 1, characterized by the features described herein. (Note 34) The generating unit is It estimates the user's emotions and adjusts the length of the reply email based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The generating unit is When generating a reply email, the priority of the reply email is determined based on when the email was sent. The system described in Appendix 1, characterized by the features described herein. (Note 36) The generating unit is When generating reply emails, the order of reply emails is adjusted based on the relevance of the emails. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that handles business emails in English, The summarization unit analyzes the English business emails received by the reception unit and summarizes them in Japanese. A providing unit that provides the content summarized by the summarizing unit, An input unit for inputting the reply content in Japanese based on the summary content provided by the aforementioned provision unit, The system comprises: an input unit that analyzes the Japanese content entered by the input unit and generates a reply email in business English; and A system characterized by the following features.

2. The summary section above is, Extract the key points from English business emails and summarize them concisely in Japanese. The system according to feature 1.

3. The generating unit is It generates appropriate English sentences, taking into account the grammar and expressions used in business emails. The system according to feature 1.

4. The aforementioned supply unit is, Provide users with a summarized version of the content. The system according to feature 1.

5. The aforementioned input unit is Enter the user's reply in Japanese using bullet points. The system according to feature 1.

6. The generating unit is The system analyzes the entered Japanese text and generates a reply email in polite business English. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's sentiment and adjusts how English business emails are received based on the estimated sentiment. The system according to feature 1.

8. The aforementioned reception unit is We analyze the user's past email reception history and suggest the optimal reception method. The system according to feature 1.

9. The aforementioned reception unit is When receiving business emails in English, the reception method is adjusted to take into account the user's current situation. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and adjusts the reception interface design based on the estimated user emotions. The system according to feature 1.

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