system

Abstract

The system according to this embodiment aims to efficiently perform the creation, translation, voice input, and reading aloud of business emails. [Solution] The system according to the embodiment comprises a generation unit, an adjustment unit, a translation unit, a reception unit, and a reading unit. The generation unit generates business emails. The adjustment unit adjusts the context of the emails generated by the generation unit. The translation unit translates the emails adjusted by the adjustment unit. The reception unit receives voice input. The reading unit reads the emails aloud based on the voice input received by the reception unit.

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 conventional technology, there is a problem that the creation and translation of business e-mails, and the voice input and reading function for visually impaired people are not integrated, making it difficult to create e-mails efficiently.

[0005] The system according to the embodiment aims to efficiently perform the creation, translation, voice input, and reading of business e-mails.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, an adjustment unit, a translation unit, a reception unit, and a reading unit. The generation unit generates business emails. The adjustment unit adjusts the context of the emails generated by the generation unit. The translation unit translates the emails adjusted by the adjustment unit. The reception unit receives voice input. The reading unit reads the emails aloud based on the voice input received by the reception unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently create, translate, input voice data into, and read aloud business emails. [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 numbered 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] [[ID=ID=18]]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 generation system according to an embodiment of the present invention is a system that automatically generates business emails, adjusts the context, translates them, accepts voice input, and reads aloud the generated emails. When a user creates a business email, the generating AI understands the context and generates appropriate text. The context can then be adjusted using a controller. Furthermore, if the creator's language differs from the recipient's language, the email is automatically translated into the creator's native language after completion, allowing for content checking. For visually impaired users, voice prompts can be used to generate the email, which is then read aloud for checking. For example, when a user creates a business email, the generating AI understands the context and generates appropriate text. The user only needs to input simple prompts. For example, inputting "Send a meeting reminder" causes the generating AI to generate an appropriate reminder email. The context can then be adjusted using a controller. For example, parts of the generated email can be modified, or additional information can be inserted. This allows the user to adjust the generated email to their own intentions. Furthermore, if the sender's language differs from the recipient's language, the email can be automatically translated into the sender's native language after completion, allowing for content review. For example, an email written in Japanese can be translated into English, allowing the sender to review its content. Finally, for visually impaired users, voice prompts can be used to generate emails using AI, which can then read the email aloud for review. For instance, if a visually impaired user voice-inputs "Send a meeting reminder," the AI ​​will generate an appropriate reminder email and read its contents aloud after completion. This enables the business email generation system to handle business email generation, contextualization, translation, voice input acceptance, and reading aloud.

[0029] The business email generation system according to this embodiment comprises a generation unit, an adjustment unit, a translation unit, a reception unit, and a reading unit. The generation unit generates business emails. The generation unit uses a generation AI to understand the context and generate appropriate text. For example, if the user inputs "Send a meeting reminder," the generation AI generates an appropriate reminder email. The generation unit also understands the context so that the generation AI can generate text that aligns with the user's intent. For example, the generation unit receives a prompt from the generation AI saying "Summarize the main points of this text," extracts the main points, and creates a summary. The adjustment unit adjusts the context of the generated email. The adjustment unit modifies parts of the generated email or inserts additional information. For example, the adjustment unit modifies the grammar of the generated email. The adjustment unit can also insert additional information into the generated email. For example, the adjustment unit adds relevant data or supplementary explanations to the generated email. The translation unit automatically translates the email text if the language of the creator and the language of the recipient are different. The translation unit can translate generated emails into the author's native language and check the content. For example, the translation unit can translate an email written in Japanese into English, allowing the author to review the content. The reception unit accepts voice prompts for visually impaired users. The reception unit accepts voice prompts from visually impaired users, and the generation AI creates the email. For example, if a visually impaired user voice-inputs "Send a meeting reminder," the generation AI will generate an appropriate reminder email. The reading unit reads the email aloud for review after creation is complete. The reading unit reads the generated email aloud, allowing visually impaired users to review the content. For example, the reading unit reads the generated email aloud, allowing visually impaired users to review the content. This enables the business email generation system to generate, contextualize, translate, accept voice input, and read aloud business emails.

[0030] The generation unit can understand context and generate text using generation AI. The generation unit uses generation AI to understand context and generate appropriate text. For example, if a user inputs "Send a meeting reminder," the generation AI will generate an appropriate reminder email. Furthermore, the generation unit understands context so that the generation AI can generate text that aligns with the user's intent. For example, if the generation unit receives a prompt such as "Summarize the main points of this text," the generation AI will extract the main points and create a summary. This allows the generation AI to understand context and generate appropriate text, thereby creating emails that align with the user's intent.

[0031] The adjustment unit can modify parts of the generated email and insert additional information. The adjustment unit adjusts the context of the generated email. For example, the adjustment unit can correct the grammar of the generated email. It can also insert additional information into the generated email. For example, the adjustment unit can add relevant data or supplementary explanations to the generated email. This allows the email to be tailored to the user's intent by modifying parts of the generated email or inserting additional information.

[0032] The translation unit can automatically translate emails when the sender's language differs from the recipient's language. The translation unit can then translate the generated email into the sender's native language and allow them to check the content. For example, the translation unit can translate an email written in Japanese into English, allowing the sender to review the content. This enables communication that transcends language barriers, even when the sender's language differs from the recipient's language, by automatically translating emails.

[0033] The reception desk can accept voice prompts from visually impaired individuals. The reception desk accepts voice prompts from visually impaired individuals, and a generative AI creates an email. For example, if a visually impaired person voice-inputs "Send a meeting reminder," the generative AI generates an appropriate reminder email. This allows visually impaired individuals to easily create emails by accepting voice prompts.

[0034] The text-to-speech function can read the email aloud for checking after it has been created. The text-to-speech function reads the generated email aloud, allowing visually impaired individuals to review its content. For example, the text-to-speech function reads the generated email aloud, allowing visually impaired individuals to review its content. This allows visually impaired individuals to review the email content by having it read aloud after it has been created.

[0035] The generation unit can analyze a user's past email history and select a writing style. Using generation AI, the generation unit analyzes the user's past email history and selects the optimal writing style. For example, the generation unit analyzes the tone and style of emails the user has sent in the past, and the generation AI generates text based on that. The generation unit can also analyze the wording and expressions the user has used with specific recipients, and the generation AI can reflect that. Furthermore, the generation unit can analyze the formal and casual styles the user has used in the past, and the generation AI can select the appropriate style. This allows for the selection of the optimal writing style and the generation of more consistent emails by analyzing the user's past email history.

[0036] The generation unit can automatically insert industry-specific terminology and phrases during the generation process. Using generation AI, the generation unit automatically inserts industry-specific terminology and phrases during generation. For example, if the user belongs to the IT industry, the generation AI will insert industry-specific technical terms into the text. Similarly, if the user belongs to the medical industry, the generation AI can insert medical and specialized terminology. Furthermore, if the user belongs to the financial industry, the generation AI can insert financial terms and industry-specific phrases. This allows for the generation of more specialized emails by automatically inserting industry-specific terminology and phrases.

[0037] The adjustment unit can suggest the optimal correction method by referring to the user's past revision history during the adjustment process. The adjustment unit uses AI to suggest the optimal correction method by referring to the user's past revision history. For example, the adjustment unit can suggest the optimal correction method based on the user's frequently made past revision patterns. The adjustment unit can also refer to the user's revision history for specific recipients, and the AI ​​can suggest corrections based on that. Furthermore, the adjustment unit can analyze the user's past revision trends, and the AI ​​can suggest the optimal correction method. This allows for efficient adjustments by suggesting the optimal correction method by referring to the user's past revision history.

[0038] The adjustment unit can perform adjustments based on the attribute information of the email recipient. The adjustment unit uses AI to consider the attribute information of the email recipient when performing adjustments. For example, if the recipient is a superior, the adjustment unit will prioritize formal language. If the recipient is a colleague, the adjustment unit can also prioritize casual language. Furthermore, if the recipient is a customer, the adjustment unit can prioritize polite and professional language. This allows for more appropriate adjustments by considering the attribute information of the email recipient.

[0039] The translation department can select a translation method that takes into account the context of the email during translation. Using AI, the translation department selects the most appropriate translation method based on the email's context. For example, if the email is business-related, the translation department will select a formal translation method. If the email is a casual conversation, it can select a casual translation method. Furthermore, if the email contains technical information, the translation department can select a method that appropriately translates technical terms. This allows for the selection of a more appropriate translation method and enables accurate translation by considering the email's context.

[0040] The translation department can take into account the cultural background of the email recipient when translating. Using AI, the translation department considers the recipient's cultural background during the translation process. For example, if the recipient is Japanese, the translation department will consider Japanese culture. Similarly, if the recipient is American, the translation department can consider American culture. Furthermore, if the recipient is French, the translation department can consider French culture. This allows for more appropriate translations by considering the recipient's cultural background.

[0041] The reception system can suggest the optimal reception method by referring to the user's past voice input history during voice input. Using AI, the reception system suggests the optimal reception method based on the user's past voice input history. For example, it can suggest the optimal reception method based on the voice commands the user has frequently used in the past. It can also suggest the optimal reception method by referring to the voice commands the user has used for specific recipients. Furthermore, it can analyze the user's past voice input trends and suggest the optimal reception method. This allows for efficient voice input by suggesting the optimal reception method based on the user's past voice input history.

[0042] The reception unit can improve the accuracy of speech recognition by taking into account the user's pronunciation characteristics during voice input. The reception unit uses AI to improve the accuracy of speech recognition by considering the user's pronunciation characteristics during voice input. For example, the reception unit learns the user's pronunciation characteristics to improve the accuracy of speech recognition. Furthermore, if the user has a specific accent, the reception unit can perform speech recognition corresponding to that accent. The reception unit can also improve the accuracy of speech recognition by considering the user's pronunciation habits. As a result, by considering the user's pronunciation characteristics, the accuracy of speech recognition is improved, enabling more accurate voice input.

[0043] The text-to-speech unit can suggest the optimal reading method by referring to the user's past reading history during reading. Using AI, the unit suggests the optimal reading method by referring to the user's past reading history. For example, it can suggest the optimal reading method based on the reading tone the user has preferred in the past. It can also suggest the optimal reading method by referring to the reading tone the user has used for specific recipients. Furthermore, it can analyze the user's past reading trends and suggest the optimal reading method. This allows for efficient reading by suggesting the optimal reading method based on the user's past reading history.

[0044] The text-to-speech function can read emails while considering the recipient's attributes. Using AI, the text-to-speech function takes the recipient's attributes into account when reading. For example, if the recipient is a superior, the text-to-speech function will read in a formal tone. If the recipient is a colleague, it can read in a casual tone. Furthermore, if the recipient is a customer, it can read in a polite and professional tone. This allows for more appropriate reading by considering the recipient's attributes.

[0045] The text-to-speech function can adjust the level of detail in its reading based on when the email was sent. Using AI, the text-to-speech function adjusts the level of detail based on the email's sending date. For example, in the case of an urgent email, the text-to-speech function will provide a concise and to-the-point reading. For regular report emails, the text-to-speech function can also provide a reading that includes detailed information. Furthermore, for event invitation emails, the text-to-speech function can include detailed event information. By adjusting the level of detail based on when the email was sent, more appropriate readings become possible.

[0046] The text-to-speech unit can improve the accuracy of its reading by referencing relevant literature related to the email during the reading process. The text-to-speech unit uses AI to improve accuracy by referencing relevant literature related to the email during reading. For example, the text-to-speech unit can refer to literature related to the email's content to ensure accurate reading. It can also refer to the latest research findings related to the email's topic and read them aloud. Furthermore, it can refer to industry guidelines related to the email's content and read them aloud. This allows for improved reading accuracy and more precise reading by referencing relevant literature related to the email.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The generation unit can also analyze the user's past email history and select a writing style. For example, the generation unit can analyze the tone and style of emails the user has sent in the past, and the generation AI can generate text based on that. The generation unit can also analyze the wording and expressions the user has used with specific recipients, and the generation AI can reflect that. Furthermore, the generation unit can analyze the formal and casual styles the user has used in the past, and the generation AI can select the appropriate style. As a result, by analyzing the user's past email history, the optimal writing style can be selected, and more consistent emails can be generated.

[0049] The generation unit can also automatically insert industry-specific terminology and phrases during the generation process. For example, if the user belongs to the IT industry, the generation AI will insert industry-specific technical terms into the text. Similarly, if the user belongs to the medical industry, the generation AI can insert medical and specialized terminology. Furthermore, if the user belongs to the financial industry, the generation AI can insert financial terms and industry-specific phrases. This allows for the generation of more professional emails by automatically inserting industry-specific terminology and phrases.

[0050] The adjustment unit can also suggest the optimal correction method by referring to the user's past revision history during the adjustment process. For example, the adjustment unit can use AI to suggest the optimal correction method based on the user's frequently used revision patterns in the past. Furthermore, the adjustment unit can refer to the user's revision history for specific recipients, and the AI ​​can suggest corrections based on that. In addition, the adjustment unit can analyze the user's past revision trends, and the AI ​​can suggest the optimal correction method. This allows for efficient adjustments by suggesting the optimal correction method through reference to the user's past revision history.

[0051] The translation department can also select a translation method that takes into account the context of the email. For example, if the email is business-related, the department will select a formal translation method. If the email is a casual conversation, the department can select a casual translation method. Furthermore, if the email contains technical information, the department can select a method that appropriately translates technical terms. By considering the context of the email, the department can select a more appropriate translation method and achieve a more accurate translation.

[0052] The reception unit can also improve the accuracy of speech recognition by taking into account the user's pronunciation characteristics during voice input. For example, the reception unit can learn the user's pronunciation characteristics to improve the accuracy of speech recognition. Furthermore, if the user has a specific accent, the reception unit can perform speech recognition corresponding to that accent. In addition, the reception unit can improve the accuracy of speech recognition by taking into account the user's pronunciation habits. As a result, by considering the user's pronunciation characteristics, the accuracy of speech recognition is improved, enabling more accurate voice input.

[0053] The text-to-speech function can also adjust the level of detail in its reading based on when the email was sent. For example, for urgent emails, the reading function will provide a concise and to-the-point reading. For regular report emails, it can provide a reading that includes detailed information. Furthermore, for event invitation emails, it can provide a reading that includes detailed event information. By adjusting the level of detail in the reading based on when the email was sent, more appropriate readings can be achieved.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The generation unit generates business emails. The generation unit uses generation AI to understand the context and generate appropriate text. For example, if the user enters "Send a meeting reminder," the generation AI will generate an appropriate reminder email. The generation unit also understands the context so that the generation AI can generate text that aligns with the user's intent. For example, if it receives a prompt such as "Summarize the main points of this text," it will extract the main points and create a summary. Step 2: The adjustment unit adjusts the context of the generated email. The adjustment unit modifies parts of the generated email or inserts additional information. For example, it can correct the grammar of the generated email or add relevant data or supplementary explanations. Step 3: The translation unit automatically translates emails if the sender's language differs from the recipient's language. For example, an email written in Japanese can be translated into English, allowing the sender to review the content. Step 4: The reception desk accepts voice prompts for visually impaired users. For example, if a visually impaired user voice-inputs "Send a meeting reminder," the generation AI will generate an appropriate reminder email. Step 5: The reading section reads the email aloud after it has been created for review. For example, the generated email can be read aloud, allowing visually impaired individuals to check its contents.

[0056] (Example of form 2) The business email generation system according to an embodiment of the present invention is a system that automatically generates business emails, adjusts the context, translates them, accepts voice input, and reads aloud the generated emails. When a user creates a business email, the generating AI understands the context and generates appropriate text. The context can then be adjusted using a controller. Furthermore, if the creator's language differs from the recipient's language, the email is automatically translated into the creator's native language after completion, allowing for content checking. For visually impaired users, voice prompts can be used to generate the email, which is then read aloud for checking. For example, when a user creates a business email, the generating AI understands the context and generates appropriate text. The user only needs to input simple prompts. For example, inputting "Send a meeting reminder" causes the generating AI to generate an appropriate reminder email. The context can then be adjusted using a controller. For example, parts of the generated email can be modified, or additional information can be inserted. This allows the user to adjust the generated email to their own intentions. Furthermore, if the sender's language differs from the recipient's language, the email can be automatically translated into the sender's native language after completion, allowing for content review. For example, an email written in Japanese can be translated into English, allowing the sender to review its content. Finally, for visually impaired users, voice prompts can be used to generate emails using AI, which can then read the email aloud for review. For instance, if a visually impaired user voice-inputs "Send a meeting reminder," the AI ​​will generate an appropriate reminder email and read its contents aloud after completion. This enables the business email generation system to handle business email generation, contextualization, translation, voice input acceptance, and reading aloud.

[0057] The business email generation system according to this embodiment comprises a generation unit, an adjustment unit, a translation unit, a reception unit, and a reading unit. The generation unit generates business emails. The generation unit uses a generation AI to understand the context and generate appropriate text. For example, if the user inputs "Send a meeting reminder," the generation AI generates an appropriate reminder email. The generation unit also understands the context so that the generation AI can generate text that aligns with the user's intent. For example, the generation unit receives a prompt from the generation AI saying "Summarize the main points of this text," extracts the main points, and creates a summary. The adjustment unit adjusts the context of the generated email. The adjustment unit modifies parts of the generated email or inserts additional information. For example, the adjustment unit modifies the grammar of the generated email. The adjustment unit can also insert additional information into the generated email. For example, the adjustment unit adds relevant data or supplementary explanations to the generated email. The translation unit automatically translates the email text if the language of the creator and the language of the recipient are different. The translation unit can translate generated emails into the author's native language and check the content. For example, the translation unit can translate an email written in Japanese into English, allowing the author to review the content. The reception unit accepts voice prompts for visually impaired users. The reception unit accepts voice prompts from visually impaired users, and the generation AI creates the email. For example, if a visually impaired user voice-inputs "Send a meeting reminder," the generation AI will generate an appropriate reminder email. The reading unit reads the email aloud for review after creation is complete. The reading unit reads the generated email aloud, allowing visually impaired users to review the content. For example, the reading unit reads the generated email aloud, allowing visually impaired users to review the content. This enables the business email generation system to generate, contextualize, translate, accept voice input, and read aloud business emails.

[0058] The generation unit can understand context and generate text using generation AI. The generation unit uses generation AI to understand context and generate appropriate text. For example, if a user inputs "Send a meeting reminder," the generation AI will generate an appropriate reminder email. Furthermore, the generation unit understands context so that the generation AI can generate text that aligns with the user's intent. For example, if the generation unit receives a prompt such as "Summarize the main points of this text," the generation AI will extract the main points and create a summary. This allows the generation AI to understand context and generate appropriate text, thereby creating emails that align with the user's intent.

[0059] The adjustment unit can modify parts of the generated email and insert additional information. The adjustment unit adjusts the context of the generated email. For example, the adjustment unit can correct the grammar of the generated email. It can also insert additional information into the generated email. For example, the adjustment unit can add relevant data or supplementary explanations to the generated email. This allows the email to be tailored to the user's intent by modifying parts of the generated email or inserting additional information.

[0060] The translation unit can automatically translate emails when the sender's language differs from the recipient's language. The translation unit can then translate the generated email into the sender's native language and allow them to check the content. For example, the translation unit can translate an email written in Japanese into English, allowing the sender to review the content. This enables communication that transcends language barriers, even when the sender's language differs from the recipient's language, by automatically translating emails.

[0061] The reception desk can accept voice prompts from visually impaired individuals. The reception desk accepts voice prompts from visually impaired individuals, and a generative AI creates an email. For example, if a visually impaired person voice-inputs "Send a meeting reminder," the generative AI generates an appropriate reminder email. This allows visually impaired individuals to easily create emails by accepting voice prompts.

[0062] The text-to-speech function can read the email aloud for checking after it has been created. The text-to-speech function reads the generated email aloud, allowing visually impaired individuals to review its content. For example, the text-to-speech function reads the generated email aloud, allowing visually impaired individuals to review its content. This allows visually impaired individuals to review the email content by having it read aloud after it has been created.

[0063] The generation unit can estimate the user's emotions and adjust the tone of the generated text based on those emotions. The generation unit uses a generation AI to estimate the user's emotions and adjusts the tone of the generated text based on those emotions. For example, if the user is stressed, the generation AI will generate text in a calm and soothing tone. Similarly, if the user is happy, the generation AI can generate text in a bright and positive tone. Furthermore, if the user is in a hurry, the generation AI can generate text in a concise and quick tone. This allows for the generation of emails with a more appropriate tone by adjusting the tone of text based on the user's emotions.

[0064] The generation unit can analyze a user's past email history and select a writing style. Using generation AI, the generation unit analyzes the user's past email history and selects the optimal writing style. For example, the generation unit analyzes the tone and style of emails the user has sent in the past, and the generation AI generates text based on that. The generation unit can also analyze the wording and expressions the user has used with specific recipients, and the generation AI can reflect that. Furthermore, the generation unit can analyze the formal and casual styles the user has used in the past, and the generation AI can select the appropriate style. This allows for the selection of the optimal writing style and the generation of more consistent emails by analyzing the user's past email history.

[0065] The generation unit can automatically insert industry-specific terminology and phrases during the generation process. Using generation AI, the generation unit automatically inserts industry-specific terminology and phrases during generation. For example, if the user belongs to the IT industry, the generation AI will insert industry-specific technical terms into the text. Similarly, if the user belongs to the medical industry, the generation AI can insert medical and specialized terminology. Furthermore, if the user belongs to the financial industry, the generation AI can insert financial terms and industry-specific phrases. This allows for the generation of more specialized emails by automatically inserting industry-specific terminology and phrases.

[0066] The adjustment unit can estimate the user's emotions and determine the priority of adjustments based on those emotions. The adjustment unit uses AI to estimate the user's emotions and determines the priority of adjustments based on those emotions. For example, if the user is stressed, the adjustment unit will prioritize displaying important information and omit unnecessary information. If the user is relaxed, the adjustment unit can also make adjustments that include detailed information. Furthermore, if the user is in a hurry, the adjustment unit can display the most important information first and make adjustments quickly. This allows for more appropriate adjustments by prioritizing adjustments based on the user's emotions.

[0067] The adjustment unit can suggest the optimal correction method by referring to the user's past revision history during the adjustment process. The adjustment unit uses AI to suggest the optimal correction method by referring to the user's past revision history. For example, the adjustment unit can suggest the optimal correction method based on the user's frequently made past revision patterns. The adjustment unit can also refer to the user's revision history for specific recipients, and the AI ​​can suggest corrections based on that. Furthermore, the adjustment unit can analyze the user's past revision trends, and the AI ​​can suggest the optimal correction method. This allows for efficient adjustments by suggesting the optimal correction method by referring to the user's past revision history.

[0068] The adjustment unit can perform adjustments based on the attribute information of the email recipient. The adjustment unit uses AI to consider the attribute information of the email recipient when performing adjustments. For example, if the recipient is a superior, the adjustment unit will prioritize formal language. If the recipient is a colleague, the adjustment unit can also prioritize casual language. Furthermore, if the recipient is a customer, the adjustment unit can prioritize polite and professional language. This allows for more appropriate adjustments by considering the attribute information of the email recipient.

[0069] The translation unit can estimate the user's emotions and adjust the tone of the translation based on those emotions. Using AI, the translation unit estimates the user's emotions and adjusts the tone of the translation accordingly. For example, if the user is stressed, the translation unit will use a calm and soothing tone. If the user is happy, the translation unit can use a bright and positive tone. Furthermore, if the user is in a hurry, the translation unit can use a concise and quick tone. This allows for more appropriate translations by adjusting the tone based on the user's emotions.

[0070] The translation department can select a translation method that takes into account the context of the email during translation. Using AI, the translation department selects the most appropriate translation method based on the email's context. For example, if the email is business-related, the translation department will select a formal translation method. If the email is a casual conversation, it can select a casual translation method. Furthermore, if the email contains technical information, the translation department can select a method that appropriately translates technical terms. This allows for the selection of a more appropriate translation method and enables accurate translation by considering the email's context.

[0071] The translation department can take into account the cultural background of the email recipient when translating. Using AI, the translation department considers the recipient's cultural background during the translation process. For example, if the recipient is Japanese, the translation department will consider Japanese culture. Similarly, if the recipient is American, the translation department can consider American culture. Furthermore, if the recipient is French, the translation department can consider French culture. This allows for more appropriate translations by considering the recipient's cultural background.

[0072] The reception desk can estimate the user's emotions and adjust the voice input reception method based on the estimated emotions. The reception desk uses AI to estimate the user's emotions and adjusts the voice input reception method based on the estimated emotions. For example, if the user is nervous, the reception desk will accept voice input in a calm voice. If the user is relaxed, the reception desk can accept voice input in a cheerful voice. Furthermore, if the user is in a hurry, the reception desk can accept quick and concise voice input. This allows for more appropriate voice input by adjusting the voice input reception method based on the user's emotions.

[0073] The reception system can suggest the optimal reception method by referring to the user's past voice input history during voice input. Using AI, the reception system suggests the optimal reception method based on the user's past voice input history. For example, it can suggest the optimal reception method based on the voice commands the user has frequently used in the past. It can also suggest the optimal reception method by referring to the voice commands the user has used for specific recipients. Furthermore, it can analyze the user's past voice input trends and suggest the optimal reception method. This allows for efficient voice input by suggesting the optimal reception method based on the user's past voice input history.

[0074] The reception unit can improve the accuracy of speech recognition by taking into account the user's pronunciation characteristics during voice input. The reception unit uses AI to improve the accuracy of speech recognition by considering the user's pronunciation characteristics during voice input. For example, the reception unit learns the user's pronunciation characteristics to improve the accuracy of speech recognition. Furthermore, if the user has a specific accent, the reception unit can perform speech recognition corresponding to that accent. The reception unit can also improve the accuracy of speech recognition by considering the user's pronunciation habits. As a result, by considering the user's pronunciation characteristics, the accuracy of speech recognition is improved, enabling more accurate voice input.

[0075] The text-to-speech unit can estimate the user's emotions and adjust its tone of voice based on those emotions. Using AI, the unit estimates the user's emotions and adjusts its tone of voice accordingly. For example, if the user is nervous, the unit will read in a calm voice. If the user is relaxed, it can read in a cheerful voice. Furthermore, if the user is in a hurry, it can read quickly and concisely. By adjusting the tone of voice based on the user's emotions, a more appropriate tone of voice can be achieved.

[0076] The text-to-speech unit can suggest the optimal reading method by referring to the user's past reading history during reading. Using AI, the unit suggests the optimal reading method by referring to the user's past reading history. For example, it can suggest the optimal reading method based on the reading tone the user has preferred in the past. It can also suggest the optimal reading method by referring to the reading tone the user has used for specific recipients. Furthermore, it can analyze the user's past reading trends and suggest the optimal reading method. This allows for efficient reading by suggesting the optimal reading method based on the user's past reading history.

[0077] The text-to-speech function can read emails while considering the recipient's attributes. Using AI, the text-to-speech function takes the recipient's attributes into account when reading. For example, if the recipient is a superior, the text-to-speech function will read in a formal tone. If the recipient is a colleague, it can read in a casual tone. Furthermore, if the recipient is a customer, it can read in a polite and professional tone. This allows for more appropriate reading by considering the recipient's attributes.

[0078] The text-to-speech unit can estimate the user's emotions and determine reading priorities based on those emotions. Using AI, the unit estimates the user's emotions and determines reading priorities based on those emotions. For example, if the user is in a hurry, the unit will prioritize reading important parts. If the user is relaxed, the unit can also provide detailed information. Furthermore, if the user is stressed, the unit can provide concise and easy-to-understand readings. This allows for reading in a more appropriate order by prioritizing reading based on the user's emotions.

[0079] The text-to-speech function can adjust the level of detail in its reading based on when the email was sent. Using AI, the text-to-speech function adjusts the level of detail based on the email's sending date. For example, in the case of an urgent email, the text-to-speech function will provide a concise and to-the-point reading. For regular report emails, the text-to-speech function can also provide a reading that includes detailed information. Furthermore, for event invitation emails, the text-to-speech function can include detailed event information. By adjusting the level of detail based on when the email was sent, more appropriate readings become possible.

[0080] The text-to-speech unit can improve the accuracy of its reading by referencing relevant literature related to the email during the reading process. The text-to-speech unit uses AI to improve accuracy by referencing relevant literature related to the email during reading. For example, the text-to-speech unit can refer to literature related to the email's content to ensure accurate reading. It can also refer to the latest research findings related to the email's topic and read them aloud. Furthermore, it can refer to industry guidelines related to the email's content and read them aloud. This allows for improved reading accuracy and more precise reading by referencing relevant literature related to the email. === Hard Collateral 1-1 === Each of the multiple elements described above, including the generation unit, adjustment unit, translation unit, reception unit, and reading unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and generates business emails using generation AI. The adjustment unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and adjusts the context of the generated email. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12, and automatically translates the email text. The reception unit is implemented by the reception device 38 of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and accepts voice prompt input. The reading unit is implemented by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and reads the generated email aloud. === Hard Collateral 1-2 === Each of the multiple elements described above, including the generation unit, adjustment unit, translation unit, reception unit, and reading unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and generates business emails using generation AI. The adjustment unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and adjusts the context of the generated email. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12, and automatically translates the email text. The reception unit is implemented by the microphone 238 of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and accepts voice prompt input. The reading unit is implemented by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and reads the generated email aloud. === Hard Collateral 1-3 === Each of the multiple elements described above, including the generation unit, adjustment unit, translation unit, reception unit, and reading unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the processor 46 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and generates business emails using generation AI. The adjustment unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and adjusts the context of the generated email. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12, and automatically translates the email text. The reception unit is implemented by the microphone 238 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and accepts voice prompt input. The reading unit is implemented by the speaker 240 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and reads the generated email aloud. === Hard Collateral 1-4 === Each of the multiple elements described above, including the generation unit, adjustment unit, translation unit, reception unit, and reading unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing unit 12, and generates business emails using generation AI. The adjustment unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, and adjusts the context of the generated email. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12, and automatically translates the email text. The reception unit is implemented by the microphone 238 of the robot 414 or the specific processing unit 290 of the data processing unit 12, and accepts voice prompt input. The reading unit is implemented by the speaker 240 of the robot 414 or the specific processing unit 290 of the data processing unit 12, and reads the generated email aloud.

[0081] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0082] The generation unit can also analyze the user's past email history and select a writing style. For example, the generation unit can analyze the tone and style of emails the user has sent in the past, and the generation AI can generate text based on that. The generation unit can also analyze the wording and expressions the user has used with specific recipients, and the generation AI can reflect that. Furthermore, the generation unit can analyze the formal and casual styles the user has used in the past, and the generation AI can select the appropriate style. As a result, by analyzing the user's past email history, the optimal writing style can be selected, and more consistent emails can be generated.

[0083] The generation unit can also automatically insert industry-specific terminology and phrases during the generation process. For example, if the user belongs to the IT industry, the generation AI will insert industry-specific technical terms into the text. Similarly, if the user belongs to the medical industry, the generation AI can insert medical and specialized terminology. Furthermore, if the user belongs to the financial industry, the generation AI can insert financial terms and industry-specific phrases. This allows for the generation of more professional emails by automatically inserting industry-specific terminology and phrases.

[0084] The adjustment unit can also estimate the user's emotions and determine the priority of adjustments based on those emotions. For example, if the user is stressed, the adjustment unit will prioritize displaying important information and omit unnecessary information. If the user is relaxed, the adjustment unit can also make adjustments that include detailed information. Furthermore, if the user is in a hurry, the adjustment unit can display the most important information first and make adjustments quickly. This allows for more appropriate adjustments by prioritizing adjustments based on the user's emotions.

[0085] The translation unit can also estimate the user's emotions and adjust the tone of the translation based on those emotions. For example, if the user is stressed, the translation unit will use a calm and soothing tone. If the user is happy, the translation unit can use a bright and positive tone. Furthermore, if the user is in a hurry, the translation unit can use a concise and quick tone. By adjusting the tone of the translation based on the user's emotions, a more appropriate tone of translation becomes possible.

[0086] The reception desk can also estimate the user's emotions and adjust the voice input reception method based on the estimated emotions. For example, if the reception desk is nervous, it can accept voice input in a calm voice. If the user is relaxed, it can accept voice input in a cheerful voice. Furthermore, if the user is in a hurry, it can accept quick and concise voice input. By adjusting the voice input reception method based on the user's emotions, more appropriate voice input becomes possible.

[0087] The text-to-speech unit can also estimate the user's emotions and adjust its tone of voice based on those emotions. For example, if the user is nervous, the unit will read in a calm voice. Conversely, if the user is relaxed, it can read in a cheerful voice. Furthermore, if the user is in a hurry, the unit can read quickly and concisely. By adjusting the tone of voice based on the user's emotions, a more appropriate tone of voice can be achieved.

[0088] The adjustment unit can also suggest the optimal correction method by referring to the user's past revision history during the adjustment process. For example, the adjustment unit can use AI to suggest the optimal correction method based on the user's frequently used revision patterns in the past. Furthermore, the adjustment unit can refer to the user's revision history for specific recipients, and the AI ​​can suggest corrections based on that. In addition, the adjustment unit can analyze the user's past revision trends, and the AI ​​can suggest the optimal correction method. This allows for efficient adjustments by suggesting the optimal correction method through reference to the user's past revision history.

[0089] The translation department can also select a translation method that takes into account the context of the email. For example, if the email is business-related, the department will select a formal translation method. If the email is a casual conversation, the department can select a casual translation method. Furthermore, if the email contains technical information, the department can select a method that appropriately translates technical terms. By considering the context of the email, the department can select a more appropriate translation method and achieve a more accurate translation.

[0090] The reception unit can also improve the accuracy of speech recognition by taking into account the user's pronunciation characteristics during voice input. For example, the reception unit can learn the user's pronunciation characteristics to improve the accuracy of speech recognition. Furthermore, if the user has a specific accent, the reception unit can perform speech recognition corresponding to that accent. In addition, the reception unit can improve the accuracy of speech recognition by taking into account the user's pronunciation habits. As a result, by considering the user's pronunciation characteristics, the accuracy of speech recognition is improved, enabling more accurate voice input.

[0091] The text-to-speech function can also adjust the level of detail in its reading based on when the email was sent. For example, for urgent emails, the reading function will provide a concise and to-the-point reading. For regular report emails, it can provide a reading that includes detailed information. Furthermore, for event invitation emails, it can provide a reading that includes detailed event information. By adjusting the level of detail in the reading based on when the email was sent, more appropriate readings can be achieved.

[0092] The following briefly describes the processing flow for example form 2.

[0093] Step 1: The generation unit generates business emails. The generation unit uses generation AI to understand the context and generate appropriate text. For example, if the user enters "Send a meeting reminder," the generation AI will generate an appropriate reminder email. The generation unit also understands the context so that the generation AI can generate text that aligns with the user's intent. For example, if it receives a prompt such as "Summarize the main points of this text," it will extract the main points and create a summary. Step 2: The adjustment unit adjusts the context of the generated email. The adjustment unit modifies parts of the generated email or inserts additional information. For example, it can correct the grammar of the generated email or add relevant data or supplementary explanations. Step 3: The translation unit automatically translates emails if the sender's language differs from the recipient's language. For example, an email written in Japanese can be translated into English, allowing the sender to review the content. Step 4: The reception desk accepts voice prompts for visually impaired users. For example, if a visually impaired user voice-inputs "Send a meeting reminder," the generation AI will generate an appropriate reminder email. Step 5: The reading section reads the email aloud after it has been created for review. For example, the generated email can be read aloud, allowing visually impaired individuals to check its contents.

[0094] 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.

[0095] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0096] 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.

[0097] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0098] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.).

[0110] 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.

[0111] 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. 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.

[0112] 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.

[0113] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0114] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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. 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.

[0128] 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.

[0129] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0130] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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. 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.

[0145] 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.

[0146] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 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 generation unit that generates business emails, An adjustment unit adjusts the context of the email generated by the generation unit, A translation unit that translates the email adjusted by the adjustment unit, A reception desk that accepts voice input, The system includes a reading unit that reads out emails based on voice input received by the reception unit. A system characterized by the following features.

2. The generating unit is Generative AI understands context and generates text. The system according to feature 1.

3. The adjustment unit is, Modify parts of the generated email and insert additional information. The system according to feature 1.

4. The aforementioned translation department, If the sender's language and the recipient's language are different, the email text will be automatically translated. The system according to feature 1.

5. The aforementioned reception unit is Accepts voice prompts from visually impaired users. The system according to feature 1.

6. The aforementioned reading unit, After creation is complete, read the email aloud to check it. The system according to feature 1.

7. The generating unit is It estimates the user's emotions and adjusts the tone of the generated text based on those estimated emotions. The system according to feature 1.

8. The generating unit is Analyze the user's past email history and select a writing style. The system according to feature 1.

9. The generating unit is During generation, the system automatically inserts industry-specific terminology and phrases used by the user. The system according to feature 1.

10. The adjustment unit is, It estimates the user's emotions and determines the priority of adjustments based on those estimated emotions. The system according to feature 1.

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