system

Abstract

The system according to this embodiment aims to simplify the process from letter creation to delivery, enabling even beginners to write letters with confidence. [Solution] The system according to the embodiment comprises an advice unit, a selection unit, a printing unit, and a guide. The advice unit provides real-time advice on writing and suggests wording. The selection unit selects the most suitable letter set from the postal service's inventory according to the user's content. The printing unit prints the completed wording onto the specified letter set and automates the delivery procedure. The guide provides comprehensive support from the beginning of writing the letter to guidance on its content.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 process from creating a letter to delivering it is laborious and it is difficult for beginners to write a letter.

[0005] The system according to the embodiment aims to facilitate the process from creating a letter to delivering it and enable beginners to write a letter with confidence.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an advice unit, a selection unit, a printing unit, and a guide. The advice unit provides real-time advice and text suggestions for writing. The selection unit selects the most suitable letter set from the postal service's inventory according to the user's content. The printing unit prints the completed text onto the specified letter set and automates the delivery process. The guide provides comprehensive support from the beginning of writing the letter to content guidance. [Effects of the Invention]

[0007] The system according to this embodiment simplifies the process from letter creation to delivery, allowing even beginners to write letters with confidence. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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 letter-writing support system according to an embodiment of the present invention simplifies the letter-writing process and is a system that even beginners can use with confidence. This letter-writing support system is equipped with a digital pen and an advisory function, providing real-time advice on writing and suggesting wording. The letter-writing support system also provides a function to select the most suitable letter set from the postal service's inventory according to the user's content. Furthermore, the letter-writing support system has a function to print the completed wording on the specified letter set and automate the delivery procedure. Finally, the letter-writing support system provides a beginner-friendly guidance function that comprehensively supports everything from the beginning of writing the letter to guidance on content. For example, the letter-writing support system provides real-time advice using a digital pen when the user starts writing a letter. For example, the letter-writing support system suggests appropriate wording based on what the user has written. Furthermore, the letter-writing support system selects the most suitable letter set from the postal service's inventory according to the user's content. For example, the letter-writing support system suggests the most suitable letter set based on the user's preferences and theme. Next, the letter-writing support system prints the completed wording on the specified letter set and automates the delivery procedure. For example, the letter-writing support system automatically places printed letters into envelopes and handles the delivery process. Finally, the system provides comprehensive support, from writing the opening lines to guiding the content. For instance, it offers advice on letter-writing techniques and content for beginners. This makes letter-writing easier, allowing even beginners to use it with confidence. Furthermore, the system allows users to express their individuality by selecting stationery sets that align with a consistent theme, and its seamless integration from creation to delivery enhances convenience. This makes letter-writing easier, ensuring that even beginners can use it with confidence.

[0029] The letter-writing support system according to this embodiment comprises an advice unit, a selection unit, a printing unit, and a finger guide. The advice unit provides real-time advice and text suggestions for writing. For example, the advice unit suggests appropriate text while the user is writing a letter. The advice unit can also adjust the tone and style of the text based on what the user has written. For example, the advice unit can provide advice on how to start writing when the user has just begun writing. For example, the advice unit can provide advice on how to develop the content when the user is in the middle of writing. For example, the advice unit can provide advice on how to conclude when the user is nearing the end of writing. The selection unit selects the most suitable letter set from the postal service's inventory that matches the user's content. For example, the selection unit suggests the most suitable letter set based on the user's preferences and themes. For example, the selection unit can analyze the user's past letter set selection history and suggest the most suitable letter set. For example, the selection unit can suggest a letter set based on the user's current projects or areas of interest. The selection unit can, for example, estimate the user's emotions and suggest a letter set design based on those emotions. The printing unit prints the completed text onto the specified letter set and automates the delivery process. The printing unit can, for example, automatically place the printed letter into an envelope and handle the delivery process. The printing unit can, for example, estimate the user's emotions and adjust the font and layout of the print based on those emotions. The printing unit can, for example, analyze the user's past printing history and select the optimal printing settings. The printing unit can, for example, customize the printed content based on the user's current projects or areas of interest. The guidance unit provides comprehensive support, from writing the letter to providing content guidance. The guidance unit can, for example, provide advice on how to write and what to write for beginners. The guidance unit can, for example, estimate the user's emotions and adjust the content and method of guidance based on those emotions. The guidance unit can, for example, refer to the user's past letter-writing history to provide optimal guidance.The guidance can also be customized based on, for example, the user's current projects or areas of interest. This makes the letter-writing support system according to the embodiment easier to use and more user-friendly, even for beginners.

[0030] The advice section provides real-time advice and suggestions for wording as the user writes. Specifically, it has a function to suggest appropriate wording while the user is writing a letter. For example, it can provide advice on how to start the letter when the user has just begun writing. This helps the user to start writing smoothly by suggesting appropriate words and phrases when they are unsure how to begin. It can also provide advice on how to develop the content when the user is in the middle of writing. This ensures that the content of the letter is consistent and easy for the reader to understand. Furthermore, it can provide advice on how to conclude the letter when the user is nearing the end of writing. This ensures that the letter ends naturally and leaves a good impression on the reader. The advice section can also adjust the tone and style of the wording based on what the user has written. For example, when writing a formal letter, it will suggest appropriate honorifics and polite expressions, and when writing a casual letter, it will suggest friendly language and phrases. This allows the user to create appropriate wording according to the purpose of the letter and the recipient. The advice section uses AI to analyze the user's writing and provide appropriate advice in real time. The AI ​​uses natural language processing technology to understand what the user is writing and generates context-appropriate advice. This allows the user to complete their letter smoothly without getting lost during the writing process.

[0031] The selection function chooses the most suitable letter set from the postal service's inventory based on the user's content. Specifically, it has a function to suggest the most suitable letter set based on the user's preferences and themes. For example, if the user is writing a thank-you letter, it will suggest a letter set with a design that expresses gratitude. It can also analyze the user's past letter set selection history and suggest the most suitable letter set. This makes it easy for users to choose a letter set that suits their preferences. Furthermore, the selection function can also suggest letter sets based on the user's current projects and areas of interest. For example, if the user is writing a letter related to a specific event or project, it will suggest a letter set that matches that theme. The selection function can also use AI to estimate the user's emotions and suggest letter set designs based on those estimated emotions. For example, if the user is writing a joyful letter, it will suggest a bright and cheerful letter set, and if they are writing a sad letter, it will suggest a calm letter set. This allows users to choose a letter set that matches the content and emotions of their letter. By comprehensively analyzing the user's selection history and current situation and suggesting the most suitable letter set, the selection function can streamline the letter-writing process.

[0032] The printing unit prints the completed text onto a specified letter set and automates the delivery process. Specifically, it has the function of printing the text of a letter created by the user onto a selected letter set. For example, the printing unit can automatically place the printed letter into an envelope and handle the delivery process. This eliminates the user's need to print and enclose the letter themselves. Furthermore, the printing unit can estimate the user's emotions and adjust the font and layout of the print based on those emotions. For example, it might use a warm font and layout for a thank-you letter and a formal font and layout for a business letter. This ensures that the print is appropriate for the content and purpose of the letter. The printing unit can also analyze the user's past printing history and select the optimal printing settings. For example, it can refer to fonts and layouts used by the user in the past and apply similar settings to the current letter. This allows the user to create consistent letters. In addition, the printing unit can customize the printed content based on the user's current projects and areas of interest. For example, for letters related to a specific event or project, the system uses fonts and layouts appropriate to the theme. The printing unit utilizes AI to analyze the user's writing content and emotions, automatically selecting the optimal printing settings. This eliminates the need for users to manually configure printing settings, allowing for a more efficient letter-writing process.

[0033] The Guide provides comprehensive support, from writing the opening of a letter to guiding the content. Specifically, it offers advice on letter writing and content for beginners. For example, it provides detailed guidance on how to start a letter, appropriate expressions, and structure the text. This allows even users unfamiliar with letter writing to write with confidence. The Guide can also estimate the user's emotions and adjust the content and method of guidance based on those emotions. For example, when writing a thank-you letter, it suggests appropriate expressions and phrases to convey gratitude, and when writing an apology letter, it suggests appropriate expressions and phrases to convey apologies. This allows users to receive appropriate guidance according to the content and purpose of their letter. The Guide can also refer to the user's past letter-writing history to provide optimal guidance. For example, it suggests a similar style and expression for the current letter based on the content and style of previously written letters. This allows users to create consistent letters. Furthermore, the Guide can customize the guidance content based on the user's current projects and areas of interest. For example, for letters related to a specific event or project, it suggests content and expressions that match that theme. The "Shinanbu" system utilizes AI to analyze the user's writing content and emotions, providing optimal guidance in real time. This allows users to complete their letters smoothly without getting lost during the writing process.

[0034] The advice unit can provide real-time advice and text suggestions for writing. For example, it can suggest appropriate wording while the user is writing a letter. The advice unit can also adjust the tone and style of the text based on what the user has written. For example, it can provide advice on how to start the letter when the user has just begun writing. For example, it can provide advice on how to develop the content when the user is halfway through writing. For example, it can provide advice on how to conclude the letter when the user is nearing the end of writing. This makes letter writing smoother through real-time advice and text suggestions. Some or all of the above processes in the advice unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the advice unit can input what the user has written into a generative AI, which can then suggest appropriate wording.

[0035] The selection unit can choose the most suitable letter set from the postal service's inventory based on the user's content. For example, the selection unit can suggest the most suitable letter set based on the user's preferences or themes. The selection unit can also suggest the most suitable letter set by analyzing the user's past letter set selection history. For example, the selection unit can suggest a letter set based on the user's current projects or areas of interest. For example, the selection unit can estimate the user's emotions and suggest a letter set design based on those emotions. This allows users to express their individuality by selecting a letter set that suits their content. Some or all of the above processes in the selection unit may be performed using AI, for example, or not. For example, the selection unit can input the user's content into AI, which can then suggest the most suitable letter set.

[0036] The printing unit can print the completed text onto a specified letter set and automate the delivery process. For example, the printing unit can automatically place the printed letter into an envelope and handle the delivery process. The printing unit can also estimate the user's emotions and adjust the font and layout of the print based on the estimated emotions. For example, the printing unit can analyze the user's past printing history and select the optimal printing settings. For example, the printing unit can customize the printed content based on the user's current projects or areas of interest. This automates the printing and delivery process, saving time and effort. Some or all of the above processes in the printing unit may be performed using AI, for example, or not. For example, the printing unit can input the user's emotions into the AI, which can then suggest appropriate fonts and layouts.

[0037] The guidance system can provide comprehensive support, from writing the opening of a letter to providing guidance on its content. For example, it can offer advice on letter writing and content for beginners. The guidance system can also estimate the user's emotions and adjust the content and method of guidance based on those emotions. For example, it can refer to the user's past letter-writing history to provide optimal guidance. The guidance system can also customize the guidance content based on the user's current projects and areas of interest. This allows even beginners to use the system with confidence, as it supports them from writing the opening of a letter to providing guidance on its content. Some or all of the processes described above in the guidance system may be performed using AI, for example, or not. For example, the guidance system can input the user's emotions into AI, which can then provide appropriate advice.

[0038] The advice unit can analyze the user's past letter-writing history and suggest the most suitable wording. For example, the advice unit can analyze the content of letters the user has written in the past and suggest similar wording. The advice unit can also suggest wording by referring to expressions and phrases the user has used in the past. For example, the advice unit can analyze the reactions of recipients of letters the user has sent in the past and suggest the most suitable wording. In this way, by analyzing the user's past letter-writing history, the advice unit can suggest the most suitable wording for the user. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the user's past letter-writing history into AI, and the AI ​​can suggest the most suitable wording.

[0039] The advice section can provide advice at the appropriate time according to the progress of the writing. For example, the advice section can provide advice on the beginning of the writing when the user has just started writing. For example, the advice section can also provide advice on the development of the content when the user is in the middle of writing. For example, the advice section can also provide advice on concluding when the user is nearing the end of writing. This makes letter writing smoother by providing advice according to the progress of the writing. Some or all of the above processes in the advice section may be performed using AI, for example, or not using AI. For example, the advice section can input the user's writing progress into the AI, and the AI ​​can provide advice at the appropriate time.

[0040] The advice section can suggest region-specific expressions when a user is writing, taking into account their geographical location. For example, if the user is in the Kansai region, the advice section can suggest text incorporating the Kansai dialect. If the user is in Hokkaido, the advice section can also suggest text incorporating expressions specific to Hokkaido. If the user is in Okinawa, the advice section can also suggest text incorporating expressions specific to Okinawa. By suggesting expressions that take geographical location into account, it is possible to create letters appropriate for the region. Some or all of the above processing in the advice section may be performed using AI, for example, or without AI. For example, the advice section can input the user's geographical location information into the AI, which can then suggest region-specific expressions.

[0041] The advice unit can analyze the user's social media activity when making a post and suggest relevant text. For example, the advice unit can suggest text by referring to content the user has recently shared on social media. The advice unit can also suggest text by referring to expressions and phrases the user has used on social media. The advice unit can also analyze the content of the user's social media interactions and suggest the most suitable text. In this way, by analyzing social media activity, it can suggest text relevant to the user. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the user's social media activity into AI, and the AI ​​can suggest relevant text.

[0042] The selection unit can analyze the user's past letter set selection history and suggest the most suitable letter set. For example, the selection unit can suggest a letter set with a similar design by referencing the design of a letter set previously selected by the user. For example, the selection unit can suggest a letter set with a related theme by referencing the theme of a letter set previously selected by the user. For example, the selection unit can suggest a letter set with the same color scheme by referencing the color scheme of a letter set previously selected by the user. In this way, by analyzing the user's past letter set selection history, the system can suggest the most suitable letter set for the user. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's past letter set selection history into AI, which can then suggest the most suitable letter set.

[0043] The selection unit can filter letter sets based on the user's current projects and areas of interest. For example, the selection unit can suggest letter sets with designs related to the user's current projects. The selection unit can also suggest letter sets on relevant themes based on the user's areas of interest. The selection unit can also suggest the most suitable letter set based on topics the user has recently become interested in. This allows the user to select the most suitable letter set by suggesting letter sets based on their current projects and areas of interest. Some or all of the above processing in the selection unit may be performed using AI, for example, or not. For example, the selection unit can input the user's current projects and areas of interest into an AI, which can then suggest the most suitable letter set.

[0044] The selection unit can prioritize suggesting highly relevant letter sets by considering the user's geographical location when selecting a letter set. For example, if the user is in the Kansai region, the selection unit will suggest a letter set incorporating Kansai region designs. If the user is in Hokkaido, the selection unit can also suggest a letter set incorporating Hokkaido designs. If the user is in Okinawa, the selection unit can also suggest a letter set incorporating Okinawa designs. By suggesting letter sets that take geographical location into consideration, the user can select a letter set appropriate for their region. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's geographical location information into AI, which can then suggest highly relevant letter sets.

[0045] The selection unit can analyze the user's social media activity when selecting a letter set and suggest a relevant letter set. For example, the selection unit can suggest a letter set based on content the user has recently shared on social media. The selection unit can also suggest a letter set based on expressions and phrases the user has used on social media. For example, the selection unit can analyze the user's social media interactions and suggest the most suitable letter set. In this way, by analyzing social media activity, it is possible to suggest a letter set relevant to the user. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's social media activity into AI, and the AI ​​can suggest a relevant letter set.

[0046] The printing unit can analyze the user's past printing history and select the optimal printing settings during printing. For example, the printing unit can suggest printing settings by referring to fonts and layouts previously used by the user. For example, the printing unit can also suggest optimal printing settings by referring to the content of documents previously printed by the user. For example, the printing unit can suggest optimal printing settings by analyzing the reactions of recipients of letters previously printed by the user. In this way, by analyzing past printing history, the printing unit can suggest the optimal printing settings for the user. Some or all of the above processes in the printing unit may be performed using AI, for example, or without AI. For example, the printing unit can input the user's past printing history into AI, and the AI ​​can suggest the optimal printing settings.

[0047] The printing unit can customize the printed content based on the user's current projects and areas of interest during printing. For example, the printing unit can print content related to the project the user is currently working on. The printing unit can also print relevant content based on the user's areas of interest. The printing unit can also customize the printed content based on topics the user has recently become interested in. This allows the user to receive optimal printed content by suggesting content based on their current projects and areas of interest. Some or all of the above processing in the printing unit may be performed using AI, for example, or without AI. For example, the printing unit can input the user's current projects and areas of interest into the AI, which can then suggest optimal printed content.

[0048] The printing unit can select the optimal printing settings while considering the user's geographical location information. For example, if the user is in the Kansai region, the printing unit will print text incorporating the Kansai dialect. For example, if the user is in Hokkaido, the printing unit can also print text incorporating expressions specific to Hokkaido. For example, if the user is in Okinawa, the printing unit can also print text incorporating expressions specific to Okinawa. In this way, by suggesting printing settings that take geographical location information into account, letters appropriate for the region can be created. Some or all of the above processing in the printing unit may be performed using AI, for example, or without AI. For example, the printing unit can input the user's geographical location information into the AI, and the AI ​​can suggest the optimal printing settings.

[0049] The printing unit can analyze the user's social media activity during printing and suggest relevant print content. For example, the printing unit can suggest print content by referring to content the user has recently shared on social media. The printing unit can also suggest print content by referring to expressions and phrases the user has used on social media. For example, the printing unit can analyze the user's social media interactions and suggest the most suitable print content. In this way, by analyzing social media activity, it is possible to suggest print content relevant to the user. Some or all of the above processing in the printing unit may be performed using AI, for example, or without AI. For example, the printing unit can input the user's social media activity into AI, and the AI ​​can suggest relevant print content.

[0050] The guidance system can provide optimal guidance by referring to the user's past letter-writing history. For example, the guidance system can analyze the content of letters the user has written in the past and provide guidance with similar content. For example, the guidance system can also provide guidance by referring to expressions and phrases the user has used in the past. For example, the guidance system can analyze the reactions of recipients of letters the user has sent in the past and provide optimal guidance. In this way, the guidance system can provide the user with the most suitable guidance by referring to their past letter-writing history. Some or all of the above processes in the guidance system may be performed using AI, for example, or not using AI. For example, the guidance system can input the user's past letter-writing history into AI, and the AI ​​can provide optimal guidance.

[0051] The guidance system can customize its guidance content based on the user's current projects and areas of interest. For example, it can provide guidance related to the project the user is currently working on. It can also provide guidance related to the user's areas of interest. It can also customize its guidance content based on topics the user has recently become interested in. This allows the system to provide the user with the most suitable guidance by suggesting guidance based on their current projects and areas of interest. Some or all of the above processes in the guidance system may be performed using AI, for example, or not. For example, the guidance system can input the user's current projects and areas of interest into an AI, which can then suggest the most suitable guidance.

[0052] The guidance system can provide optimal guidance by taking into account the user's geographical location. For example, if the user is in the Kansai region, the guidance system will incorporate the Kansai dialect. If the user is in Hokkaido, the guidance system can also incorporate expressions specific to Hokkaido. If the user is in Okinawa, the guidance system can also incorporate expressions specific to Okinawa. By proposing guidance that takes geographical location into account, the guidance system can provide guidance appropriate to the region. Some or all of the above processing in the guidance system may be performed using AI, for example, or without AI. For example, the guidance system can input the user's geographical location information into AI, which can then propose the optimal guidance.

[0053] The guidance system can analyze a user's social media activity and suggest relevant guidance content during the guidance process. For example, the guidance system can suggest guidance content based on what the user has recently shared on social media. The guidance system can also suggest guidance content based on expressions and phrases used by the user on social media. The guidance system can also analyze the user's social media interactions and suggest the most appropriate guidance content. In this way, by analyzing social media activity, the guidance system can suggest guidance content relevant to the user. Some or all of the above processing in the guidance system may be performed using AI, for example, or without AI. For example, the guidance system can input the user's social media activity into AI, which can then suggest relevant guidance content.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The advice section can analyze a user's past letter-writing history and suggest the most suitable wording. For example, it can analyze the content of letters the user has written in the past and suggest similar wording. It can also suggest wording based on expressions and phrases the user has used in the past. It can analyze the responses of recipients to letters the user has sent in the past and suggest the most suitable wording. In this way, by analyzing the user's past letter-writing history, it can suggest the most suitable wording for the user.

[0056] The selection section can analyze the user's past letter set selection history and suggest the most suitable letter set. For example, it can suggest letter sets with similar designs by referencing the designs of letter sets the user has selected in the past. It can suggest letter sets with related themes by referencing the themes of letter sets the user has selected in the past. It can suggest letter sets with the same color scheme by referencing the color scheme of letter sets the user has selected in the past. In this way, by analyzing the user's past letter set selection history, the system can suggest the most suitable letter set for them.

[0057] The printing unit can analyze the user's past printing history and select the optimal printing settings during printing. For example, it can suggest printing settings based on fonts and layouts the user has used in the past. It can also suggest optimal printing settings based on the content of documents the user has printed in the past. Furthermore, it can suggest optimal printing settings by analyzing the recipient's reactions to letters the user has printed in the past. In this way, by analyzing past printing history, the system can suggest the most suitable printing settings for the user.

[0058] The guidance system can provide optimal guidance by referencing the user's past letter-writing history. For example, it can analyze the content of letters the user has written in the past and provide guidance with similar content. It can also provide guidance by referring to expressions and phrases the user has used in the past. It can analyze the responses of recipients to letters the user has sent in the past and provide optimal guidance. In this way, by referring to the user's past letter-writing history, it can provide the most suitable guidance.

[0059] The advice section can provide advice at the appropriate time, depending on the progress of the writing. For example, it can provide advice on the opening when the user has just started writing. It can provide advice on the development of the content when the user is in the middle of writing. It can provide advice on concluding when the user is nearing the end of writing. In this way, by providing advice according to the progress of the writing, the letter writing process becomes smoother.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The advice section provides real-time advice and suggestions for the writing process. While the user is writing the letter, it suggests appropriate wording and adjusts the tone and style. It provides advice on the opening at the beginning, advice on developing the content in the middle, and advice on concluding as the user nears the end. Step 2: The selection section chooses the most suitable letter set from the postal service's inventory based on the user's content. It suggests the best letter set based on the user's preferences and themes, past letter set selection history, current projects and areas of interest, and emotions. Step 3: The printing unit prints the completed text onto the specified letter set and automates the delivery process. It automatically places the printed letters into envelopes and handles the delivery. It adjusts the font and layout of the print based on the user's sentiment and analyzes past printing history to select the optimal printing settings. It customizes the printed content based on the current project and areas of interest. Step 4: Guide Nanbu provides comprehensive support, from writing the opening of the letter to guiding you through its content. It offers advice on letter writing and content for beginners, and adjusts the content and methods of the guidance based on the user's feelings. It provides optimal guidance by referring to past letter writing history and customizes the guidance content based on the current project and areas of interest.

[0062] (Example of form 2) The letter-writing support system according to an embodiment of the present invention simplifies the letter-writing process and is a system that even beginners can use with confidence. This letter-writing support system is equipped with a digital pen and an advisory function, providing real-time advice on writing and suggesting wording. The letter-writing support system also provides a function to select the most suitable letter set from the postal service's inventory according to the user's content. Furthermore, the letter-writing support system has a function to print the completed wording on the specified letter set and automate the delivery procedure. Finally, the letter-writing support system provides a beginner-friendly guidance function that comprehensively supports everything from the beginning of writing the letter to guidance on content. For example, the letter-writing support system provides real-time advice using a digital pen when the user starts writing a letter. For example, the letter-writing support system suggests appropriate wording based on what the user has written. Furthermore, the letter-writing support system selects the most suitable letter set from the postal service's inventory according to the user's content. For example, the letter-writing support system suggests the most suitable letter set based on the user's preferences and theme. Next, the letter-writing support system prints the completed wording on the specified letter set and automates the delivery procedure. For example, the letter-writing support system automatically places printed letters into envelopes and handles the delivery process. Finally, the system provides comprehensive support, from writing the opening lines to guiding the content. For instance, it offers advice on letter-writing techniques and content for beginners. This makes letter-writing easier, allowing even beginners to use it with confidence. Furthermore, the system allows users to express their individuality by selecting stationery sets that align with a consistent theme, and its seamless integration from creation to delivery enhances convenience. This makes letter-writing easier, ensuring that even beginners can use it with confidence.

[0063] The letter-writing support system according to this embodiment comprises an advice unit, a selection unit, a printing unit, and a finger guide. The advice unit provides real-time advice and text suggestions for writing. For example, the advice unit suggests appropriate text while the user is writing a letter. The advice unit can also adjust the tone and style of the text based on what the user has written. For example, the advice unit can provide advice on how to start writing when the user has just begun writing. For example, the advice unit can provide advice on how to develop the content when the user is in the middle of writing. For example, the advice unit can provide advice on how to conclude when the user is nearing the end of writing. The selection unit selects the most suitable letter set from the postal service's inventory that matches the user's content. For example, the selection unit suggests the most suitable letter set based on the user's preferences and themes. For example, the selection unit can analyze the user's past letter set selection history and suggest the most suitable letter set. For example, the selection unit can suggest a letter set based on the user's current projects or areas of interest. The selection unit can, for example, estimate the user's emotions and suggest a letter set design based on those emotions. The printing unit prints the completed text onto the specified letter set and automates the delivery process. The printing unit can, for example, automatically place the printed letter into an envelope and handle the delivery process. The printing unit can, for example, estimate the user's emotions and adjust the font and layout of the print based on those emotions. The printing unit can, for example, analyze the user's past printing history and select the optimal printing settings. The printing unit can, for example, customize the printed content based on the user's current projects or areas of interest. The guidance unit provides comprehensive support, from writing the letter to providing content guidance. The guidance unit can, for example, provide advice on how to write and what to write for beginners. The guidance unit can, for example, estimate the user's emotions and adjust the content and method of guidance based on those emotions. The guidance unit can, for example, refer to the user's past letter-writing history to provide optimal guidance.The guidance can also be customized based on, for example, the user's current projects or areas of interest. This makes the letter-writing support system according to the embodiment easier to use and more user-friendly, even for beginners.

[0064] The advice section provides real-time advice and suggestions for wording as the user writes. Specifically, it has a function to suggest appropriate wording while the user is writing a letter. For example, it can provide advice on how to start the letter when the user has just begun writing. This helps the user to start writing smoothly by suggesting appropriate words and phrases when they are unsure how to begin. It can also provide advice on how to develop the content when the user is in the middle of writing. This ensures that the content of the letter is consistent and easy for the reader to understand. Furthermore, it can provide advice on how to conclude the letter when the user is nearing the end of writing. This ensures that the letter ends naturally and leaves a good impression on the reader. The advice section can also adjust the tone and style of the wording based on what the user has written. For example, when writing a formal letter, it will suggest appropriate honorifics and polite expressions, and when writing a casual letter, it will suggest friendly language and phrases. This allows the user to create appropriate wording according to the purpose of the letter and the recipient. The advice section uses AI to analyze the user's writing and provide appropriate advice in real time. The AI ​​uses natural language processing technology to understand what the user is writing and generates context-appropriate advice. This allows the user to complete their letter smoothly without getting lost during the writing process.

[0065] The selection function chooses the most suitable letter set from the postal service's inventory based on the user's content. Specifically, it has a function to suggest the most suitable letter set based on the user's preferences and themes. For example, if the user is writing a thank-you letter, it will suggest a letter set with a design that expresses gratitude. It can also analyze the user's past letter set selection history and suggest the most suitable letter set. This makes it easy for users to choose a letter set that suits their preferences. Furthermore, the selection function can also suggest letter sets based on the user's current projects and areas of interest. For example, if the user is writing a letter related to a specific event or project, it will suggest a letter set that matches that theme. The selection function can also use AI to estimate the user's emotions and suggest letter set designs based on those estimated emotions. For example, if the user is writing a joyful letter, it will suggest a bright and cheerful letter set, and if they are writing a sad letter, it will suggest a calm letter set. This allows users to choose a letter set that matches the content and emotions of their letter. By comprehensively analyzing the user's selection history and current situation and suggesting the most suitable letter set, the selection function can streamline the letter-writing process.

[0066] The printing unit prints the completed text onto a specified letter set and automates the delivery process. Specifically, it has the function of printing the text of a letter created by the user onto a selected letter set. For example, the printing unit can automatically place the printed letter into an envelope and handle the delivery process. This eliminates the user's need to print and enclose the letter themselves. Furthermore, the printing unit can estimate the user's emotions and adjust the font and layout of the print based on those emotions. For example, it might use a warm font and layout for a thank-you letter and a formal font and layout for a business letter. This ensures that the print is appropriate for the content and purpose of the letter. The printing unit can also analyze the user's past printing history and select the optimal printing settings. For example, it can refer to fonts and layouts used by the user in the past and apply similar settings to the current letter. This allows the user to create consistent letters. In addition, the printing unit can customize the printed content based on the user's current projects and areas of interest. For example, for letters related to a specific event or project, the system uses fonts and layouts appropriate to the theme. The printing unit utilizes AI to analyze the user's writing content and emotions, automatically selecting the optimal printing settings. This eliminates the need for users to manually configure printing settings, allowing for a more efficient letter-writing process.

[0067] The Guide provides comprehensive support, from writing the opening of a letter to guiding the content. Specifically, it offers advice on letter writing and content for beginners. For example, it provides detailed guidance on how to start a letter, appropriate expressions, and structure the text. This allows even users unfamiliar with letter writing to write with confidence. The Guide can also estimate the user's emotions and adjust the content and method of guidance based on those emotions. For example, when writing a thank-you letter, it suggests appropriate expressions and phrases to convey gratitude, and when writing an apology letter, it suggests appropriate expressions and phrases to convey apologies. This allows users to receive appropriate guidance according to the content and purpose of their letter. The Guide can also refer to the user's past letter-writing history to provide optimal guidance. For example, it suggests a similar style and expression for the current letter based on the content and style of previously written letters. This allows users to create consistent letters. Furthermore, the Guide can customize the guidance content based on the user's current projects and areas of interest. For example, for letters related to a specific event or project, it suggests content and expressions that match that theme. The "Shinanbu" system utilizes AI to analyze the user's writing content and emotions, providing optimal guidance in real time. This allows users to complete their letters smoothly without getting lost during the writing process.

[0068] The advice unit can provide real-time advice and text suggestions for writing. For example, it can suggest appropriate wording while the user is writing a letter. The advice unit can also adjust the tone and style of the text based on what the user has written. For example, it can provide advice on how to start the letter when the user has just begun writing. For example, it can provide advice on how to develop the content when the user is halfway through writing. For example, it can provide advice on how to conclude the letter when the user is nearing the end of writing. This makes letter writing smoother through real-time advice and text suggestions. Some or all of the above processes in the advice unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the advice unit can input what the user has written into a generative AI, which can then suggest appropriate wording.

[0069] The selection unit can choose the most suitable letter set from the postal service's inventory based on the user's content. For example, the selection unit can suggest the most suitable letter set based on the user's preferences or themes. The selection unit can also suggest the most suitable letter set by analyzing the user's past letter set selection history. For example, the selection unit can suggest a letter set based on the user's current projects or areas of interest. For example, the selection unit can estimate the user's emotions and suggest a letter set design based on those emotions. This allows users to express their individuality by selecting a letter set that suits their content. Some or all of the above processes in the selection unit may be performed using AI, for example, or not. For example, the selection unit can input the user's content into AI, which can then suggest the most suitable letter set.

[0070] The printing unit can print the completed text onto a specified letter set and automate the delivery process. For example, the printing unit can automatically place the printed letter into an envelope and handle the delivery process. The printing unit can also estimate the user's emotions and adjust the font and layout of the print based on the estimated emotions. For example, the printing unit can analyze the user's past printing history and select the optimal printing settings. For example, the printing unit can customize the printed content based on the user's current projects or areas of interest. This automates the printing and delivery process, saving time and effort. Some or all of the above processes in the printing unit may be performed using AI, for example, or not. For example, the printing unit can input the user's emotions into the AI, which can then suggest appropriate fonts and layouts.

[0071] The guidance system can provide comprehensive support, from writing the opening of a letter to providing guidance on its content. For example, it can offer advice on letter writing and content for beginners. The guidance system can also estimate the user's emotions and adjust the content and method of guidance based on those emotions. For example, it can refer to the user's past letter-writing history to provide optimal guidance. The guidance system can also customize the guidance content based on the user's current projects and areas of interest. This allows even beginners to use the system with confidence, as it supports them from writing the opening of a letter to providing guidance on its content. Some or all of the processes described above in the guidance system may be performed using AI, for example, or not. For example, the guidance system can input the user's emotions into AI, which can then provide appropriate advice.

[0072] The advice unit can estimate the user's emotions and adjust the tone and style of the text based on the estimated emotions. For example, if the user is sad, the advice unit can suggest words of comfort and a gentle tone. For example, if the user is happy, the advice unit can suggest words of congratulations and a cheerful tone. For example, if the user is angry, the advice unit can suggest calm words and a calm tone. This allows for the creation of more appropriate letters by suggesting a tone and style of text that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not using AI. For example, the advice unit can input the user's emotions into an AI, and the AI ​​can suggest an appropriate tone and style of text.

[0073] The advice unit can analyze the user's past letter-writing history and suggest the most suitable wording. For example, the advice unit can analyze the content of letters the user has written in the past and suggest similar wording. The advice unit can also suggest wording by referring to expressions and phrases the user has used in the past. For example, the advice unit can analyze the reactions of recipients of letters the user has sent in the past and suggest the most suitable wording. In this way, by analyzing the user's past letter-writing history, the advice unit can suggest the most suitable wording for the user. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the user's past letter-writing history into AI, and the AI ​​can suggest the most suitable wording.

[0074] The advice section can provide advice at the appropriate time according to the progress of the writing. For example, the advice section can provide advice on the beginning of the writing when the user has just started writing. For example, the advice section can also provide advice on the development of the content when the user is in the middle of writing. For example, the advice section can also provide advice on concluding when the user is nearing the end of writing. This makes letter writing smoother by providing advice according to the progress of the writing. Some or all of the above processes in the advice section may be performed using AI, for example, or not using AI. For example, the advice section can input the user's writing progress into the AI, and the AI ​​can provide advice at the appropriate time.

[0075] The advice unit can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, if the user is sad, the advice unit may prioritize offering words of comfort. For example, if the user is happy, the advice unit may prioritize offering words of congratulations. For example, if the user is angry, the advice unit may prioritize offering calming words. By determining the priority of advice according to the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI, or not using AI. For example, the advice unit can input the user's emotions into an AI, and the AI ​​can determine the priority of appropriate advice.

[0076] The advice section can suggest region-specific expressions when a user is writing, taking into account their geographical location. For example, if the user is in the Kansai region, the advice section can suggest text incorporating the Kansai dialect. If the user is in Hokkaido, the advice section can also suggest text incorporating expressions specific to Hokkaido. If the user is in Okinawa, the advice section can also suggest text incorporating expressions specific to Okinawa. By suggesting expressions that take geographical location into account, it is possible to create letters appropriate for the region. Some or all of the above processing in the advice section may be performed using AI, for example, or without AI. For example, the advice section can input the user's geographical location information into the AI, which can then suggest region-specific expressions.

[0077] The advice unit can analyze the user's social media activity when making a post and suggest relevant text. For example, the advice unit can suggest text by referring to content the user has recently shared on social media. The advice unit can also suggest text by referring to expressions and phrases the user has used on social media. The advice unit can also analyze the content of the user's social media interactions and suggest the most suitable text. In this way, by analyzing social media activity, it can suggest text relevant to the user. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the user's social media activity into AI, and the AI ​​can suggest relevant text.

[0078] The selection unit can estimate the user's emotions and suggest a letter set design based on the estimated emotions. For example, if the user is sad, the selection unit can suggest a letter set with a calm design. For example, if the user is happy, the selection unit can suggest a letter set with a bright design. For example, if the user is angry, the selection unit can suggest a letter set with a simple design. This allows for the selection of a more appropriate letter set by suggesting a letter set design that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, or not using AI. For example, the selection unit can input the user's emotions into an AI, and the AI ​​can suggest an appropriate letter set design.

[0079] The selection unit can analyze the user's past letter set selection history and suggest the most suitable letter set. For example, the selection unit can suggest a letter set with a similar design by referencing the design of a letter set previously selected by the user. For example, the selection unit can suggest a letter set with a related theme by referencing the theme of a letter set previously selected by the user. For example, the selection unit can suggest a letter set with the same color scheme by referencing the color scheme of a letter set previously selected by the user. In this way, by analyzing the user's past letter set selection history, the system can suggest the most suitable letter set for the user. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's past letter set selection history into AI, which can then suggest the most suitable letter set.

[0080] The selection unit can filter letter sets based on the user's current projects and areas of interest. For example, the selection unit can suggest letter sets with designs related to the user's current projects. The selection unit can also suggest letter sets on relevant themes based on the user's areas of interest. The selection unit can also suggest the most suitable letter set based on topics the user has recently become interested in. This allows the user to select the most suitable letter set by suggesting letter sets based on their current projects and areas of interest. Some or all of the above processing in the selection unit may be performed using AI, for example, or not. For example, the selection unit can input the user's current projects and areas of interest into an AI, which can then suggest the most suitable letter set.

[0081] The selection unit can estimate the user's emotions and determine the priority of letter sets based on the estimated emotions. For example, if the user is sad, the selection unit will prioritize suggesting letter sets with calm designs. For example, if the user is happy, the selection unit may also prioritize suggesting letter sets with bright designs. For example, if the user is angry, the selection unit may also prioritize suggesting letter sets with simple designs. This allows for the selection of more appropriate letter sets by determining the priority of letter sets according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, or not using AI. For example, the selection unit can input the user's emotions into an AI, which can then determine the priority of appropriate letter sets.

[0082] The selection unit can prioritize suggesting highly relevant letter sets by considering the user's geographical location when selecting a letter set. For example, if the user is in the Kansai region, the selection unit will suggest a letter set incorporating Kansai region designs. If the user is in Hokkaido, the selection unit can also suggest a letter set incorporating Hokkaido designs. If the user is in Okinawa, the selection unit can also suggest a letter set incorporating Okinawa designs. By suggesting letter sets that take geographical location into consideration, the user can select a letter set appropriate for their region. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's geographical location information into AI, which can then suggest highly relevant letter sets.

[0083] The selection unit can analyze the user's social media activity when selecting a letter set and suggest a relevant letter set. For example, the selection unit can suggest a letter set based on content the user has recently shared on social media. The selection unit can also suggest a letter set based on expressions and phrases the user has used on social media. For example, the selection unit can analyze the user's social media interactions and suggest the most suitable letter set. In this way, by analyzing social media activity, it is possible to suggest a letter set relevant to the user. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's social media activity into AI, and the AI ​​can suggest a relevant letter set.

[0084] The printing unit can estimate the user's emotions and adjust the font and layout of the printed text based on the estimated emotions. For example, if the user is sad, the printing unit may suggest a calm font and layout. For example, if the user is happy, the printing unit may suggest a bright font and layout. For example, if the user is angry, the printing unit may suggest a simple font and layout. This allows for the creation of more appropriate letters by suggesting fonts and layouts that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the printing unit may be performed using AI, for example, or not using AI. For example, the printing unit can input the user's emotions into the AI, and the AI ​​can suggest an appropriate font and layout.

[0085] The printing unit can analyze the user's past printing history and select the optimal printing settings during printing. For example, the printing unit can suggest printing settings by referring to fonts and layouts previously used by the user. For example, the printing unit can also suggest optimal printing settings by referring to the content of documents previously printed by the user. For example, the printing unit can suggest optimal printing settings by analyzing the reactions of recipients of letters previously printed by the user. In this way, by analyzing past printing history, the printing unit can suggest the optimal printing settings for the user. Some or all of the above processes in the printing unit may be performed using AI, for example, or without AI. For example, the printing unit can input the user's past printing history into AI, and the AI ​​can suggest the optimal printing settings.

[0086] The printing unit can customize the printed content based on the user's current projects and areas of interest during printing. For example, the printing unit can print content related to the project the user is currently working on. The printing unit can also print relevant content based on the user's areas of interest. The printing unit can also customize the printed content based on topics the user has recently become interested in. This allows the user to receive optimal printed content by suggesting content based on their current projects and areas of interest. Some or all of the above processing in the printing unit may be performed using AI, for example, or without AI. For example, the printing unit can input the user's current projects and areas of interest into the AI, which can then suggest optimal printed content.

[0087] The printing unit can estimate the user's emotions and determine printing priorities based on those emotions. For example, if the user is sad, the printing unit may prioritize suggesting calm fonts and layouts. If the user is happy, the printing unit may prioritize suggesting bright fonts and layouts. If the user is angry, the printing unit may prioritize suggesting simple fonts and layouts. This allows for more appropriate printing by determining printing priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the printing unit may be performed using AI or not. For example, the printing unit can input the user's emotions into the AI, which can then determine appropriate printing priorities.

[0088] The printing unit can select the optimal printing settings while considering the user's geographical location information. For example, if the user is in the Kansai region, the printing unit will print text incorporating the Kansai dialect. For example, if the user is in Hokkaido, the printing unit can also print text incorporating expressions specific to Hokkaido. For example, if the user is in Okinawa, the printing unit can also print text incorporating expressions specific to Okinawa. In this way, by suggesting printing settings that take geographical location information into account, letters appropriate for the region can be created. Some or all of the above processing in the printing unit may be performed using AI, for example, or without AI. For example, the printing unit can input the user's geographical location information into the AI, and the AI ​​can suggest the optimal printing settings.

[0089] The printing unit can analyze the user's social media activity during printing and suggest relevant print content. For example, the printing unit can suggest print content by referring to content the user has recently shared on social media. The printing unit can also suggest print content by referring to expressions and phrases the user has used on social media. For example, the printing unit can analyze the user's social media interactions and suggest the most suitable print content. In this way, by analyzing social media activity, it is possible to suggest print content relevant to the user. Some or all of the above processing in the printing unit may be performed using AI, for example, or without AI. For example, the printing unit can input the user's social media activity into AI, and the AI ​​can suggest relevant print content.

[0090] The guidance system can estimate the user's emotions and adjust the content and method of guidance based on the estimated emotions. For example, if the user is sad, the guidance system can provide comforting words and guidance in a gentle tone. For example, if the user is happy, the guidance system can provide congratulatory words and guidance in a cheerful tone. For example, if the user is angry, the guidance system can provide calm words and guidance in a calm tone. This allows for more appropriate guidance by suggesting guidance content and methods that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance system may be performed using AI, for example, or not using AI. For example, the guidance system can input the user's emotions into an AI, which can then suggest appropriate guidance content and methods.

[0091] The guidance system can provide optimal guidance by referring to the user's past letter-writing history. For example, the guidance system can analyze the content of letters the user has written in the past and provide guidance with similar content. For example, the guidance system can also provide guidance by referring to expressions and phrases the user has used in the past. For example, the guidance system can analyze the reactions of recipients of letters the user has sent in the past and provide optimal guidance. In this way, the guidance system can provide the user with the most suitable guidance by referring to their past letter-writing history. Some or all of the above processes in the guidance system may be performed using AI, for example, or not using AI. For example, the guidance system can input the user's past letter-writing history into AI, and the AI ​​can provide optimal guidance.

[0092] The guidance system can customize its guidance content based on the user's current projects and areas of interest. For example, it can provide guidance related to the project the user is currently working on. It can also provide guidance related to the user's areas of interest. It can also customize its guidance content based on topics the user has recently become interested in. This allows the system to provide the user with the most suitable guidance by suggesting guidance based on their current projects and areas of interest. Some or all of the above processes in the guidance system may be performed using AI, for example, or not. For example, the guidance system can input the user's current projects and areas of interest into an AI, which can then suggest the most suitable guidance.

[0093] The guidance system can estimate the user's emotions and determine the priority of guidance based on the estimated emotions. For example, if the user is sad, the guidance system will prioritize words of comfort. If the user is happy, the guidance system may also prioritize words of congratulations. If the user is angry, the guidance system may also prioritize calming words. By determining the priority of guidance according to the user's emotions, more appropriate guidance can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance system may be performed using AI or not using AI. For example, the guidance system can input the user's emotions into an AI, which can then determine the appropriate priority of guidance.

[0094] The guidance system can provide optimal guidance by taking into account the user's geographical location. For example, if the user is in the Kansai region, the guidance system will incorporate the Kansai dialect. If the user is in Hokkaido, the guidance system can also incorporate expressions specific to Hokkaido. If the user is in Okinawa, the guidance system can also incorporate expressions specific to Okinawa. By proposing guidance that takes geographical location into account, the guidance system can provide guidance appropriate to the region. Some or all of the above processing in the guidance system may be performed using AI, for example, or without AI. For example, the guidance system can input the user's geographical location information into AI, which can then propose the optimal guidance.

[0095] The guidance system can analyze a user's social media activity and suggest relevant guidance content during the guidance process. For example, the guidance system can suggest guidance content based on what the user has recently shared on social media. The guidance system can also suggest guidance content based on expressions and phrases used by the user on social media. The guidance system can also analyze the user's social media interactions and suggest the most appropriate guidance content. In this way, by analyzing social media activity, the guidance system can suggest guidance content relevant to the user. Some or all of the above processing in the guidance system may be performed using AI, for example, or without AI. For example, the guidance system can input the user's social media activity into AI, which can then suggest relevant guidance content.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The advice section can estimate the user's emotions and adjust the tone and style of the message based on those estimates. For example, if the user is sad, it can suggest comforting words and a gentle tone. If the user is happy, it can suggest congratulatory words and a cheerful tone. If the user is angry, it can suggest calm words and a calm tone. This allows for the creation of more appropriate letters by suggesting a tone and style that matches the user's emotions.

[0098] The selection function can estimate the user's emotions and suggest letter set designs based on those emotions. For example, if the user is sad, it can suggest a letter set with a calm design. If the user is happy, it can suggest a letter set with a bright design. If the user is angry, it can suggest a letter set with a simple design. By suggesting letter set designs that match the user's emotions, a more appropriate letter set can be selected.

[0099] The printing unit can estimate the user's emotions and adjust the font and layout of the printed text based on those emotions. For example, if the user is sad, it can suggest a calm font and layout. If the user is happy, it can suggest a bright font and layout. If the user is angry, it can suggest a simple font and layout. This allows for the creation of more appropriate letters by suggesting fonts and layouts that match the user's emotions.

[0100] The guidance system can estimate the user's emotions and adjust the content and method of guidance based on those estimates. For example, if the user is sad, it can provide comforting words and guidance in a gentle tone. If the user is happy, it can provide congratulatory words and guidance in a cheerful tone. If the user is angry, it can provide calm words and guidance in a calm tone. This allows for more appropriate guidance by suggesting guidance content and methods that match the user's emotions.

[0101] The advice function can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is sad, it can prioritize offering words of comfort. If the user is happy, it can prioritize offering words of congratulations. If the user is angry, it can prioritize offering calming words. By prioritizing advice according to the user's emotions, it can provide more appropriate advice.

[0102] The advice section can analyze a user's past letter-writing history and suggest the most suitable wording. For example, it can analyze the content of letters the user has written in the past and suggest similar wording. It can also suggest wording based on expressions and phrases the user has used in the past. It can analyze the responses of recipients to letters the user has sent in the past and suggest the most suitable wording. In this way, by analyzing the user's past letter-writing history, it can suggest the most suitable wording for the user.

[0103] The selection section can analyze the user's past letter set selection history and suggest the most suitable letter set. For example, it can suggest letter sets with similar designs by referencing the designs of letter sets the user has selected in the past. It can suggest letter sets with related themes by referencing the themes of letter sets the user has selected in the past. It can suggest letter sets with the same color scheme by referencing the color scheme of letter sets the user has selected in the past. In this way, by analyzing the user's past letter set selection history, the system can suggest the most suitable letter set for them.

[0104] The printing unit can analyze the user's past printing history and select the optimal printing settings during printing. For example, it can suggest printing settings based on fonts and layouts the user has used in the past. It can also suggest optimal printing settings based on the content of documents the user has printed in the past. Furthermore, it can suggest optimal printing settings by analyzing the recipient's reactions to letters the user has printed in the past. In this way, by analyzing past printing history, the system can suggest the most suitable printing settings for the user.

[0105] The guidance system can provide optimal guidance by referencing the user's past letter-writing history. For example, it can analyze the content of letters the user has written in the past and provide guidance with similar content. It can also provide guidance by referring to expressions and phrases the user has used in the past. It can analyze the responses of recipients to letters the user has sent in the past and provide optimal guidance. In this way, by referring to the user's past letter-writing history, it can provide the most suitable guidance.

[0106] The advice section can provide advice at the appropriate time, depending on the progress of the writing. For example, it can provide advice on the opening when the user has just started writing. It can provide advice on the development of the content when the user is in the middle of writing. It can provide advice on concluding when the user is nearing the end of writing. In this way, by providing advice according to the progress of the writing, the letter writing process becomes smoother.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The advice section provides real-time advice and suggestions for the writing process. While the user is writing the letter, it suggests appropriate wording and adjusts the tone and style. It provides advice on the opening at the beginning, advice on developing the content in the middle, and advice on concluding as the user nears the end. Step 2: The selection section chooses the most suitable letter set from the postal service's inventory based on the user's content. It suggests the best letter set based on the user's preferences and themes, past letter set selection history, current projects and areas of interest, and emotions. Step 3: The printing unit prints the completed text onto the specified letter set and automates the delivery process. It automatically places the printed letters into envelopes and handles the delivery. It adjusts the font and layout of the print based on the user's sentiment and analyzes past printing history to select the optimal printing settings. It customizes the printed content based on the current project and areas of interest. Step 4: Guide Nanbu provides comprehensive support, from writing the opening of the letter to guiding you through its content. It offers advice on letter writing and content for beginners, and adjusts the content and methods of the guidance based on the user's feelings. It provides optimal guidance by referring to past letter writing history and customizes the guidance content based on the current project and areas of interest.

[0109] 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.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] 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.

[0112] Each of the multiple elements described above, including the advice unit, selection unit, printing unit, and guidance unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the advice unit is implemented by the control unit 46A of the smart device 14 and provides real-time advice while the user is writing a letter. The selection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and selects the most suitable letter set from the postal service's inventory according to the user's content. The printing unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and prints the completed text on the specified letter set, automating the delivery procedure. The guidance unit is implemented, for example, by the control unit 46A of the smart device 14 and provides comprehensive support from the beginning of writing the letter to guidance on its content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] 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.

[0128] Each of the multiple elements described above, including the advice unit, selection unit, printing unit, and guidance unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the advice unit is implemented by the control unit 46A of the smart glasses 214 and provides real-time advice while the user is writing a letter. The selection unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects the most suitable letter set from the postal service's inventory according to the user's content. The printing unit is implemented by the identification processing unit 290 of the data processing unit 12 and prints the completed text onto the specified letter set, automating the delivery procedure. The guidance unit is implemented by the control unit 46A of the smart glasses 214 and provides comprehensive support from the beginning of writing the letter to guidance on its content. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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.

[0144] Each of the multiple elements described above, including the advice unit, selection unit, printing unit, and guidance unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the advice unit is implemented by the control unit 46A of the headset terminal 314 and provides real-time advice while the user is writing a letter. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects the most suitable letter set from the postal service's inventory according to the user's content. The printing unit is implemented by the specific processing unit 290 of the data processing unit 12 and prints the completed text onto the specified letter set, automating the delivery procedure. The guidance unit is implemented by the control unit 46A of the headset terminal 314 and provides comprehensive support from the beginning of writing the letter to guidance on its content. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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.

[0161] Each of the multiple elements described above, including the advice unit, selection unit, printing unit, and guidance unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the advice unit is implemented by the control unit 46A of the robot 414 and provides real-time advice while the user is writing a letter. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects the most suitable letter set from the postal service's inventory according to the user's content. The printing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and prints the completed text onto the specified letter set, automating the delivery procedure. The guidance unit is implemented by, for example, the control unit 46A of the robot 414 and provides comprehensive support from the beginning of writing the letter to guidance on its content. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The advice department provides real-time advice and suggestions for wording for posts, A selection section that allows users to choose the most suitable letter set from the postal service's inventory based on their needs, A printing unit prints the completed text onto a specified letter set and automates the delivery process, It includes a guide that provides comprehensive support from the opening of a letter to guidance on its content. A system characterized by the following features. (Note 2) The aforementioned advice section, We provide real-time advice and suggestions for wording for posts. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned selection unit is Select the most suitable letter set from the postal service's inventory, tailored to the user's needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned printing section is, The completed document is printed on a designated letter set, and the delivery process is automated. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned southern tip is, We provide comprehensive support, from writing the opening of a letter to providing guidance on its content. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advice section, It estimates the user's emotions and adjusts the tone and style of the text based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned advice section, The system analyzes the user's past letter-writing history and suggests the most suitable wording. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned advice section, We will provide advice at the appropriate time, depending on the progress of the writing process. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned advice section, When posting, the system will suggest region-specific expressions that take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned advice section, When posting, the system analyzes the user's social media activity and suggests relevant text. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned selection unit is It estimates the user's emotions and proposes a letter set design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned selection unit is We analyze the user's past letter set selection history and suggest the most suitable letter set. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned selection unit is When selecting a letter set, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned selection unit is It estimates the user's emotions and determines the priority of letter sets based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned selection unit is When selecting a letter set, the system prioritizes suggesting the most relevant letter sets by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned selection unit is When selecting a letter set, the system analyzes the user's social media activity and suggests relevant letter sets. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned printing section is, It estimates the user's emotions and adjusts the font and layout of the printed text based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned printing section is, During printing, the system analyzes the user's past printing history and selects the optimal printing settings. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned printing section is, When printing, the content of the printout is customized based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned printing section is, It estimates the user's emotions and determines the printing priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned printing section is, When printing, the system selects the optimal print settings by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned printing section is, During printing, the system analyzes the user's social media activity and suggests relevant print content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned southern tip is, The system estimates the user's emotions and adjusts the content and methods of the guidance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned southern tip is, When providing guidance, the system will refer to the user's past letter-writing history to provide the most suitable guidance. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned southern tip is, During the guidance session, the content of the guidance will be customized based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned southern tip is, It estimates the user's emotions and determines the priority of guidance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned southern tip is, When providing guidance, the system will take into account the user's geographical location to provide the most appropriate guidance. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned southern tip is, During the guidance process, we analyze the user's social media activity and suggest relevant guidance content. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 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. The advice department provides real-time advice and suggestions for wording for posts, A selection section that allows users to choose the most suitable letter set from the postal service's inventory based on their needs, A printing unit prints the completed text onto a specified letter set and automates the delivery process, It includes a guide that provides comprehensive support from the opening of a letter to guidance on its content. A system characterized by the following features.

2. The aforementioned advice section, It estimates the user's emotions and adjusts the tone and style of the text based on those estimated emotions. The system according to feature 1.

3. The aforementioned advice section, The system analyzes the user's past letter-writing history and suggests the most suitable wording. The system according to feature 1.

4. The aforementioned advice section, We will provide advice at the appropriate time, depending on the progress of the writing process. The system according to feature 1.

5. The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system according to feature 1.

6. The aforementioned advice section, When posting, the system will suggest region-specific expressions that take into account the user's geographical location. The system according to feature 1.

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