system

Abstract

The system according to this embodiment aims to automate the creation of routine emails and provide an environment where employees can focus on more creative tasks. [Solution] The system according to the embodiment comprises an automation unit, a learning unit, and a construction unit. The automation unit automates scheduled emails. The learning unit trains a script platform with the code generated by the automation unit. The construction unit constructs the system based on the code learned by the learning unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, a large amount of man-hours is required to create regular mails within the company, and there is a problem that employees cannot concentrate on creative work.

[0005] The system according to the embodiment aims to automate the creation of regular mails and provide an environment in which employees can focus on more creative work.

Means for Solving the Problems

[0006] The system according to the embodiment includes an automation unit, a learning unit, and a construction unit. The automation unit automates the regular mails. The learning unit causes the generated AI to learn the code generated by the automation unit. The construction unit constructs a system based on the code learned by the learning unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate the creation of routine emails, providing an environment where employees can focus on more creative tasks. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 email automation system according to an embodiment of the present invention is a system that reduces the amount of time spent on email within a company and creates an environment where all employees can focus on more creative tasks. This email automation system allows each employee to automate routine emails using a generation AI, and then the code generated is trained on the company-wide scripting platform, creating a system that can create and run its own code tailored to the company's needs. For example, the email automation system trains the generation AI with emails that each employee sends regularly, such as weekly progress reports and meeting reminder emails. The generation AI analyzes the content of these emails and automatically generates them. This eliminates the need for employees to manually compose emails. Next, the email automation system trains the generated code on the company-wide scripting platform. The scripting platform can achieve automation by linking with various services. By training the scripting platform with the code generated by the generation AI, a system is built that can create and run its own code tailored to the company's needs. For example, scripts can be created to automatically send emails based on specific conditions or to generate emails in a specific format. This system reduces the amount of time spent on email. Employees can eliminate the need to manually compose emails and focus on more creative tasks. Furthermore, using a scripting platform allows for the creation of a company-wide unified automation system, which also serves as a countermeasure against unofficial scripting platforms. For example, scripts created independently by each employee can be managed within a unified system without any mixing. In addition, using generative AI makes script maintenance easier. Generative AI can automatically modify and add to scripts, simplifying system maintenance. For example, even if a new format for regular emails is added, the generative AI can automatically handle it. In this way, the email automation system uses generative AI and a scripting platform to reduce the workload of email management within the company, providing an environment where employees can focus on more creative tasks.This allows email automation systems to reduce the time employees spend writing emails, providing an environment where they can focus on more creative tasks.

[0029] The email automation system according to this embodiment comprises an automation unit, a learning unit, and a construction unit. The automation unit automates routine emails. The automation unit trains a generation AI on regularly sent emails, such as weekly progress report emails and meeting reminder emails. The generation AI analyzes the content of these emails and automatically generates emails. For example, the generation AI can learn the content of emails using natural language processing technology based on past email data and automatically generate routine emails. The learning unit trains a script platform with the code generated by the automation unit. The learning unit, for example, imports the code generated by the generation AI into the script platform and realizes automation in cooperation with various services. For example, the learning unit creates scripts that automatically send emails based on specific conditions or scripts that generate emails in a specific format. The construction unit builds a system based on the code learned by the learning unit. For example, the construction unit builds a system that can create and run its own code to meet our needs. For example, the construction unit can build a system based on specific functional requirements or performance requirements. As a result, the email automation system according to the embodiment can efficiently automate routine emails and build the system. Some or all of the above-described processes in the automation unit, learning unit, and construction unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the automation unit can automatically generate routine emails using a generation AI, the learning unit can train a script platform with the code generated using the generation AI, and the construction unit can build the system using the generation AI.

[0030] The automation unit automates the generation of regular emails. For example, it trains a generation AI on regularly sent emails, such as weekly progress reports and meeting reminder emails. The generation AI analyzes the content of these emails and automatically generates new emails. Specifically, the generation AI collects past email data and analyzes the content using natural language processing technology. For example, in the case of progress report emails, it extracts information such as project progress, achieved goals, and next week's schedule, and generates an email in the appropriate format. In the case of meeting reminder emails, it creates a reminder based on information such as the date, time, location, participants, and agenda. The generation AI can automatically combine this information to generate emails in natural language. Furthermore, the generation AI can improve the content of emails based on user feedback. For example, if a user makes revisions to a generated email, the AI ​​learns from these revisions and incorporates them into future email generation. This allows the automation unit to automatically generate high-quality regular emails tailored to user needs. The automation unit also has a function to automatically send the generated emails. For example, it's possible to set a schedule for sending emails at specific dates and times, automatically sending the generated emails at the designated time. This significantly reduces the effort users spend creating and sending routine emails.

[0031] The learning unit trains the script platform with the code generated by the automation unit. For example, the learning unit imports the code generated by the generation AI into the script platform and integrates with various services to achieve automation. For instance, when creating a script to automatically send emails based on specific conditions, the learning unit imports the script generated by the generation AI into the script platform and configures it to automatically send emails based on those conditions (e.g., a specific date and time or the occurrence of an event). Similarly, when creating a script to generate emails in a specific format, the learning unit imports the script generated by the generation AI into the script platform and configures it to generate emails in that format (e.g., progress report emails or meeting reminder emails). Furthermore, the learning unit can utilize the script platform's functions to monitor the execution results of scripts and modify them as needed. For example, it can log the execution results of scripts and modify the script based on the error log if an error occurs. The learning unit can also improve the script content based on user feedback. This allows the learning unit to train the script platform with the code generated by the generation AI and integrate with various services to achieve automation.

[0032] The Construction Department builds the system based on the code learned by the Learning Department. For example, the Construction Department builds a system that can generate and run its own code tailored to our needs. Specifically, the Construction Department designs the system based on specific functional and performance requirements and builds the system by combining the necessary components. For example, based on specific functional requirements, it implements automatic generation and sending functions for regular emails. It also optimizes the system's processing speed and scalability based on performance requirements. Furthermore, the Construction Department tests the system and verifies its operation. For example, it verifies whether the content of emails generated by the generation AI is accurate and whether automatic email sending is performed correctly. It also evaluates the system's performance and optimizes it as needed. This allows the Construction Department to build a high-quality email automation system tailored to our needs. In addition, the Construction Department operates and maintains the system. For example, it performs regular maintenance on the system to ensure stable operation. It also updates the system, adding new functions and improvements. This allows the Construction Department to support the long-term operation of the system and provide flexible support to meet user needs.

[0033] The automation unit can learn the content of regular emails and automatically generate emails. For example, the automation unit learns the content of regular emails using natural language processing technology based on past email data. For example, the automation unit can use a generation AI to analyze the content of past progress report emails and meeting reminder emails and automatically generate emails. For example, the automation unit can receive a prompt from the generation AI saying, "Please learn the content of this email," and automatically generate regular emails based on past email data. This enables the automation unit to automatically generate regular emails. Some or all of the above processing in the automation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the automation unit can use a generation AI to learn the content of regular emails and automatically generate emails.

[0034] The learning unit can train the script platform with the generated code. For example, the learning unit can import the code generated by the generation AI into the script platform and automate tasks by integrating with various services. For example, by training the script platform with the code generated by the generation AI, the learning unit can create scripts that automatically send emails based on specific conditions or scripts that generate emails in a specific format. For example, the learning unit can receive a prompt from the generation AI saying, "Please train the script platform with this code," and train the script platform with the generated code. In this way, the learning unit can train the script platform with the generated code. Some or all of the above-described processes in the learning unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the learning unit can train the script platform with the code generated using the generation AI.

[0035] The Construction Department can build a system that can generate and run its own code tailored to our needs. For example, the Construction Department can build a system based on specific functional or performance requirements. For instance, the Construction Department can build a system that can generate and run its own code tailored to our needs based on code generated by a generation AI. For example, the Construction Department can build a system that can generate and run its own code tailored to our needs when the generation AI receives a prompt such as "Please build a system based on this code." This allows the Construction Department to build a system that can generate and run its own code tailored to our needs. Some or all of the above-described processes in the Construction Department may be performed using a generation AI, or not. For example, the Construction Department can use a generation AI to build a system that can generate and run its own code tailored to our needs.

[0036] The Construction Department can build a company-wide unified automation system. The Construction Department can build the system based on unified protocols and standardized procedures, for example. For example, the Construction Department can build a company-wide unified automation system based on code generated by a Generative AI. For example, the Construction Department can build the system based on unified protocols and standardized procedures when the Generative AI receives a prompt such as "Please build a company-wide unified automation system." This allows the Construction Department to build a company-wide unified automation system. Some or all of the above processes in the Construction Department may be performed using a Generative AI, for example, or without using a Generative AI. For example, the Construction Department can build a company-wide unified automation system using a Generative AI.

[0037] The build unit can perform maintenance on the script. The build unit performs maintenance based on, for example, bug fixes, feature additions, and periodic updates. For example, the build unit can perform maintenance on the script based on the code generated by the generation AI. For example, the build unit can perform maintenance based on bug fixes, feature additions, and periodic updates when the generation AI receives a prompt from it saying, "Please perform maintenance on the script." This allows the build unit to perform maintenance on the script. Some or all of the above processes in the build unit may be performed using, for example, the generation AI, or not using the generation AI. For example, the build unit can perform maintenance on the script using the generation AI.

[0038] The automation unit can analyze the content of regular emails and select the optimal email format based on past email history. For example, the automation unit can use a generation AI to analyze past progress report emails and select the most frequently used format. The automation unit can also use a generation AI to analyze past meeting reminder emails and select the most effective format. Furthermore, the automation unit can use a generation AI to analyze past project update emails and select the most appropriate format. In this way, the automation unit can select the optimal email format based on past email history. Some or all of the above processing in the automation unit may be performed using a generation AI, for example, or without a generation AI. For example, the automation unit can use a generation AI to analyze the content of regular emails and select the optimal email format based on past email history.

[0039] The automation unit can customize the content of regular emails based on the user's current work status and project progress when generating them. For example, the automation unit's generation AI can check the user's current project progress and reflect the content in the email according to that progress. The automation unit can also have the generation AI check the user's current work status and reflect the content in the email according to the priority of the tasks. For example, the automation unit can have the generation AI check the user's current work status and reflect the content in the email according to the priority of the tasks. Furthermore, the automation unit can have the generation AI check the user's current task status and reflect the content in the email according to the progress of the tasks. For example, the automation unit can have the generation AI check the user's current task status and reflect the content in the email according to the progress of the tasks. This allows the automation unit to customize the content of regular emails according to the user's work status and project progress. Some or all of the above processing in the automation unit may be performed using, for example, the generation AI, or without the generation AI. For example, the automation unit can use generation AI to customize the content of routine emails based on the user's current work status and project progress.

[0040] The automation unit can prioritize the generation of highly relevant content by considering the user's geographical location information when generating regular emails. For example, the automation unit's generation AI can check the user's current location and include geographically relevant information in the email. The automation unit can also have the generation AI check the user's business trip destination and include information related to that destination in the email. Furthermore, the automation unit can have the generation AI check the user's business trip destination and include information related to that destination in the email. In addition, the automation unit can have the generation AI check the user's office location and include information related to the office in the email. This allows the automation unit to generate highly relevant content based on the user's geographical location information. Some or all of the above processing in the automation unit may be performed using, for example, the generation AI, or without the generation AI. For example, the automation unit can use the generation AI to prioritize the generation of highly relevant content by considering the user's geographical location information when generating regular emails.

[0041] The automation unit can analyze a user's social media activity and include relevant information in the email when generating scheduled emails. For example, the automation unit can use a generation AI to analyze a user's social media posts and include relevant topics in the email. The automation unit can also use a generation AI to check the number of a user's social media followers and include influential information in the email. Furthermore, the automation unit can use a generation AI to analyze a user's social media engagement and include information of high interest in the email. This allows the automation unit to include relevant information in the email based on the user's social media activity. Some or all of the above processing in the automation unit may be performed using a generation AI, or without one. For example, the automation unit can use a generation AI to analyze a user's social media activity and include relevant information in the email when generating scheduled emails.

[0042] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can have a generative AI analyze past learning data and select the optimal algorithm. The learning unit can also have a generative AI evaluate past learning results and adjust the algorithm parameters. Furthermore, the learning unit can have a generative AI identify areas for improvement in the algorithm based on past learning data. This allows the learning unit to optimize the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the learning unit can optimize the learning algorithm by referring to past learning data during the learning process using a generative AI.

[0043] The learning unit can improve accuracy by filtering training data based on specific business processes during training. For example, the learning unit can enable the generative AI to prioritize learning data related to specific business processes. The learning unit can also enable the generative AI to exclude unnecessary data based on specific business processes. Furthermore, the learning unit can enable the generative AI to evaluate the importance of data based on specific business processes. This allows the learning unit to filter training data based on specific business processes and improve accuracy. Some or all of the above processing in the learning unit may be performed using the generative AI, or not. For example, the learning unit can use the generative AI to filter training data based on specific business processes during training and improve accuracy.

[0044] The learning unit can weight the training data based on the submission timing of regular emails during training. For example, the learning unit can use a generative AI to weight important data based on the submission timing of regular emails. The learning unit can also use a generative AI to reduce the weight of unnecessary data based on the submission timing of regular emails. Furthermore, the learning unit can use a generative AI to set data priorities based on the submission timing of regular emails. In addition, the learning unit can use a generative AI to set data priorities based on the submission timing of regular emails. This allows the learning unit to weight the training data based on the submission timing of regular emails. Some or all of the above processing in the learning unit may be performed using a generative AI, or without using a generative AI. For example, the learning unit can use a generative AI to weight the training data based on the submission timing of regular emails during training.

[0045] The learning unit can analyze the user's work history during training and prioritize learning relevant data. For example, the learning unit can have a generative AI analyze the user's work history and prioritize learning relevant data. The learning unit can also have a generative AI identify important data based on the user's work history. Furthermore, the learning unit can have a generative AI refer to the user's work history and set the priority of the learning data. This allows the learning unit to prioritize learning relevant data based on the user's work history. Some or all of the above processing in the learning unit may be performed using a generative AI, or not. For example, the learning unit can use a generative AI to analyze the user's work history during training and prioritize learning relevant data.

[0046] The construction unit can select the optimal construction method by referring to past system construction history when building a system. For example, the construction unit can use a generation AI to analyze past system construction history and select the optimal method. The construction unit can also use a generation AI to evaluate past system construction results and identify areas for improvement in the methods. Furthermore, the construction unit can use a generation AI to evaluate past system construction results and identify areas for improvement in the methods. In addition, the construction unit can use a generation AI to select an efficient method based on past system construction history. This allows the construction unit to select the optimal construction method by referring to past system construction history. Some or all of the above-described processes in the construction unit may be performed using a generation AI, or without using a generation AI. For example, the construction unit can use a generation AI to select the optimal construction method by referring to past system construction history when building a system.

[0047] The construction unit can customize the system based on specific business processes during system construction. For example, the construction unit can use a generation AI to customize the system based on specific business processes. The construction unit can also use a generation AI to prioritize the construction of functions related to specific business processes. Furthermore, the construction unit can use a generation AI to adjust system settings based on specific business processes. This allows the construction unit to customize the system based on specific business processes. Some or all of the above-described processes in the construction unit may be performed using a generation AI, or not. For example, the construction unit can use a generation AI to customize the system based on specific business processes during system construction.

[0048] The construction unit can select the optimal construction method when building a system, taking into account the user's geographical location information. For example, the construction unit can use a generation AI to confirm the user's current location and select a geographically relevant method. The construction unit can also use a generation AI to confirm the user's business trip destination and select a method suitable for that destination. Furthermore, the construction unit can use a generation AI to confirm the user's business trip destination and select a method suitable for that destination. In addition, the construction unit can use a generation AI to confirm the user's office location and select a method suitable for that office. This allows the construction unit to select the optimal construction method based on the user's geographical location information. Some or all of the above-described processes in the construction unit may be performed using a generation AI, or without using a generation AI. For example, the construction unit can use a generation AI to select the optimal construction method when building a system, taking into account the user's geographical location information.

[0049] The system development unit can analyze users' social media activity and reflect relevant information in the system during system development. For example, the system development unit can use a generative AI to analyze users' social media posts and reflect relevant topics in the system. The system development unit can also use a generative AI to check the number of users' social media followers and reflect influential information in the system. Furthermore, the system development unit can use a generative AI to analyze users' social media engagement and reflect information of high interest in the system. This allows the system development unit to reflect relevant information in the system based on users' social media activity. Some or all of the above processing in the system development unit may be performed using a generative AI, or without one. For example, the system development unit can use a generative AI to analyze users' social media activity and reflect relevant information in the system during system development.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The automation unit can analyze the user's work schedule and determine the optimal timing for sending emails. For example, it can refer to the user's calendar and avoid sending emails immediately before or after meetings. It can also consider the user's working hours and avoid sending emails outside of business hours. Furthermore, it can send important emails at the appropriate time to coincide with the user's project deadlines. In this way, the automation unit can determine the optimal timing for sending emails based on the user's work schedule.

[0052] The automation unit can analyze a user's past email history and select the optimal email format. For example, it can analyze past progress report emails and select the most frequently used format. It can also analyze past meeting reminder emails and select the most effective format. Furthermore, it can analyze past project update emails and select the most appropriate format. In this way, the automation unit can select the optimal email format based on past email history.

[0053] The automation unit can customize email content based on the user's work status. For example, it can check the user's current project progress and reflect the content in the email according to that progress. It can also check the user's current work status and reflect the content in the email according to the priority of the task. Furthermore, it can check the user's current task status and reflect the content in the email according to the progress of the task. In this way, the automation unit can customize email content based on the user's work status.

[0054] The automated system can prioritize generating highly relevant content by considering the user's geographical location. For example, it can check the user's current location and include geographically relevant information in emails. It can also check the user's business trip destination and include relevant information in emails. Furthermore, it can check the user's office location and include relevant information in emails. In this way, the automated system can generate highly relevant content based on the user's geographical location.

[0055] The automation unit can analyze users' social media activity and include relevant information in emails. For example, it can analyze users' social media posts and include relevant topics in emails. It can also check the number of users' social media followers and include influential information in emails. Furthermore, it can analyze users' social media engagement and include information of high interest in emails. In this way, the automation unit can include relevant information in emails based on users' social media activity.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The automation unit automates routine emails. For example, it trains the generation AI on regularly sent emails such as weekly progress report emails and meeting reminder emails. The generation AI analyzes the content of past email data using natural language processing technology and automatically generates emails. Step 2: The learning unit trains the script platform with the code generated by the automation unit. For example, the code generated by the generation AI is imported into the script platform and integrated with various services to achieve automation. This allows for the creation of scripts that automatically send emails based on specific conditions, or scripts that generate emails in a specific format. Step 3: The build unit constructs the system based on the code learned by the learning unit. For example, it can build a system that can generate and run its own code tailored to our needs. It can build a system based on specific functional and performance requirements.

[0058] (Example of form 2) The email automation system according to an embodiment of the present invention is a system that reduces the amount of time spent on email within a company and creates an environment where all employees can focus on more creative work. This email automation system allows each employee to automate routine emails using a generation AI, and then trains the company-wide scripting platform with the resulting code, thereby building a system that can create and run its own code tailored to the company's needs. For example, the email automation system trains the generation AI with emails that each employee sends regularly, such as weekly progress reports and meeting reminder emails. The generation AI analyzes the content of these emails and automatically generates them. This eliminates the need for employees to manually compose emails. Next, the email automation system trains the generated code on the company-wide scripting platform. The scripting platform is a scripting language that can integrate with various services to achieve automation. By training the scripting platform with the code generated by the generation AI, a system is built that can create and run its own code tailored to the company's needs. For example, it can create scripts that automatically send emails based on specific conditions or scripts that generate emails in a specific format. This system reduces the amount of time spent on email. Employees can avoid the hassle of manually composing emails, allowing them to focus on more creative tasks. Furthermore, using a scripting platform enables the creation of a company-wide unified automation system, providing protection against unofficial scripting platforms. For example, scripts created independently by individual employees can be managed within a unified system without becoming a mix. Additionally, using generative AI simplifies script maintenance. Generative AI can automatically modify and add scripts, simplifying system maintenance. For instance, if a new format for routine emails is added, the generative AI can automatically handle it. In this way, the email automation system, utilizing generative AI and a scripting platform, reduces internal email workload, providing employees with an environment where they can focus on more creative tasks.This allows email automation systems to reduce the time employees spend writing emails, providing an environment where they can focus on more creative tasks.

[0059] The email automation system according to this embodiment comprises an automation unit, a learning unit, and a construction unit. The automation unit automates routine emails. The automation unit trains a generation AI on regularly sent emails, such as weekly progress report emails and meeting reminder emails. The generation AI analyzes the content of these emails and automatically generates emails. For example, the generation AI can learn the content of emails using natural language processing technology based on past email data and automatically generate routine emails. The learning unit trains a script platform with the code generated by the automation unit. The learning unit, for example, imports the code generated by the generation AI into the script platform and realizes automation in cooperation with various services. For example, the learning unit creates scripts that automatically send emails based on specific conditions or scripts that generate emails in a specific format. The construction unit builds a system based on the code learned by the learning unit. For example, the construction unit builds a system that can create and run its own code to meet our needs. For example, the construction unit can build a system based on specific functional requirements or performance requirements. As a result, the email automation system according to the embodiment can efficiently automate routine emails and build the system. Some or all of the above-described processes in the automation unit, learning unit, and construction unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the automation unit can automatically generate routine emails using a generation AI, the learning unit can train a script platform with the code generated using the generation AI, and the construction unit can build the system using the generation AI.

[0060] The automation unit automates the generation of regular emails. For example, it trains a generation AI on regularly sent emails, such as weekly progress reports and meeting reminder emails. The generation AI analyzes the content of these emails and automatically generates new emails. Specifically, the generation AI collects past email data and analyzes the content using natural language processing technology. For example, in the case of progress report emails, it extracts information such as project progress, achieved goals, and next week's schedule, and generates an email in the appropriate format. In the case of meeting reminder emails, it creates a reminder based on information such as the date, time, location, participants, and agenda. The generation AI can automatically combine this information to generate emails in natural language. Furthermore, the generation AI can improve the content of emails based on user feedback. For example, if a user makes revisions to a generated email, the AI ​​learns from these revisions and incorporates them into future email generation. This allows the automation unit to automatically generate high-quality regular emails tailored to user needs. The automation unit also has a function to automatically send the generated emails. For example, it's possible to set a schedule for sending emails at specific dates and times, automatically sending the generated emails at the designated time. This significantly reduces the effort users spend creating and sending routine emails.

[0061] The learning unit trains the script platform with the code generated by the automation unit. For example, the learning unit imports the code generated by the generation AI into the script platform and integrates with various services to achieve automation. For instance, when creating a script to automatically send emails based on specific conditions, the learning unit imports the script generated by the generation AI into the script platform and configures it to automatically send emails based on those conditions (e.g., a specific date and time or the occurrence of an event). Similarly, when creating a script to generate emails in a specific format, the learning unit imports the script generated by the generation AI into the script platform and configures it to generate emails in that format (e.g., progress report emails or meeting reminder emails). Furthermore, the learning unit can utilize the script platform's functions to monitor the execution results of scripts and modify them as needed. For example, it can log the execution results of scripts and modify the script based on the error log if an error occurs. The learning unit can also improve the script content based on user feedback. This allows the learning unit to train the script platform with the code generated by the generation AI and integrate with various services to achieve automation.

[0062] The Construction Department builds the system based on the code learned by the Learning Department. For example, the Construction Department builds a system that can generate and run its own code tailored to our needs. Specifically, the Construction Department designs the system based on specific functional and performance requirements and builds the system by combining the necessary components. For example, based on specific functional requirements, it implements automatic generation and sending functions for regular emails. It also optimizes the system's processing speed and scalability based on performance requirements. Furthermore, the Construction Department tests the system and verifies its operation. For example, it verifies whether the content of emails generated by the generation AI is accurate and whether automatic email sending is performed correctly. It also evaluates the system's performance and optimizes it as needed. This allows the Construction Department to build a high-quality email automation system tailored to our needs. In addition, the Construction Department operates and maintains the system. For example, it performs regular maintenance on the system to ensure stable operation. It also updates the system, adding new functions and improvements. This allows the Construction Department to support the long-term operation of the system and provide flexible support to meet user needs.

[0063] The automation unit can learn the content of regular emails and automatically generate emails. For example, the automation unit learns the content of regular emails using natural language processing technology based on past email data. For example, the automation unit can use a generation AI to analyze the content of past progress report emails and meeting reminder emails and automatically generate emails. For example, the automation unit can receive a prompt from the generation AI saying, "Please learn the content of this email," and automatically generate regular emails based on past email data. This enables the automation unit to automatically generate regular emails. Some or all of the above processing in the automation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the automation unit can use a generation AI to learn the content of regular emails and automatically generate emails.

[0064] The learning unit can train the script platform with the generated code. For example, the learning unit can import the code generated by the generation AI into the script platform and automate tasks by integrating with various services. For example, by training the script platform with the code generated by the generation AI, the learning unit can create scripts that automatically send emails based on specific conditions or scripts that generate emails in a specific format. For example, the learning unit can receive a prompt from the generation AI saying, "Please train the script platform with this code," and train the script platform with the generated code. In this way, the learning unit can train the script platform with the generated code. Some or all of the above-described processes in the learning unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the learning unit can train the script platform with the code generated using the generation AI.

[0065] The Construction Department can build a system that can generate and run its own code tailored to our needs. For example, the Construction Department can build a system based on specific functional or performance requirements. For instance, the Construction Department can build a system that can generate and run its own code tailored to our needs based on code generated by a generation AI. For example, the Construction Department can build a system that can generate and run its own code tailored to our needs when the generation AI receives a prompt such as "Please build a system based on this code." This allows the Construction Department to build a system that can generate and run its own code tailored to our needs. Some or all of the above-described processes in the Construction Department may be performed using a generation AI, or not. For example, the Construction Department can use a generation AI to build a system that can generate and run its own code tailored to our needs.

[0066] The Construction Department can build a company-wide unified automation system. The Construction Department can build the system based on unified protocols and standardized procedures, for example. For example, the Construction Department can build a company-wide unified automation system based on code generated by a Generative AI. For example, the Construction Department can build the system based on unified protocols and standardized procedures when the Generative AI receives a prompt such as "Please build a company-wide unified automation system." This allows the Construction Department to build a company-wide unified automation system. Some or all of the above processes in the Construction Department may be performed using a Generative AI, for example, or without using a Generative AI. For example, the Construction Department can build a company-wide unified automation system using a Generative AI.

[0067] The build unit can perform maintenance on the script. The build unit performs maintenance based on, for example, bug fixes, feature additions, and periodic updates. For example, the build unit can perform maintenance on the script based on the code generated by the generation AI. For example, the build unit can perform maintenance based on bug fixes, feature additions, and periodic updates when the generation AI receives a prompt from it saying, "Please perform maintenance on the script." This allows the build unit to perform maintenance on the script. Some or all of the above processes in the build unit may be performed using, for example, the generation AI, or not using the generation AI. For example, the build unit can perform maintenance on the script using the generation AI.

[0068] The automation unit can estimate the user's emotions and adjust the timing of regular email generation based on the estimated emotions. For example, if the user is feeling stressed, the generation AI can delay the sending of regular emails. The automation unit can also speed up the sending of regular emails if the user is relaxed. For example, if the generation AI estimates the user's emotions and they are relaxed, the automation unit can speed up the sending of regular emails. Furthermore, if the user is in a hurry, the generation AI can instantly set the sending time for regular emails. For example, if the generation AI estimates the user's emotions and they are in a hurry, the automation unit can instantly set the sending time for regular emails. In this way, the automation unit can adjust the timing of regular email generation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the automation unit may be performed using a generation AI, or not. For example, the automation unit can use a generation AI to estimate the user's emotions and adjust the timing of the generation of regular emails based on the estimated user emotions.

[0069] The automation unit can analyze the content of regular emails and select the optimal email format based on past email history. For example, the automation unit can use a generation AI to analyze past progress report emails and select the most frequently used format. The automation unit can also use a generation AI to analyze past meeting reminder emails and select the most effective format. Furthermore, the automation unit can use a generation AI to analyze past project update emails and select the most appropriate format. In this way, the automation unit can select the optimal email format based on past email history. Some or all of the above processing in the automation unit may be performed using a generation AI, for example, or without a generation AI. For example, the automation unit can use a generation AI to analyze the content of regular emails and select the optimal email format based on past email history.

[0070] The automation unit can customize the content of regular emails based on the user's current work status and project progress when generating them. For example, the automation unit's generation AI can check the user's current project progress and reflect the content in the email according to that progress. The automation unit can also have the generation AI check the user's current work status and reflect the content in the email according to the priority of the tasks. For example, the automation unit can have the generation AI check the user's current work status and reflect the content in the email according to the priority of the tasks. Furthermore, the automation unit can have the generation AI check the user's current task status and reflect the content in the email according to the progress of the tasks. For example, the automation unit can have the generation AI check the user's current task status and reflect the content in the email according to the progress of the tasks. This allows the automation unit to customize the content of regular emails according to the user's work status and project progress. Some or all of the above processing in the automation unit may be performed using, for example, the generation AI, or without the generation AI. For example, the automation unit can use generation AI to customize the content of routine emails based on the user's current work status and project progress.

[0071] The automated unit can estimate the user's emotions and determine the priority of emails to generate based on the estimated emotions. For example, if the user is stressed, the automated unit's generating AI can lower the priority of low-priority emails. The automated unit can also raise the priority of high-priority emails if the user is relaxed. For example, if the automated unit's generating AI estimates the user's emotions and the user is relaxed, it can raise the priority of high-priority emails. Furthermore, if the user is in a hurry, the automated unit can set the generating AI to prioritize urgent emails. For example, if the automated unit's generating AI estimates the user's emotions and the user is in a hurry, it can set the generating AI to prioritize urgent emails. In this way, the automated unit can determine the priority of emails to generate according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generating AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the automation unit may be performed using a generation AI, or not. For example, the automation unit can use a generation AI to estimate the user's emotions and determine the priority of emails to generate based on the estimated user emotions.

[0072] The automation unit can prioritize the generation of highly relevant content by considering the user's geographical location information when generating regular emails. For example, the automation unit's generation AI can check the user's current location and include geographically relevant information in the email. The automation unit can also have the generation AI check the user's business trip destination and include information related to that destination in the email. Furthermore, the automation unit can have the generation AI check the user's business trip destination and include information related to that destination in the email. In addition, the automation unit can have the generation AI check the user's office location and include information related to the office in the email. This allows the automation unit to generate highly relevant content based on the user's geographical location information. Some or all of the above processing in the automation unit may be performed using, for example, the generation AI, or without the generation AI. For example, the automation unit can use the generation AI to prioritize the generation of highly relevant content by considering the user's geographical location information when generating regular emails.

[0073] The automation unit can analyze a user's social media activity and include relevant information in the email when generating scheduled emails. For example, the automation unit can use a generation AI to analyze a user's social media posts and include relevant topics in the email. The automation unit can also use a generation AI to check the number of a user's social media followers and include influential information in the email. Furthermore, the automation unit can use a generation AI to analyze a user's social media engagement and include information of high interest in the email. This allows the automation unit to include relevant information in the email based on the user's social media activity. Some or all of the above processing in the automation unit may be performed using a generation AI, or without one. For example, the automation unit can use a generation AI to analyze a user's social media activity and include relevant information in the email when generating scheduled emails.

[0074] The learning unit can estimate the user's emotions and select training data based on those estimated emotions. For example, if the user is feeling stressed, the learning unit can select training data that helps the generating AI relax. Similarly, if the user is relaxed, the learning unit can select training data that helps the generating AI concentrate. Furthermore, if the user is in a hurry, the learning unit can select training data that allows the generating AI to learn efficiently. This allows the learning unit to select training data according to the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or a generating AI with an emotion estimation function. The generative AI is, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the learning unit may be performed using a generative AI, or not. For example, the learning unit may use a generative AI to estimate the user's emotions and select training data based on the estimated user emotions.

[0075] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can have a generative AI analyze past learning data and select the optimal algorithm. The learning unit can also have a generative AI evaluate past learning results and adjust the algorithm parameters. Furthermore, the learning unit can have a generative AI identify areas for improvement in the algorithm based on past learning data. This allows the learning unit to optimize the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the learning unit can optimize the learning algorithm by referring to past learning data during the learning process using a generative AI.

[0076] The learning unit can improve accuracy by filtering training data based on specific business processes during training. For example, the learning unit can enable the generative AI to prioritize learning data related to specific business processes. The learning unit can also enable the generative AI to exclude unnecessary data based on specific business processes. Furthermore, the learning unit can enable the generative AI to evaluate the importance of data based on specific business processes. This allows the learning unit to filter training data based on specific business processes and improve accuracy. Some or all of the above processing in the learning unit may be performed using the generative AI, or not. For example, the learning unit can use the generative AI to filter training data based on specific business processes during training and improve accuracy.

[0077] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit can reduce the frequency of learning by the generative AI. For example, if the generative AI estimates the user's emotions and finds them stressed, the learning unit can reduce the learning frequency. The learning unit can also increase the frequency of learning by the generative AI if the user is relaxed. For example, if the generative AI estimates the user's emotions and finds them relaxed, the learning unit can increase the learning frequency. Furthermore, if the user is in a hurry, the learning unit can optimize the learning frequency by the generative AI. For example, if the generative AI estimates the user's emotions and finds them in a hurry, the learning unit can optimize the learning frequency. In this way, the learning unit can adjust the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the processing described above in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can use a generative AI to estimate the user's emotions and adjust the learning frequency based on the estimated user emotions.

[0078] The learning unit can weight the training data based on the submission timing of regular emails during training. For example, the learning unit can use a generative AI to weight important data based on the submission timing of regular emails. The learning unit can also use a generative AI to reduce the weight of unnecessary data based on the submission timing of regular emails. Furthermore, the learning unit can use a generative AI to set data priorities based on the submission timing of regular emails. In addition, the learning unit can use a generative AI to set data priorities based on the submission timing of regular emails. This allows the learning unit to weight the training data based on the submission timing of regular emails. Some or all of the above processing in the learning unit may be performed using a generative AI, or without using a generative AI. For example, the learning unit can use a generative AI to weight the training data based on the submission timing of regular emails during training.

[0079] The learning unit can analyze the user's work history during training and prioritize learning relevant data. For example, the learning unit can have a generative AI analyze the user's work history and prioritize learning relevant data. The learning unit can also have a generative AI identify important data based on the user's work history. Furthermore, the learning unit can have a generative AI refer to the user's work history and set the priority of the learning data. This allows the learning unit to prioritize learning relevant data based on the user's work history. Some or all of the above processing in the learning unit may be performed using a generative AI, or not. For example, the learning unit can use a generative AI to analyze the user's work history during training and prioritize learning relevant data.

[0080] The system builder can estimate the user's emotions and adjust the system build method based on the estimated emotions. For example, if the user is stressed, the system builder can use the generative AI to select a simple system build method. For example, if the generative AI estimates the user's emotions and they are stressed, the system builder can select a simple system build method. The system builder can also use the generative AI to select a more detailed system build method if the user is relaxed. For example, if the generative AI estimates the user's emotions and they are relaxed, the system builder can select a more detailed system build method. Furthermore, if the user is in a hurry, the system builder can use the generative AI to select a system build method that can be built quickly. For example, if the generative AI estimates the user's emotions and they are in a hurry, the system builder can select a system build method that can be built quickly. In this way, the system builder can adjust the system build method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the construction unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the construction unit can use a generative AI to estimate the user's emotions and adjust the system construction method based on the estimated user emotions.

[0081] The construction unit can select the optimal construction method by referring to past system construction history when building a system. For example, the construction unit can use a generation AI to analyze past system construction history and select the optimal method. The construction unit can also use a generation AI to evaluate past system construction results and identify areas for improvement in the methods. Furthermore, the construction unit can use a generation AI to evaluate past system construction results and identify areas for improvement in the methods. In addition, the construction unit can use a generation AI to select an efficient method based on past system construction history. This allows the construction unit to select the optimal construction method by referring to past system construction history. Some or all of the above-described processes in the construction unit may be performed using a generation AI, or without using a generation AI. For example, the construction unit can use a generation AI to select the optimal construction method by referring to past system construction history when building a system.

[0082] The construction unit can customize the system based on specific business processes during system construction. For example, the construction unit can use a generation AI to customize the system based on specific business processes. The construction unit can also use a generation AI to prioritize the construction of functions related to specific business processes. Furthermore, the construction unit can use a generation AI to adjust system settings based on specific business processes. This allows the construction unit to customize the system based on specific business processes. Some or all of the above-described processes in the construction unit may be performed using a generation AI, or not. For example, the construction unit can use a generation AI to customize the system based on specific business processes during system construction.

[0083] The build unit can estimate the user's emotions and determine system priorities based on those emotions. For example, if the user is stressed, the build unit can lower the priority of less important systems using its generative AI. For example, if the generative AI estimates the user's emotions and they are stressed, the build unit can lower the priority of less important systems. The build unit can also raise the priority of important systems if the user is relaxed. For example, if the generative AI estimates the user's emotions and they are relaxed, the build unit can raise the priority of important systems. Furthermore, if the user is in a hurry, the build unit can set urgent systems as the highest priority using its generative AI. For example, if the generative AI estimates the user's emotions and they are in a hurry, the build unit can set urgent systems as the highest priority. In this way, the build unit can determine system priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines 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-described processes in the construction unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the construction unit can use a generative AI to estimate the user's emotions and determine the system's priorities based on the estimated user emotions.

[0084] The construction unit can select the optimal construction method when building a system, taking into account the user's geographical location information. For example, the construction unit can use a generation AI to confirm the user's current location and select a geographically relevant method. The construction unit can also use a generation AI to confirm the user's business trip destination and select a method suitable for that destination. Furthermore, the construction unit can use a generation AI to confirm the user's business trip destination and select a method suitable for that destination. In addition, the construction unit can use a generation AI to confirm the user's office location and select a method suitable for that office. This allows the construction unit to select the optimal construction method based on the user's geographical location information. Some or all of the above-described processes in the construction unit may be performed using a generation AI, or without using a generation AI. For example, the construction unit can use a generation AI to select the optimal construction method when building a system, taking into account the user's geographical location information.

[0085] The system development unit can analyze users' social media activity and reflect relevant information in the system during system development. For example, the system development unit can use a generative AI to analyze users' social media posts and reflect relevant topics in the system. The system development unit can also use a generative AI to check the number of users' social media followers and reflect influential information in the system. Furthermore, the system development unit can use a generative AI to analyze users' social media engagement and reflect information of high interest in the system. This allows the system development unit to reflect relevant information in the system based on users' social media activity. Some or all of the above processing in the system development unit may be performed using a generative AI, or without one. For example, the system development unit can use a generative AI to analyze users' social media activity and reflect relevant information in the system during system development.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The automated unit can estimate the user's emotions and customize the email content based on those emotions. For example, if the user is stressed, the automated unit can use generative AI to generate an email containing encouraging or relaxing content. If the user is relaxed, the automated unit can use generative AI to generate an email containing more detailed information or prompting progress on tasks. Furthermore, if the user is in a hurry, the automated unit can use generative AI to generate an email containing only concise and essential information. In this way, the automated unit can customize the email content according to the user's emotions.

[0088] The automated unit can estimate the user's emotions and adjust the frequency of email sending based on those emotions. For example, if the user is stressed, the automated unit can use generative AI to reduce the frequency of email sending. Conversely, if the user is relaxed, the automated unit can use generative AI to increase the frequency of email sending. Furthermore, if the user is in a hurry, the automated unit can use generative AI to immediately send only important emails. In this way, the automated unit can adjust the frequency of email sending according to the user's emotions.

[0089] The automated unit can estimate the user's emotions and adjust the tone of the email based on those emotions. For example, if the user is stressed, the automated unit can use generative AI to generate an email with a gentle tone. If the user is relaxed, the automated unit can use generative AI to generate an email with a friendly tone. Furthermore, if the user is in a hurry, the automated unit can use generative AI to generate an email with a concise and direct tone. In this way, the automated unit can adjust the tone of the email according to the user's emotions.

[0090] The automated unit can estimate the user's emotions and personalize email content based on those emotions. For example, if the user is stressed, the automated unit can use generative AI to generate an email containing words of encouragement and suggestions for relaxation. If the user is relaxed, the automated unit can use generative AI to generate an email containing suggestions for the next steps or new tasks. Furthermore, if the user is in a hurry, the automated unit can use generative AI to generate an email containing only the most important information. In this way, the automated unit can personalize email content according to the user's emotions.

[0091] The automated unit can estimate the user's emotions and adjust the timing of email delivery based on those emotions. For example, if the user is stressed, the automated unit can use generative AI to delay the email delivery. If the user is relaxed, the automated unit can use generative AI to speed up the email delivery. Furthermore, if the user is in a hurry, the automated unit can use generative AI to send the email immediately. In this way, the automated unit can adjust the timing of email delivery according to the user's emotions.

[0092] The automation unit can analyze the user's work schedule and determine the optimal timing for sending emails. For example, it can refer to the user's calendar and avoid sending emails immediately before or after meetings. It can also consider the user's working hours and avoid sending emails outside of business hours. Furthermore, it can send important emails at the appropriate time to coincide with the user's project deadlines. In this way, the automation unit can determine the optimal timing for sending emails based on the user's work schedule.

[0093] The automation unit can analyze a user's past email history and select the optimal email format. For example, it can analyze past progress report emails and select the most frequently used format. It can also analyze past meeting reminder emails and select the most effective format. Furthermore, it can analyze past project update emails and select the most appropriate format. In this way, the automation unit can select the optimal email format based on past email history.

[0094] The automation unit can customize email content based on the user's work status. For example, it can check the user's current project progress and reflect the content in the email according to that progress. It can also check the user's current work status and reflect the content in the email according to the priority of the task. Furthermore, it can check the user's current task status and reflect the content in the email according to the progress of the task. In this way, the automation unit can customize email content based on the user's work status.

[0095] The automated system can prioritize generating highly relevant content by considering the user's geographical location. For example, it can check the user's current location and include geographically relevant information in emails. It can also check the user's business trip destination and include relevant information in emails. Furthermore, it can check the user's office location and include relevant information in emails. In this way, the automated system can generate highly relevant content based on the user's geographical location.

[0096] The automation unit can analyze users' social media activity and include relevant information in emails. For example, it can analyze users' social media posts and include relevant topics in emails. It can also check the number of users' social media followers and include influential information in emails. Furthermore, it can analyze users' social media engagement and include information of high interest in emails. In this way, the automation unit can include relevant information in emails based on users' social media activity.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The automation unit automates routine emails. For example, it trains the generation AI on regularly sent emails such as weekly progress report emails and meeting reminder emails. The generation AI analyzes the content of past email data using natural language processing technology and automatically generates emails. Step 2: The learning unit trains the script platform with the code generated by the automation unit. For example, the code generated by the generation AI is imported into the script platform and integrated with various services to achieve automation. This allows for the creation of scripts that automatically send emails based on specific conditions, or scripts that generate emails in a specific format. Step 3: The build unit constructs the system based on the code learned by the learning unit. For example, it can build a system that can generate and run its own code tailored to our needs. It can build a system based on specific functional and performance requirements.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the automation unit, learning unit, and construction unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the automation unit is implemented by the processor 46 of the smart device 14 and automatically generates regular emails using a generation AI. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and trains the script platform with the code generated by the generation AI. The construction unit is implemented by the control unit 46A of the smart device 14 and builds a system that can create and run its own code to meet our needs. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the automation unit, learning unit, and construction unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the automation unit is implemented by the processor 46 of the smart glasses 214 and automatically generates regular emails using a generation AI. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and trains the script platform with the code generated by the generation AI. The construction unit is implemented by the control unit 46A of the smart glasses 214 and builds a system that can create and run its own code to meet our needs. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the automation unit, learning unit, and construction unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the automation unit is implemented by the processor 46 of the headset terminal 314 and automatically generates regular emails using a generation AI. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and trains the script platform with the code generated by the generation AI. The construction unit is implemented by the control unit 46A of the headset terminal 314 and builds a system that can create and run its own code to meet our needs. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the automation unit, learning unit, and construction unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the automation unit is implemented by the processor 46 of the robot 414 and automatically generates regular emails using a generation AI. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and trains the script platform with the code generated by the generation AI. The construction unit is implemented by the control unit 46A of the robot 414 and builds a system that can create and operate its own code to meet our needs. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) The automation department automates the sending of regular emails, A learning unit that causes the script platform to learn the code generated by the automation unit, The system comprises a construction unit that constructs a system based on the code learned by the learning unit. A system characterized by the following features. (Note 2) The aforementioned automation unit, Learns the content of regular emails and automatically generates emails. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning unit, Train the scripting platform with the generated code. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned construction unit is We will build a system that can create and run its own code tailored to our needs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned construction unit is To build a company-wide unified automation system. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned construction unit is Implement measures against unofficial script platforms. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned construction unit is Perform script maintenance. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned automation unit, The system estimates the user's emotions and adjusts the timing of regular email generation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned automation unit, Analyze the content of regular emails and select the optimal email format based on past email history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned automation unit, When generating regular emails, customize the content based on the user's current work status and project progress. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned automation unit, It estimates the user's emotions and determines the priority of emails to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned automation unit, When generating regular emails, the system prioritizes generating highly relevant content by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned automation unit, When generating regular emails, we analyze users' social media activity and include relevant information in the emails. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning unit, During training, the training data is filtered based on specific business processes to improve accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning unit, During training, the training data is weighted based on the submission timing of regular emails. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning unit, During training, the system analyzes the user's work history and prioritizes learning relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned construction unit is The system estimates user emotions and adjusts how the system is built based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned construction unit is When building a system, the optimal construction method is selected by referring to past system construction history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned construction unit is During system development, the system is customized based on specific business processes. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned construction unit is The system estimates the user's emotions and determines system priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned construction unit is When building the system, the optimal construction method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned construction unit is During system development, analyze users' social media activity and incorporate relevant information into the system. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 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 automation department automates the sending of regular emails, A learning unit that causes the generation AI to learn the code generated by the automation unit, The system comprises a construction unit that constructs a system based on the code learned by the learning unit. A system characterized by the following features.

2. The aforementioned automation unit, Learns the content of regular emails and automatically generates emails. The system according to feature 1.

3. The aforementioned learning unit, The generated code is used to train the generation AI. The system according to feature 1.

4. The aforementioned construction unit is We will build a system that can create and run its own code tailored to our needs. The system according to feature 1.

5. The aforementioned construction unit is To build a company-wide unified automation system. The system according to feature 1.

6. The aforementioned construction unit is Perform script maintenance. The system according to feature 1.

7. The aforementioned automation unit, The system estimates the user's emotions and adjusts the timing of regular email generation based on those estimated emotions. The system according to feature 1.

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