system

Abstract

The system according to this embodiment aims to efficiently manage a large volume of email and chat communications and to respond to them with priority. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a reply unit, and a scheduling unit. The collection unit centralizes the contents of past emails and chats. The analysis unit learns the contents centralized by the collection unit and assigns priorities according to the recipient's position and situation. The reply unit automatically replies to emails that the analysis unit has determined to have low priority. The scheduling unit summarizes the contents of emails that the analysis unit has determined to have high priority and schedules requests with deadlines if any.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to efficiently manage a large number of email and chat exchanges and handle them with priorities.

[0005] The system according to the embodiment aims to efficiently manage a large number of email and chat exchanges and handle them with priorities.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a reply unit, and a scheduling unit. The collection unit centralizes the contents of past emails and chats. The analysis unit learns the contents centralized by the collection unit and prioritizes them according to the recipient's position and situation. The reply unit automatically replies to emails that the analysis unit has determined to have low priority. The scheduling unit summarizes the contents of emails that the analysis unit has determined to have high priority and schedules them if there are requests with deadlines. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage a large number of emails and chat messages and respond to them with priority. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example of form 1) The email and chat management system according to an embodiment of the present invention is a system that uses AI to centralize and efficiently manage the content of past emails and chats when users exchange many emails and chats on a daily basis. This system learns and centralizes the content of past emails and chats, making it easy to refer to past interactions. Furthermore, the AI ​​prioritizes emails and chats based on past interactions, according to the other party's position and situation. For example, important emails and chats are given high priority, enabling a quick response. In addition, the AI ​​automatically replies to emails that do not require the user's input. For example, the AI ​​automatically replies to emails and chats that require a standard response, saving the user time. Furthermore, the AI ​​also summarizes the content and schedules requests with deadlines. For example, for emails and chats that include requests with deadlines, the AI ​​automatically sets a schedule and notifies the user. This allows users to respond efficiently without missing important emails and chats. With this system, users only need to check emails and chats that have been summarized and prioritized by the AI, significantly reducing the time spent checking emails. For example, by reducing the time spent checking emails without missing important emails, overtime can be reduced. Furthermore, it eliminates the need to repeatedly review emails, allowing for more efficient work. This system is particularly useful for employees of large companies with over 1,000 employees, as it can solve the challenges that arise when dealing with a large volume of emails and chats daily. For example, it can automatically summarize and sort time-sensitive requests and high-priority emails that are not addressed to the user, and even automatically reply to emails that do not require user judgment. This allows users to receive notifications only for high-priority emails, while reviewing others all at once. It can also schedule time-sensitive requests, eliminating the need to review only the summarized content. This system is achieved by training an AI with emails and automatically flagging patterns that require a reply, high-priority emails, and emails that require action. This makes it possible for anyone to improve efficiency, significantly streamlining the daily email and chat review process for all employees.This allows the email and chat management system to centralize past emails and chat content, prioritize them, and automatically handle replies and scheduling, thereby streamlining email and chat management.

[0029] The email and chat management system according to this embodiment comprises a collection unit, an analysis unit, a reply unit, and a scheduling unit. The collection unit centralizes the content of past emails and chats. The collection unit can centralize emails and chats of various formats and types, such as business emails, personal chats, and group chats. The collection unit can centralize the content by methods such as integrating it into a database or standardizing the format. The analysis unit learns the content centralized by the collection unit and prioritizes it according to the recipient's position and situation. The analysis unit can determine priority based on criteria such as importance, urgency, and the sender's position. The analysis unit can use AI to analyze past interactions and assign priorities. The reply unit automatically replies to emails that the analysis unit has determined to have low priority. The reply unit can perform automatic replies by methods such as template replies or AI-generated reply content. The reply unit can use AI to automatically reply to emails and chats that require standardized replies. The scheduling unit summarizes the content of emails that the analysis unit has determined to have high priority and schedules them if there are time-sensitive requests. The scheduling unit can perform summaries based on factors such as the length of the text and the importance of the information being summarized. Using AI, the scheduling unit can summarize email and chat content and set schedules if deadlines are included. The scheduling unit can set schedules based on criteria such as how deadlines are set and how schedule notifications are sent. This allows the email and chat management system to streamline email and chat management by centralizing past email and chat content, prioritizing it, and automatically responding and scheduling.

[0030] The data collection unit centralizes past email and chat content. Specifically, it can centralize emails and chats of various formats and types, such as business emails, personal chats, and group chats. To integrate this data from different formats, the data collection unit uses methods such as database integration and formatting standardization. For example, business emails are usually stored on a company's mail server, personal chats are stored in messaging apps on smartphones and computers, and group chats are often stored in cloud-based chat services. The data collection unit collects data from these different storage locations and converts it into a unified format. Specifically, it retrieves emails from mail servers using IMAP or POP3 protocols, and retrieves chat data from messaging apps using APIs. From cloud-based chat services, it retrieves data using the provided export functions or APIs. This data is centrally managed by the data collection unit and stored in a database. The database contains metadata such as the sender, recipient, date and time, and content of emails and chats, and is designed to facilitate searching and analysis. Furthermore, the data collection unit eliminates data duplication and cleans the data as needed. This allows the data collection unit to efficiently centralize past email and chat content, providing a foundation for subsequent analysis, replies, and scheduling.

[0031] The analysis unit learns from the data centralized by the collection unit and prioritizes messages based on the recipient's position and situation. Specifically, it uses AI to analyze past interactions and determine priorities based on criteria such as importance, urgency, and the sender's job title. For example, importance is evaluated by analyzing keywords and phrases contained in the content of emails and chats. Urgency is determined based on information such as the date and time the email or chat was sent and the reply deadline. The sender's job title is identified based on the sender's email address and chat account information, with higher job titles receiving higher priority. The analysis unit comprehensively evaluates these criteria and assigns a priority to each email and chat. The AI ​​uses natural language processing technology to analyze the content of emails and chats and understand the context and intent. For example, emails related to important projects and chats requiring urgent attention are assigned high priority. On the other hand, routine notifications and advertising emails are assigned low priority. By learning the history of past interactions and understanding user behavior patterns and prioritization methods, the analysis unit can perform more accurate prioritization. This allows the analytics department to help users efficiently manage their emails and chats, ensuring they don't miss important messages.

[0032] The reply unit automatically responds to emails that the analysis unit has determined to be of low priority. Specifically, it can automatically respond using methods such as template replies and AI-generated response content. Template replies are a method of responding quickly using pre-set standard phrases. For example, for frequently asked questions, it can automatically respond using pre-prepared answers. AI-generated response content is a method of generating appropriate responses based on the content of emails and chats using natural language generation technology. The AI ​​can understand past interactions and context to generate appropriate response content. For example, for emails regarding scheduling meetings, the AI ​​can generate a response that suggests an appropriate date and time. The reply unit can efficiently automate responses by combining these methods. Furthermore, the reply unit can collect user feedback and continuously improve the accuracy and appropriateness of its responses. For example, if a user makes a correction to an automated response, the unit can learn from that correction and reflect it in future responses. This allows the reply unit to reduce the burden on users and respond to emails and chats efficiently.

[0033] The scheduling unit summarizes the content of emails deemed high-priority by the analysis unit and schedules them if there are time-sensitive requests. Specifically, it uses AI to summarize the content of emails and chats and sets schedules if time-sensitive requests are included. The AI ​​uses natural language processing technology to analyze the content of emails and chats and extract important information. For example, for a meeting invitation email, it can extract and summarize important information such as the date, time, location, and agenda of the meeting. If a time-sensitive request is included, the AI ​​identifies the deadline and reflects it in the schedule. Based on the extracted information, the scheduling unit adds the schedule to the user's calendar or task management system. For example, for a meeting invitation email, it can add the meeting to the calendar and set a reminder. For the task management system, it can add time-sensitive tasks and manage their progress. Furthermore, the scheduling unit can adjust the schedule by considering the priority of multiple appointments and tasks in order to optimize the user's schedule. In this way, the scheduling unit helps users manage their schedules efficiently and ensures that important appointments and tasks are not overlooked.

[0034] The collection unit can analyze the user's past email and chat collection history and select the optimal collection method. For example, the collection unit can suggest the optimal collection timing based on the time slots the user frequently collected in the past. The collection unit can also prioritize suggesting collection methods the user has used in the past (manual, scheduled collection, etc.). The collection unit can suggest the optimal collection method for specific days of the week or time slots based on the user's past collection history. This allows the optimal collection method to be selected by analyzing past collection history. Some or all of the above processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0035] The collection unit can filter emails and chats based on the user's current projects and areas of interest when collecting them. For example, the collection unit can prioritize collecting emails and chats related to projects the user is currently working on. The collection unit can also filter and collect emails and chats that are highly relevant based on the user's areas of interest. The collection unit can collect relevant emails and chats based on keywords set by the user. This allows for the priority collection of highly relevant information by filtering based on current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's project information and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0036] The collection unit can prioritize the collection of highly relevant content by considering the user's geographical location when collecting emails and chats. For example, if the user is in a specific region, the collection unit can prioritize the collection of emails and chats related to that region. If the user is on a business trip, the collection unit can prioritize the collection of emails and chats related to the destination. If the user is at home, the collection unit can prioritize the collection of emails and chats related to home. In this way, highly relevant information can be prioritized by considering geographical location. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant content.

[0037] The data collection unit can analyze a user's social media activity and collect relevant content when collecting emails and chats. For example, the data collection unit can prioritize collecting emails and chats related to topics mentioned by the user on social media. The data collection unit can also prioritize collecting emails and chats related to accounts that the user follows on social media. The data collection unit can also prioritize collecting emails and chats related to groups that the user participates in on social media. This allows for the priority collection of relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant content.

[0038] The analysis unit can improve the accuracy of priority by considering the relationships between emails and chats during analysis. For example, the analysis unit can determine priority by considering the relationship between the sender and recipient of an email or chat. The analysis unit can also analyze the relevance of the content of emails and chats and adjust the priority. The analysis unit can determine priority by considering the frequency of email and chat exchanges. In this way, the accuracy of priority can be improved by considering the relationships between emails and chats. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the relationship data of emails and chats into a generating AI and have the generating AI perform the improvement of priority accuracy.

[0039] The analysis unit can determine priority by considering the attribute information of the sender of emails and chats during analysis. For example, the analysis unit can set a high priority if the sender is a supervisor. It can also set a high priority if the sender is a customer. It can set a medium priority if the sender is a colleague. In this way, appropriate priorities can be set by considering the sender's attribute information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input sender attribute information data into a generating AI and have the generating AI perform the priority determination.

[0040] The analysis unit can determine priority by considering the geographical distribution of emails and chats during analysis. For example, the analysis unit can set a higher priority if the sender is nearby. The analysis unit can also set a lower priority if the sender is far away. If the sender is in a specific region, the analysis unit can set a higher priority for emails and chats related to that region. In this way, appropriate priorities can be set by considering geographical distribution. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical distribution data of emails and chats into a generating AI and have the generating AI perform the priority determination.

[0041] The analysis unit can improve the accuracy of priority by referring to relevant literature in emails and chats during analysis. For example, the analysis unit can refer to literature related to the content of emails and chats and adjust the priority. The analysis unit can also determine priority based on literature cited by the sender of the email or chat. The analysis unit can refer to research papers related to the content of emails and chats and adjust the priority. In this way, the accuracy of priority can be improved by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of priority accuracy.

[0042] The reply function can adjust the level of detail in replies based on the importance of the email or chat. For example, it can provide detailed replies to high-priority emails and chats, concise replies to low-priority emails and chats, and quick replies to urgent emails and chats. By adjusting the level of detail in replies based on importance, appropriate replies can be provided. Some or all of the above processing in the reply function may be performed using AI, for example, or not. For example, the reply function can input email and chat importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in replies.

[0043] The reply function can apply different reply algorithms depending on the category of the email or chat when replying. For example, the reply function can apply a formal reply algorithm to business-related emails and chats. It can also apply a casual reply algorithm to private emails and chats. It can also apply a professional reply algorithm to technical emails and chats. This allows for appropriate replies by applying different reply algorithms depending on the category. Some or all of the above processing in the reply function may be performed using AI, for example, or not using AI. For example, the reply function can input email and chat category data into a generating AI and have the generating AI perform the application of the reply algorithm.

[0044] The reply function can prioritize replies based on when the email or chat was sent. For example, it can prioritize emails and chats that were sent recently. It can also postpone emails and chats that were sent earlier. It can also prioritize emails and chats that are urgent. This allows for appropriate replies by prioritizing replies based on when they were sent. Some or all of the above processing in the reply function may be performed using AI, for example, or not. For example, the reply function can input email and chat sending time data into a generating AI and have the generating AI determine the priority of replies.

[0045] The reply function can adjust the order of replies based on the relevance of emails and chats. For example, it can prioritize replying to highly relevant emails and chats. It can also postpone replying to less relevant emails and chats. The reply function can reply to multiple emails and chats related to the same topic together. This allows for appropriate replies by adjusting the order of replies based on relevance. Some or all of the above processing in the reply function may be performed using AI, or not. For example, the reply function can input email and chat relevance data into a generating AI and have the generating AI adjust the order of replies.

[0046] The scheduling unit can analyze the content of emails and chats to set the optimal schedule during scheduling. For example, the scheduling unit can suggest the optimal schedule based on the content of emails and chats. The scheduling unit can also set a schedule based on the deadline of emails and chats. The scheduling unit can set a schedule based on the importance of emails and chats. In this way, the optimal schedule can be set by analyzing the content of emails and chats. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input email and chat content data into a generating AI and have the generating AI execute the setting of the optimal schedule.

[0047] The scheduling unit can provide an optimal schedule by referring to the user's past schedule history during scheduling. For example, the scheduling unit can propose an optimal schedule based on schedules previously set by the user. The scheduling unit can also provide an optimal schedule by analyzing specific patterns from the user's past schedule history. The scheduling unit can set schedules in a way that avoids duplication by referring to the user's past schedule history. In this way, an optimal schedule can be provided by referring to past schedule history. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without using AI. For example, the scheduling unit can input the user's past schedule history data into a generating AI and have the generating AI perform the task of providing an optimal schedule.

[0048] The scheduling unit can weight schedules based on the submission dates of emails and chats during scheduling. For example, the scheduling unit can prioritize scheduling emails and chats that have been submitted recently. The scheduling unit can also postpone scheduling emails and chats that have been submitted older. The scheduling unit can also prioritize scheduling emails and chats that are urgent. In this way, an appropriate schedule can be set by weighting schedules based on submission dates. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input email and chat submission date data into a generating AI and have the generating AI perform the schedule weighting.

[0049] The scheduling unit can provide an optimal schedule by considering the user's device information during scheduling. For example, if the user is using a smartphone, the scheduling unit can set an optimal schedule for the smartphone's calendar. If the user is using a tablet, the scheduling unit can set an optimal schedule for the tablet's calendar. If the user is using a desktop, the scheduling unit can set an optimal schedule for the desktop's calendar. In this way, an optimal schedule can be provided by considering device information. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input user device information data into a generating AI and have the generating AI perform the task of providing an optimal schedule.

[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 collection unit can analyze a user's past collection history when collecting emails and chats, and select the optimal collection method. For example, it can suggest the optimal collection timing based on the time slots the user frequently collected in the past. The collection unit can also prioritize suggesting collection methods the user has used in the past (manual, scheduled collection, etc.). Based on the user's past collection history, the collection unit can suggest the optimal collection method for specific days of the week or time slots. In this way, the optimal collection method can be selected by analyzing past collection history.

[0052] The collection unit can filter emails and chats based on the user's current projects and areas of interest. For example, it can prioritize collecting emails and chats related to projects the user is currently working on. The collection unit can also filter and collect highly relevant emails and chats based on the user's areas of interest. The collection unit can collect relevant emails and chats based on keywords set by the user. This allows for the priority collection of highly relevant information by filtering based on current projects and areas of interest.

[0053] The analysis unit can improve the accuracy of priority by considering the relationships between emails and chats during analysis. For example, it can determine priority by considering the relationship between the sender and recipient of an email or chat. The analysis unit can also analyze the relevance of the content of emails and chats and adjust the priority accordingly. The analysis unit can determine priority by considering the frequency of email and chat exchanges. In this way, the accuracy of priority can be improved by considering the relationships between emails and chats.

[0054] The analysis unit can determine priority during analysis by considering the attribute information of the sender of emails and chats. For example, if the sender is a supervisor, it can be given a high priority. The analysis unit can also give a high priority if the sender is a customer. If the sender is a colleague, it can be given a medium priority. In this way, appropriate priorities can be set by considering the sender's attribute information.

[0055] The scheduling unit can provide the optimal schedule by referring to the user's past schedule history during scheduling. For example, it can suggest the optimal schedule based on schedules the user has set in the past. The scheduling unit can also analyze specific patterns from the user's past schedule history and provide the optimal schedule. The scheduling unit can set schedules to avoid duplication by referring to the user's past schedule history. In this way, it can provide the optimal schedule by referring to past schedule history.

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

[0057] Step 1: The collection unit centralizes the content of past emails and chats. The collection unit can centralize emails and chats of various formats and types, such as business emails, personal chats, and group chats. The collection unit centralizes the data through methods such as integrating it into a database and standardizing the format. Step 2: The analysis unit learns from the data centralized by the collection unit and prioritizes messages based on the recipient's position and circumstances. The analysis unit determines priorities based on criteria such as importance, urgency, and the sender's position. The analysis unit uses AI to analyze past interactions and prioritize them. Step 3: The reply unit automatically replies to emails that the analysis unit has determined to have low priority. The reply unit automatically replies using methods such as template replies and AI-generated reply content. The reply unit uses AI to automatically reply to emails and chats that require a standardized response. Step 4: The scheduling unit summarizes the content of emails that the analysis unit has determined to be high priority, and schedules any time-sensitive requests. The scheduling unit summarizes based on the length of the text and the importance of the information being summarized. The scheduling unit uses AI to summarize the content of emails and chats, and sets a schedule if time-sensitive requests are included. The scheduling unit sets a schedule based on criteria such as how deadlines are set and how schedules are notified.

[0058] (Example of form 2) The email and chat management system according to an embodiment of the present invention is a system that uses AI to centralize and efficiently manage the content of past emails and chats when users exchange many emails and chats on a daily basis. This system learns and centralizes the content of past emails and chats, making it easy to refer to past interactions. Furthermore, the AI ​​prioritizes emails and chats based on past interactions, according to the other party's position and situation. For example, important emails and chats are given high priority, enabling a quick response. In addition, the AI ​​automatically replies to emails that do not require the user's input. For example, the AI ​​automatically replies to emails and chats that require a standard response, saving the user time. Furthermore, the AI ​​also summarizes the content and schedules requests with deadlines. For example, for emails and chats that include requests with deadlines, the AI ​​automatically sets a schedule and notifies the user. This allows users to respond efficiently without missing important emails and chats. With this system, users only need to check emails and chats that have been summarized and prioritized by the AI, significantly reducing the time spent checking emails. For example, by reducing the time spent checking emails without missing important emails, overtime can be reduced. Furthermore, it eliminates the need to repeatedly review emails, allowing for more efficient work. This system is particularly useful for employees of large companies with over 1,000 employees, as it can solve the challenges that arise when dealing with a large volume of emails and chats daily. For example, it can automatically summarize and sort time-sensitive requests and high-priority emails that are not addressed to the user, and even automatically reply to emails that do not require user judgment. This allows users to receive notifications only for high-priority emails, while reviewing others all at once. It can also schedule time-sensitive requests, eliminating the need to review only the summarized content. This system is achieved by training an AI with emails and automatically flagging patterns that require a reply, high-priority emails, and emails that require action. This makes it possible for anyone to improve efficiency, significantly streamlining the daily email and chat review process for all employees.This allows the email and chat management system to centralize past emails and chat content, prioritize them, and automatically handle replies and scheduling, thereby streamlining email and chat management.

[0059] The email and chat management system according to this embodiment comprises a collection unit, an analysis unit, a reply unit, and a scheduling unit. The collection unit centralizes the content of past emails and chats. The collection unit can centralize emails and chats of various formats and types, such as business emails, personal chats, and group chats. The collection unit can centralize the content by methods such as integrating it into a database or standardizing the format. The analysis unit learns the content centralized by the collection unit and prioritizes it according to the recipient's position and situation. The analysis unit can determine priority based on criteria such as importance, urgency, and the sender's position. The analysis unit can use AI to analyze past interactions and assign priorities. The reply unit automatically replies to emails that the analysis unit has determined to have low priority. The reply unit can perform automatic replies by methods such as template replies or AI-generated reply content. The reply unit can use AI to automatically reply to emails and chats that require standardized replies. The scheduling unit summarizes the content of emails that the analysis unit has determined to have high priority and schedules them if there are time-sensitive requests. The scheduling unit can perform summaries based on factors such as the length of the text and the importance of the information being summarized. Using AI, the scheduling unit can summarize email and chat content and set schedules if deadlines are included. The scheduling unit can set schedules based on criteria such as how deadlines are set and how schedule notifications are sent. This allows the email and chat management system to streamline email and chat management by centralizing past email and chat content, prioritizing it, and automatically responding and scheduling.

[0060] The data collection unit centralizes past email and chat content. Specifically, it can centralize emails and chats of various formats and types, such as business emails, personal chats, and group chats. To integrate this data from different formats, the data collection unit uses methods such as database integration and formatting standardization. For example, business emails are usually stored on a company's mail server, personal chats are stored in messaging apps on smartphones and computers, and group chats are often stored in cloud-based chat services. The data collection unit collects data from these different storage locations and converts it into a unified format. Specifically, it retrieves emails from mail servers using IMAP or POP3 protocols, and retrieves chat data from messaging apps using APIs. From cloud-based chat services, it retrieves data using the provided export functions or APIs. This data is centrally managed by the data collection unit and stored in a database. The database contains metadata such as the sender, recipient, date and time, and content of emails and chats, and is designed to facilitate searching and analysis. Furthermore, the data collection unit eliminates data duplication and cleans the data as needed. This allows the data collection unit to efficiently centralize past email and chat content, providing a foundation for subsequent analysis, replies, and scheduling.

[0061] The analysis unit learns from the data centralized by the collection unit and prioritizes messages based on the recipient's position and situation. Specifically, it uses AI to analyze past interactions and determine priorities based on criteria such as importance, urgency, and the sender's job title. For example, importance is evaluated by analyzing keywords and phrases contained in the content of emails and chats. Urgency is determined based on information such as the date and time the email or chat was sent and the reply deadline. The sender's job title is identified based on the sender's email address and chat account information, with higher job titles receiving higher priority. The analysis unit comprehensively evaluates these criteria and assigns a priority to each email and chat. The AI ​​uses natural language processing technology to analyze the content of emails and chats and understand the context and intent. For example, emails related to important projects and chats requiring urgent attention are assigned high priority. On the other hand, routine notifications and advertising emails are assigned low priority. By learning the history of past interactions and understanding user behavior patterns and prioritization methods, the analysis unit can perform more accurate prioritization. This allows the analytics department to help users efficiently manage their emails and chats, ensuring they don't miss important messages.

[0062] The reply unit automatically responds to emails that the analysis unit has determined to be of low priority. Specifically, it can automatically respond using methods such as template replies and AI-generated response content. Template replies are a method of responding quickly using pre-set standard phrases. For example, for frequently asked questions, it can automatically respond using pre-prepared answers. AI-generated response content is a method of generating appropriate responses based on the content of emails and chats using natural language generation technology. The AI ​​can understand past interactions and context to generate appropriate response content. For example, for emails regarding scheduling meetings, the AI ​​can generate a response that suggests an appropriate date and time. The reply unit can efficiently automate responses by combining these methods. Furthermore, the reply unit can collect user feedback and continuously improve the accuracy and appropriateness of its responses. For example, if a user makes a correction to an automated response, the unit can learn from that correction and reflect it in future responses. This allows the reply unit to reduce the burden on users and respond to emails and chats efficiently.

[0063] The scheduling unit summarizes the content of emails deemed high-priority by the analysis unit and schedules them if there are time-sensitive requests. Specifically, it uses AI to summarize the content of emails and chats and sets schedules if time-sensitive requests are included. The AI ​​uses natural language processing technology to analyze the content of emails and chats and extract important information. For example, for a meeting invitation email, it can extract and summarize important information such as the date, time, location, and agenda of the meeting. If a time-sensitive request is included, the AI ​​identifies the deadline and reflects it in the schedule. Based on the extracted information, the scheduling unit adds the schedule to the user's calendar or task management system. For example, for a meeting invitation email, it can add the meeting to the calendar and set a reminder. For the task management system, it can add time-sensitive tasks and manage their progress. Furthermore, the scheduling unit can adjust the schedule by considering the priority of multiple appointments and tasks in order to optimize the user's schedule. In this way, the scheduling unit helps users manage their schedules efficiently and ensures that important appointments and tasks are not overlooked.

[0064] The collection unit can estimate the user's emotions and adjust the timing of email and chat collection based on the estimated emotions. For example, if the user is stressed, the collection unit can delay collection and collect when the user is relaxed. If the user is busy, the collection unit can adjust the collection timing to collect during a time when the user has free time. If the user is relaxed, the collection unit can perform urgent collection and provide information quickly. This reduces the burden on the user by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not using AI. For example, the collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0065] The collection unit can analyze the user's past email and chat collection history and select the optimal collection method. For example, the collection unit can suggest the optimal collection timing based on the time slots the user frequently collected in the past. The collection unit can also prioritize suggesting collection methods the user has used in the past (manual, scheduled collection, etc.). The collection unit can suggest the optimal collection method for specific days of the week or time slots based on the user's past collection history. This allows the optimal collection method to be selected by analyzing past collection history. Some or all of the above processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0066] The collection unit can filter emails and chats based on the user's current projects and areas of interest when collecting them. For example, the collection unit can prioritize collecting emails and chats related to projects the user is currently working on. The collection unit can also filter and collect emails and chats that are highly relevant based on the user's areas of interest. The collection unit can collect relevant emails and chats based on keywords set by the user. This allows for the priority collection of highly relevant information by filtering based on current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's project information and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0067] The collection unit can estimate the user's emotions and determine the priority of emails and chats to collect based on the estimated emotions. For example, if the user is stressed, the collection unit can postpone collecting less important emails and chats. If the user is relaxed, the collection unit can prioritize collecting more important emails and chats. If the user is busy, the collection unit can prioritize collecting urgent emails and chats. This allows for the priority collection of important information by determining the priority of emails and chats to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0068] The collection unit can prioritize the collection of highly relevant content by considering the user's geographical location when collecting emails and chats. For example, if the user is in a specific region, the collection unit can prioritize the collection of emails and chats related to that region. If the user is on a business trip, the collection unit can prioritize the collection of emails and chats related to the destination. If the user is at home, the collection unit can prioritize the collection of emails and chats related to home. In this way, highly relevant information can be prioritized by considering geographical location. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant content.

[0069] The data collection unit can analyze a user's social media activity and collect relevant content when collecting emails and chats. For example, the data collection unit can prioritize collecting emails and chats related to topics mentioned by the user on social media. The data collection unit can also prioritize collecting emails and chats related to accounts that the user follows on social media. The data collection unit can also prioritize collecting emails and chats related to groups that the user participates in on social media. This allows for the priority collection of relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant content.

[0070] The analysis unit can estimate the user's emotions and adjust the priority criteria based on the estimated emotions. For example, if the user is stressed, the analysis unit can lower the priority of low-priority emails and chats. If the user is relaxed, the analysis unit can raise the priority of high-priority emails and chats. If the user is busy, the analysis unit can raise the priority of urgent emails and chats. In this way, appropriate priorities can be set by adjusting the priority criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the priority criteria.

[0071] The analysis unit can improve the accuracy of priority by considering the relationships between emails and chats during analysis. For example, the analysis unit can determine priority by considering the relationship between the sender and recipient of an email or chat. The analysis unit can also analyze the relevance of the content of emails and chats and adjust the priority. The analysis unit can determine priority by considering the frequency of email and chat exchanges. In this way, the accuracy of priority can be improved by considering the relationships between emails and chats. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the relationship data of emails and chats into a generating AI and have the generating AI perform the improvement of priority accuracy.

[0072] The analysis unit can determine priority by considering the attribute information of the sender of emails and chats during analysis. For example, the analysis unit can set a high priority if the sender is a supervisor. It can also set a high priority if the sender is a customer. It can set a medium priority if the sender is a colleague. In this way, appropriate priorities can be set by considering the sender's attribute information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input sender attribute information data into a generating AI and have the generating AI perform the priority determination.

[0073] The analysis unit can estimate the user's emotions and adjust the order in which priority results are displayed based on the estimated user emotions. For example, if the user is stressed, the analysis unit can postpone displaying less important emails or chats. If the user is relaxed, the analysis unit can prioritize displaying more important emails or chats. If the user is busy, the analysis unit can prioritize displaying more urgent emails or chats. In this way, important information can be prioritized by adjusting the display order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display order.

[0074] The analysis unit can determine priority by considering the geographical distribution of emails and chats during analysis. For example, the analysis unit can set a higher priority if the sender is nearby. The analysis unit can also set a lower priority if the sender is far away. If the sender is in a specific region, the analysis unit can set a higher priority for emails and chats related to that region. In this way, appropriate priorities can be set by considering geographical distribution. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical distribution data of emails and chats into a generating AI and have the generating AI perform the priority determination.

[0075] The analysis unit can improve the accuracy of priority by referring to relevant literature in emails and chats during analysis. For example, the analysis unit can refer to literature related to the content of emails and chats and adjust the priority. The analysis unit can also determine priority based on literature cited by the sender of the email or chat. The analysis unit can refer to research papers related to the content of emails and chats and adjust the priority. In this way, the accuracy of priority can be improved by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of priority accuracy.

[0076] The reply unit can estimate the user's emotions and adjust the way it expresses its reply based on those emotions. For example, if the user is stressed, the reply unit can provide a concise and clear reply. If the user is relaxed, the reply unit can provide a detailed reply. If the user is busy, the reply unit can provide a quick and concise reply. This allows for appropriate replies by adjusting the way the reply is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reply unit may be performed using AI or not. For example, the reply unit can input user emotion data into a generative AI and have the generative AI adjust the way the reply is expressed.

[0077] The reply function can adjust the level of detail in replies based on the importance of the email or chat. For example, it can provide detailed replies to high-priority emails and chats, concise replies to low-priority emails and chats, and quick replies to urgent emails and chats. By adjusting the level of detail in replies based on importance, appropriate replies can be provided. Some or all of the above processing in the reply function may be performed using AI, for example, or not. For example, the reply function can input email and chat importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in replies.

[0078] The reply function can apply different reply algorithms depending on the category of the email or chat when replying. For example, the reply function can apply a formal reply algorithm to business-related emails and chats. It can also apply a casual reply algorithm to private emails and chats. It can also apply a professional reply algorithm to technical emails and chats. This allows for appropriate replies by applying different reply algorithms depending on the category. Some or all of the above processing in the reply function may be performed using AI, for example, or not using AI. For example, the reply function can input email and chat category data into a generating AI and have the generating AI perform the application of the reply algorithm.

[0079] The reply unit can estimate the user's emotions and adjust the length of the reply based on the estimated emotions. For example, if the user is stressed, the reply unit can provide a short, to-the-point reply. If the user is relaxed, the reply unit can provide a detailed reply. If the user is busy, the reply unit can provide a concise and quick reply. This allows for appropriate replies by adjusting the length of the reply according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reply unit may be performed using AI or not. For example, the reply unit can input user emotion data into a generative AI and have the generative AI adjust the length of the reply.

[0080] The reply function can prioritize replies based on when the email or chat was sent. For example, it can prioritize emails and chats that were sent recently. It can also postpone emails and chats that were sent earlier. It can also prioritize emails and chats that are urgent. This allows for appropriate replies by prioritizing replies based on when they were sent. Some or all of the above processing in the reply function may be performed using AI, for example, or not. For example, the reply function can input email and chat sending time data into a generating AI and have the generating AI determine the priority of replies.

[0081] The reply function can adjust the order of replies based on the relevance of emails and chats. For example, it can prioritize replying to highly relevant emails and chats. It can also postpone replying to less relevant emails and chats. The reply function can reply to multiple emails and chats related to the same topic together. This allows for appropriate replies by adjusting the order of replies based on relevance. Some or all of the above processing in the reply function may be performed using AI, or not. For example, the reply function can input email and chat relevance data into a generating AI and have the generating AI adjust the order of replies.

[0082] The scheduling unit can estimate the user's emotions and adjust the scheduling method based on the estimated emotions. For example, if the user is stressed, the scheduling unit can set a looser schedule. If the user is relaxed, the scheduling unit can set a more detailed schedule. If the user is busy, the scheduling unit can set a simpler schedule. This allows for the setting of an appropriate schedule by adjusting the scheduling method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the scheduling unit may be performed using AI, or not using AI. For example, the scheduling unit can input user emotion data into a generative AI and have the generative AI adjust the scheduling method.

[0083] The scheduling unit can analyze the content of emails and chats to set the optimal schedule during scheduling. For example, the scheduling unit can suggest the optimal schedule based on the content of emails and chats. The scheduling unit can also set a schedule based on the deadline of emails and chats. The scheduling unit can set a schedule based on the importance of emails and chats. In this way, the optimal schedule can be set by analyzing the content of emails and chats. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input email and chat content data into a generating AI and have the generating AI execute the setting of the optimal schedule.

[0084] The scheduling unit can provide an optimal schedule by referring to the user's past schedule history during scheduling. For example, the scheduling unit can propose an optimal schedule based on schedules previously set by the user. The scheduling unit can also provide an optimal schedule by analyzing specific patterns from the user's past schedule history. The scheduling unit can set schedules in a way that avoids duplication by referring to the user's past schedule history. In this way, an optimal schedule can be provided by referring to past schedule history. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without using AI. For example, the scheduling unit can input the user's past schedule history data into a generating AI and have the generating AI perform the task of providing an optimal schedule.

[0085] The scheduling unit can estimate the user's emotions and determine scheduling priorities based on those emotions. For example, if the user is stressed, the scheduling unit can postpone less important schedules. If the user is relaxed, the scheduling unit can prioritize more important schedules. If the user is busy, the scheduling unit can prioritize more urgent schedules. This allows for the setting of an appropriate schedule by determining scheduling priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input user emotion data into a generative AI and have the generative AI determine scheduling priorities.

[0086] The scheduling unit can weight schedules based on the submission dates of emails and chats during scheduling. For example, the scheduling unit can prioritize scheduling emails and chats that have been submitted recently. The scheduling unit can also postpone scheduling emails and chats that have been submitted older. The scheduling unit can also prioritize scheduling emails and chats that are urgent. In this way, an appropriate schedule can be set by weighting schedules based on submission dates. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input email and chat submission date data into a generating AI and have the generating AI perform the schedule weighting.

[0087] The scheduling unit can provide an optimal schedule by considering the user's device information during scheduling. For example, if the user is using a smartphone, the scheduling unit can set an optimal schedule for the smartphone's calendar. If the user is using a tablet, the scheduling unit can set an optimal schedule for the tablet's calendar. If the user is using a desktop, the scheduling unit can set an optimal schedule for the desktop's calendar. In this way, an optimal schedule can be provided by considering device information. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input user device information data into a generating AI and have the generating AI perform the task of providing an optimal schedule.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The analytics unit can estimate the user's emotions and classify the content of emails and chats based on those estimated emotions. For example, if a user is stressed, the analytics unit can classify low-priority emails and chats into the "Check Later" category. If a user is relaxed, the analytics unit can classify high-priority emails and chats into the "Respond Now" category. If a user is busy, the analytics unit can classify urgent emails and chats into the "Priority" category. By appropriately classifying the content of emails and chats according to the user's emotions, the burden on the user can be reduced.

[0090] The data collection unit can estimate the user's emotions and adjust the frequency of email and chat collection based on the estimated emotions. For example, if the user is stressed, the collection unit can reduce the collection frequency and collect information when the user is relaxed. If the user is relaxed, the collection unit can increase the collection frequency to provide information quickly. If the user is busy, the collection unit can adjust the collection frequency and collect information during times when the user has free time. In this way, the burden on the user can be reduced by adjusting the collection frequency according to the user's emotions.

[0091] The analytics unit can analyze the content of emails and chats and adjust the timing of replies based on the user's emotions. For example, if the user is stressed, the analytics unit can delay its reply until the user is relaxed. If the user is relaxed, the analytics unit can also reply quickly. If the user is busy, the analytics unit can prioritize replying to urgent emails and chats. This allows for appropriate replies by adjusting the timing of responses according to the user's emotions.

[0092] The scheduling unit can estimate the user's emotions and adjust the way schedule notifications are sent based on those emotions. For example, if the user is stressed, the scheduling unit can send notifications sparingly, and send notifications when the user is relaxed. If the user is relaxed, the scheduling unit can also send detailed notifications. If the user is busy, the scheduling unit can prioritize only important notifications. This allows for proper schedule management by adjusting notification methods according to the user's emotions.

[0093] The reply function can estimate the user's emotions and adjust the content of the reply based on those emotions. For example, if the user is stressed, the reply function can provide a concise and clear response. If the user is relaxed, the reply function can provide a more detailed response. If the user is busy, the reply function can provide a quick and concise response. This allows for appropriate responses by adjusting the content of the reply according to the user's emotions.

[0094] The collection unit can analyze a user's past collection history when collecting emails and chats, and select the optimal collection method. For example, it can suggest the optimal collection timing based on the time slots the user frequently collected in the past. The collection unit can also prioritize suggesting collection methods the user has used in the past (manual, scheduled collection, etc.). Based on the user's past collection history, the collection unit can suggest the optimal collection method for specific days of the week or time slots. In this way, the optimal collection method can be selected by analyzing past collection history.

[0095] The collection unit can filter emails and chats based on the user's current projects and areas of interest. For example, it can prioritize collecting emails and chats related to projects the user is currently working on. The collection unit can also filter and collect highly relevant emails and chats based on the user's areas of interest. The collection unit can collect relevant emails and chats based on keywords set by the user. This allows for the priority collection of highly relevant information by filtering based on current projects and areas of interest.

[0096] The analysis unit can improve the accuracy of priority by considering the relationships between emails and chats during analysis. For example, it can determine priority by considering the relationship between the sender and recipient of an email or chat. The analysis unit can also analyze the relevance of the content of emails and chats and adjust the priority accordingly. The analysis unit can determine priority by considering the frequency of email and chat exchanges. In this way, the accuracy of priority can be improved by considering the relationships between emails and chats.

[0097] The analysis unit can determine priority during analysis by considering the attribute information of the sender of emails and chats. For example, if the sender is a supervisor, it can be given a high priority. The analysis unit can also give a high priority if the sender is a customer. If the sender is a colleague, it can be given a medium priority. In this way, appropriate priorities can be set by considering the sender's attribute information.

[0098] The scheduling unit can provide the optimal schedule by referring to the user's past schedule history during scheduling. For example, it can suggest the optimal schedule based on schedules the user has set in the past. The scheduling unit can also analyze specific patterns from the user's past schedule history and provide the optimal schedule. The scheduling unit can set schedules to avoid duplication by referring to the user's past schedule history. In this way, it can provide the optimal schedule by referring to past schedule history.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The collection unit centralizes the content of past emails and chats. The collection unit can centralize emails and chats of various formats and types, such as business emails, personal chats, and group chats. The collection unit centralizes the data through methods such as integrating it into a database and standardizing the format. Step 2: The analysis unit learns from the data centralized by the collection unit and prioritizes messages based on the recipient's position and circumstances. The analysis unit determines priorities based on criteria such as importance, urgency, and the sender's position. The analysis unit uses AI to analyze past interactions and prioritize them. Step 3: The reply unit automatically replies to emails that the analysis unit has determined to have low priority. The reply unit automatically replies using methods such as template replies and AI-generated reply content. The reply unit uses AI to automatically reply to emails and chats that require a standardized response. Step 4: The scheduling unit summarizes the content of emails that the analysis unit has determined to be high priority, and schedules any time-sensitive requests. The scheduling unit summarizes based on the length of the text and the importance of the information being summarized. The scheduling unit uses AI to summarize the content of emails and chats, and sets a schedule if time-sensitive requests are included. The scheduling unit sets a schedule based on criteria such as how deadlines are set and how schedules are notified.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] Each of the multiple elements described above, including the collection unit, analysis unit, reply unit, and scheduling unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and centralizes the contents of past emails and chats. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and learns the contents centralized by the collection unit and assigns priorities. The reply unit is implemented by the control unit 46A of the smart device 14 and automatically replies to emails that the analysis unit has determined to have low priority. The scheduling unit is implemented by the identification processing unit 290 of the data processing unit 12 and summarizes the contents of emails that have been determined to have high priority and sets a schedule. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the collection unit, analysis unit, reply unit, and scheduling unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart glasses 214 and centralizes the contents of past emails and chats. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and learns the contents centralized by the collection unit and assigns priorities. The reply unit is implemented by the control unit 46A of the smart glasses 214 and automatically replies to emails that the analysis unit has determined to have low priority. The scheduling unit is implemented by the identification processing unit 290 of the data processing unit 12 and summarizes the contents of emails that have been determined to have high priority and sets a schedule. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the collection unit, analysis unit, reply unit, and scheduling unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and centralizes the contents of past emails and chats. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which learns the contents centralized by the collection unit and assigns priorities. The reply unit is implemented by the control unit 46A of the headset terminal 314 and automatically replies to emails that the analysis unit has determined to have low priority. The scheduling unit is implemented by the identification processing unit 290 of the data processing unit 12, which summarizes the contents of emails that have been determined to have high priority and sets a schedule. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the collection unit, analysis unit, reply unit, and scheduling unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and centralizes the contents of past emails and chats. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which learns the contents centralized by the collection unit and assigns priorities. The reply unit is implemented by the control unit 46A of the robot 414 and automatically replies to emails that the analysis unit has determined to have low priority. The scheduling unit is implemented by the identification processing unit 290 of the data processing unit 12, which summarizes the contents of emails that have been determined to have high priority and sets a schedule. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] (Note 1) A collection department that centralizes the contents of past emails and chats, The analysis unit learns the information centralized by the aforementioned collection unit and prioritizes it according to the other party's position and situation, A reply unit that automatically sends replies to emails that the analysis unit has determined to have low priority, The system includes a scheduling unit that summarizes the content of emails that the analysis unit has determined to be of high priority, and schedules requests with deadlines if any. A system characterized by the following features. (Note 2) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of email and chat collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is We analyze the user's past email and chat history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting emails and chats, filter them based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and determines the priority of emails and chats to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting emails and chat messages, the system prioritizes collecting highly relevant content by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting emails and chats, the system analyzes users' social media activity and collects relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts the priority criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, we improve the accuracy of prioritization by considering the interrelationships between emails and chats. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, priority is determined by considering the attribute information of the email and chat sender. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's sentiment and adjusts the order in which priority results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, priority is determined by considering the geographical distribution of emails and chats. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, we improve the accuracy of prioritization by referring to relevant literature in emails and chats. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reply section is, It estimates the user's emotions and adjusts the way it expresses its replies based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reply section is, When replying, adjust the level of detail in your response based on the importance of the email or chat. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reply section is, When replying, different reply algorithms are applied depending on the category of the email or chat. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reply section is, It estimates the user's emotions and adjusts the length of the reply based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reply section is, When replying, prioritize responses based on when the email or chat message was sent. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reply section is, When replying, the order of replies will be adjusted based on the relevance of the email or chat. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned scheduling unit, It estimates the user's emotions and adjusts the scheduling method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned scheduling unit, When scheduling, the content of emails and chats is analyzed to set the optimal schedule. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned scheduling unit, During scheduling, the system provides the optimal schedule by referencing the user's past schedule history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned scheduling unit, It estimates the user's emotions and determines scheduling priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned scheduling unit, When scheduling, weight the schedule based on when emails and chats are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned scheduling unit, When scheduling, the system provides an optimal schedule that takes into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that centralizes the contents of past emails and chats, The analysis unit learns the information centralized by the aforementioned collection unit and prioritizes it according to the other party's position and situation, A reply unit that automatically sends replies to emails that the analysis unit has determined to have low priority, The system includes a scheduling unit that summarizes the content of emails that the analysis unit has determined to be of high priority, and schedules requests with deadlines if any. A system characterized by the following features.

2. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of email and chat collection based on the estimated user emotions. The system according to feature 1.

3. The aforementioned collection unit is We analyze the user's past email and chat history to select the most suitable collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting emails and chats, filter them based on the user's current projects and areas of interest. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and determines the priority of emails and chats to collect based on the estimated user emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting emails and chat messages, the system prioritizes collecting highly relevant content by considering the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When collecting emails and chats, the system analyzes users' social media activity and collects relevant content. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the priority criteria based on the estimated user emotions. The system according to feature 1.

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