System

Abstract

An object of the system according to the embodiment is to monitor a non-reply state or an unprocessed state of a mail and to appropriately cope with the state.SOLUTION: A system includes a mail monitoring part and an alert issuing part. The mail monitoring part monitors the non-reply or unprocessed state of the mail. The alert issuing unit issues an alert to the unreplied or unprocessed mail identified by the mail monitoring unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there is a risk of careless mistakes occurring due to emails being left unreplied or unprocessed.

[0005] The system according to the embodiment aims to monitor unreplied or unprocessed emails and take appropriate action. [Means for solving the problem]

[0006] The system according to the embodiment includes a mail monitoring unit and an alert issuing unit. The mail monitoring unit monitors whether mail has not been replied to or processed. The alert issuing unit issues an alert for mail that has not been replied to or processed and that has been identified by the mail monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can monitor whether an email has been replied to or processed and can respond appropriately. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The email management system according to the embodiment of the present invention is a system that monitors the status of unreplied or unprocessed emails, and the generation AI issues appropriate alerts. This allows the email management system to quickly grasp the status of unreplied or unprocessed emails and prevent careless mistakes.

[0029] An email management system according to an embodiment includes an email monitoring unit and an alert issuing unit. The email monitoring unit monitors whether emails have been left unreplied or processed. For example, the generation AI periodically scans a user's mailbox to detect emails that have not been replied to or forwarded or processed for a certain period of time (e.g., three days) or have not been forwarded or acted upon. The generation AI also analyzes email content and attachments to detect whether important information is missing or whether an incorrect attachment is included. The alert issuing unit issues an alert for any unreplied or unprocessed emails identified by the email monitoring unit. For example, the generation AI issues an alert to the user via email or a LINE WORKS notification function, such as "The following email has been left unreplied. Please respond immediately." If a careless mistake is detected, the generation AI displays a notification pointing out the specific problem. This allows the email management system to quickly grasp the status of unreplied or unprocessed emails and prevent careless mistakes. For example, replies to important emails can be quickly addressed without missing them. Detecting careless mistakes in advance can improve business efficiency and quality.

[0030] The email monitoring unit can analyze the content of emails and automatically set the priority of unreplied and unprocessed emails according to their importance. For example, the email monitoring unit uses a generation AI to analyze the content of emails and automatically set the priority of unreplied and unprocessed emails according to their importance. For example, emails from superiors and emails related to project progress can be set as high priority. This allows important emails to be set according to their importance, preventing them from being overlooked.

[0031] The email monitoring unit learns the user's past reply patterns and can predict and notify in advance emails that are likely to receive a delayed reply. For example, the email monitoring unit uses a generation AI to learn the user's past reply patterns and predict and notify in advance emails that are likely to receive a delayed reply. For example, emails from specific senders to which the user frequently forgets to reply can be given priority. This allows for the prediction and notification of emails that are likely to receive a delayed reply in advance, encouraging a quick response.

[0032] The email monitoring unit can also simultaneously monitor unreplied and unprocessed messages for communication tools other than email. The email monitoring unit adds a function to simultaneously monitor unreplied and unprocessed messages for communication tools other than email (such as chat and social media). For example, messages on Slack and Microsoft Teams can also be included in the monitoring targets. This allows simultaneous monitoring of unreplied and unprocessed messages for communication tools other than email, making it possible to prevent overall oversight of responses.

[0033] The email monitoring unit can analyze the contents of emails, automatically generate related tasks and schedules, and manage them as unprocessed tasks. For example, the email monitoring unit uses a generation AI to analyze the contents of emails, automatically generate related tasks and schedules, and manage them as unprocessed tasks. For example, a meeting schedule can be automatically generated from a meeting invitation email. This allows related tasks and schedules to be automatically generated from the contents of emails and managed as unprocessed tasks, thereby improving work efficiency.

[0034] The alert issuing unit allows the generation AI to automatically generate the content of the alert and suggests the optimal response method based on the user's past response history. The alert issuing unit, for example, allows the generation AI to automatically generate the content of the alert and suggests the optimal response method based on the user's past response history. For example, the suggestion is made based on responses made to similar emails in the past. This makes it possible to support a quick and appropriate response by suggesting the optimal response method based on the user's past response history.

[0035] The alert issuing unit optimizes the timing of issuing alerts to match the user's schedule, thereby minimizing interruptions to work. The alert issuing unit, for example, optimizes the timing of issuing alerts to match the user's schedule, thereby minimizing interruptions to work. For example, an alert is not issued during a meeting or while the user is concentrating on work. In this way, by optimizing the timing of issuing alerts to match the user's schedule, interruptions to work can be minimized.

[0036] The alert issuing unit can automatically attach related documents and past email history when issuing an alert, thereby supporting a prompt response. The alert issuing unit can automatically attach related documents and past email history when issuing an alert, thereby supporting a prompt response. For example, past communications and related materials can be attached. In this way, by automatically attaching related documents and past email history, a prompt response can be supported.

[0037] The alert issuing unit can issue alerts in multiple ways, such as by email, voice notification, or notification to a smartwatch. For example, the alert issuing unit can issue alerts in multiple ways, such as by email, voice notification, or notification to a smartwatch. For example, the alert can be issued through a smartphone's voice assistant. By issuing alerts in multiple ways, the user can be sure to receive the alerts.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The email management system can also analyze a user's behavioral history and learn email processing patterns during specific time periods. For example, if a user tends to process emails in the morning, it can prioritize notifications of unreplied or unprocessed emails during that time. Also, if it is known that a user is busy on a particular day of the week, it can reduce the frequency of alerts on that day. This allows it to optimize alerts based on the user's behavioral patterns and support efficient email processing.

[0040] The email management system can also monitor the user's health and adjust the frequency of alerts if the user is not feeling well. For example, it can obtain data indicating the user's poor health (e.g., heart rate or sleep data from a smartwatch) and reduce the frequency of alerts based on that data. It can also return to normal alert frequency when the user's health improves. This allows the system to adjust alerts according to the user's health and support email processing in a comfortable manner.

[0041] The email management system can also analyze the user's schedule and prioritize notifications of unreplied or unprocessed emails before important meetings or events. For example, by notifying users of relevant emails just before a meeting, they can prepare for the meeting more efficiently. Also, by reminding users of related tasks before an event, they can help ensure the event proceeds smoothly. This allows email notifications to be optimized to suit the user's schedule, improving work efficiency.

[0042] The email management system can also analyze a user's past email processing history and automatically generate reply templates based on specific patterns. For example, it can learn frequently used phrases and standard phrases and suggest appropriate reply templates. It can also generate customized templates for specific senders and content by referring to past replies. This can help users process emails more efficiently and respond more quickly.

[0043] Email management systems can also visualize the progress of users' email processing and provide a dashboard that allows them to check outstanding emails and tasks at a glance. For example, unreplied emails and unprocessed tasks can be displayed in different colors and sorted by priority. Progress can also be displayed in graphs and charts, making it easier to grasp the overall situation. This allows users to check the progress of email processing at a glance and respond efficiently.

[0044] Email management systems can also collect user feedback on email processing and use it to improve the system. For example, they can collect user opinions on the content and timing of alerts and adjust system settings. They can also add new features based on user feedback. This allows them to provide a flexible system that meets user needs and improves the efficiency of email processing.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The email monitoring unit monitors emails for unreplied or unprocessed status. For example, the generation AI periodically scans the user's mailbox to detect emails that have not been replied to for a certain period of time (e.g., three days) or that have not been forwarded or acted upon. The generation AI also analyzes the content and attachments of emails to detect when important information is missing or when incorrect attachments are included. Step 2: The alert issuing unit issues an alert for unreplied or unprocessed emails identified by the email monitoring unit. For example, the generation AI issues an alert to the user via email or LINE WORKS notification function, stating, "The following email has been left unreplied. Please take action immediately." In addition, if the generation AI detects a careless mistake, it displays a notification pointing out the specific problem.

[0047] (Example 2) The email management system according to the embodiment of the present invention is a system that monitors the status of unreplied or unprocessed emails, and the generation AI issues appropriate alerts. This allows the email management system to quickly grasp the status of unreplied or unprocessed emails and prevent careless mistakes.

[0048] An email management system according to an embodiment includes an email monitoring unit and an alert issuing unit. The email monitoring unit monitors whether emails have been left unreplied or processed. For example, the generation AI periodically scans a user's mailbox to detect emails that have not been replied to or forwarded or processed for a certain period of time (e.g., three days) or have not been forwarded or acted upon. The generation AI also analyzes email content and attachments to detect whether important information is missing or whether an incorrect attachment is included. The alert issuing unit issues an alert for any unreplied or unprocessed emails identified by the email monitoring unit. For example, the generation AI issues an alert to the user via email or a LINE WORKS notification function, such as "The following email has been left unreplied. Please respond immediately." If a careless mistake is detected, the generation AI displays a notification pointing out the specific problem. This allows the email management system to quickly grasp the status of unreplied or unprocessed emails and prevent careless mistakes. For example, replies to important emails can be quickly addressed without missing them. Detecting careless mistakes in advance can improve business efficiency and quality.

[0049] The email monitoring unit can analyze the content of emails and automatically set the priority of unreplied and unprocessed emails according to their importance. For example, the email monitoring unit uses a generation AI to analyze the content of emails and automatically set the priority of unreplied and unprocessed emails according to their importance. For example, emails from superiors and emails related to project progress can be set as high priority. This allows important emails to be set according to their importance, preventing them from being overlooked.

[0050] The email monitoring unit learns the user's past reply patterns and can predict and notify in advance emails that are likely to receive a delayed reply. For example, the email monitoring unit uses a generation AI to learn the user's past reply patterns and predict and notify in advance emails that are likely to receive a delayed reply. For example, emails from specific senders to which the user frequently forgets to reply can be given priority. This allows for the prediction and notification of emails that are likely to receive a delayed reply in advance, encouraging a quick response.

[0051] The email monitoring unit can use the emotion estimation function to estimate the sender's emotion from the email content and prioritize monitoring of emotionally important emails. The email monitoring unit, for example, uses the emotion estimation function to estimate the sender's emotion from the email content and prioritize monitoring of emotionally important emails. For example, emails containing anger or frustration can be set as high priority. This prioritizes monitoring of emotionally important emails, thereby encouraging appropriate responses.

[0052] The email monitoring unit can also simultaneously monitor unreplied and unprocessed messages for communication tools other than email. The email monitoring unit adds a function to simultaneously monitor unreplied and unprocessed messages for communication tools other than email (such as chat and social media). For example, messages on Slack and Microsoft Teams can also be included in the monitoring targets. This allows simultaneous monitoring of unreplied and unprocessed messages for communication tools other than email, making it possible to prevent overall oversight of responses.

[0053] The email monitoring unit can analyze the contents of emails, automatically generate related tasks and schedules, and manage them as unprocessed tasks. For example, the email monitoring unit uses a generation AI to analyze the contents of emails, automatically generate related tasks and schedules, and manage them as unprocessed tasks. For example, a meeting schedule can be automatically generated from a meeting invitation email. This allows related tasks and schedules to be automatically generated from the contents of emails and managed as unprocessed tasks, thereby improving work efficiency.

[0054] The email monitoring unit uses an emotion estimation function to monitor in real time the emotions of a user when opening an email, and can flexibly adjust reminders if the user is feeling stressed. The email monitoring unit, for example, uses an emotion estimation function to monitor in real time the emotions of a user when opening an email, and can flexibly adjust reminders if the user is feeling stressed. For example, if stress is high, the frequency of reminders can be reduced. In this way, by adjusting reminders according to the user's emotions, stress can be reduced and efficient responses can be encouraged.

[0055] The alert issuing unit allows the generation AI to automatically generate the content of the alert and suggests the optimal response method based on the user's past response history. The alert issuing unit, for example, allows the generation AI to automatically generate the content of the alert and suggests the optimal response method based on the user's past response history. For example, the suggestion is made based on responses made to similar emails in the past. This makes it possible to support a quick and appropriate response by suggesting the optimal response method based on the user's past response history.

[0056] The alert issuing unit optimizes the timing of issuing alerts to match the user's schedule, thereby minimizing interruptions to work. The alert issuing unit, for example, optimizes the timing of issuing alerts to match the user's schedule, thereby minimizing interruptions to work. For example, an alert is not issued during a meeting or while the user is concentrating on work. In this way, by optimizing the timing of issuing alerts to match the user's schedule, interruptions to work can be minimized.

[0057] The alert issuing unit uses the emotion estimation function to adjust the content and timing of the alert according to the emotional state of the user, thereby reducing stress. The alert issuing unit, for example, uses the emotion estimation function to adjust the content and timing of the alert according to the emotional state of the user, thereby reducing stress. For example, when stress is high, the content of the alert is softened. In this way, stress can be reduced by adjusting the content and timing of the alert according to the emotional state of the user.

[0058] The alert issuing unit can automatically attach related documents and past email history when issuing an alert, thereby supporting a prompt response. The alert issuing unit can automatically attach related documents and past email history when issuing an alert, thereby supporting a prompt response. For example, past communications and related materials can be attached. In this way, by automatically attaching related documents and past email history, a prompt response can be supported.

[0059] The alert issuing unit can issue alerts in multiple ways, such as by email, voice notification, or notification to a smartwatch. For example, the alert issuing unit can issue alerts in multiple ways, such as by email, voice notification, or notification to a smartwatch. For example, the alert can be issued through a smartphone's voice assistant. By issuing alerts in multiple ways, the user can be sure to receive the alerts.

[0060] The alert issuing unit can use the emotion estimation function to evaluate the emotional impact that the content of the alert has on the user and convert it into a positive expression. The alert issuing unit, for example, uses the emotion estimation function to evaluate the emotional impact that the content of the alert has on the user and convert it into a positive expression. For example, it changes a harsh expression into a softer expression. In this way, by converting the content of the alert into a positive expression, it is possible to reduce stress for the user.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The email management system can also analyze a user's behavioral history and learn email processing patterns during specific time periods. For example, if a user tends to process emails in the morning, it can prioritize notifications of unreplied or unprocessed emails during that time. Also, if it is known that a user is busy on a particular day of the week, it can reduce the frequency of alerts on that day. This allows it to optimize alerts based on the user's behavioral patterns and support efficient email processing.

[0063] The email management system can also monitor the user's health and adjust the frequency of alerts if the user is not feeling well. For example, it can obtain data indicating the user's poor health (e.g., heart rate or sleep data from a smartwatch) and reduce the frequency of alerts based on that data. It can also return to normal alert frequency when the user's health improves. This allows the system to adjust alerts according to the user's health and support email processing in a comfortable manner.

[0064] The email management system can also estimate the user's emotions and reevaluate email priorities based on the estimated emotions. For example, if the user is feeling stressed, it can delay notifications of emails with low importance. On the other hand, if the user is relaxed, it can prioritize notifications of emails with high importance. This allows the system to adjust email priorities according to the user's emotional state and support efficient email processing.

[0065] The email management system can also estimate the user's emotions and adjust the content of alerts based on the estimated emotions. For example, if the user is tired, the alert content can be simplified and provide only the minimum necessary information. On the other hand, if the user is in good spirits, an alert with detailed information can be issued. This allows the content of alerts to be adjusted according to the user's emotional state, reducing stress and encouraging efficient responses.

[0066] The email management system can also estimate the user's emotions and adjust the timing of issuing alerts based on the estimated emotions. For example, if the user is feeling stressed, the system can delay issuing an alert. On the other hand, if the user is relaxed, the system can issue an alert immediately. This allows the system to adjust the timing of issuing alerts according to the user's emotional state, reducing stress and encouraging a prompt response.

[0067] The email management system can also estimate the user's emotions and convert the content of the alert into positive language based on the estimated emotions. For example, if the user is feeling stressed, the system can change harsh language to softer language. On the other hand, if the user is relaxed, the system can use normal language. This allows the content of the alert to be adjusted according to the user's emotional state, reducing stress.

[0068] The email management system can also analyze the user's schedule and prioritize notifications of unreplied or unprocessed emails before important meetings or events. For example, by notifying users of relevant emails just before a meeting, they can prepare for the meeting more efficiently. Also, by reminding users of related tasks before an event, they can help ensure the event proceeds smoothly. This allows email notifications to be optimized to suit the user's schedule, improving work efficiency.

[0069] The email management system can also analyze a user's past email processing history and automatically generate reply templates based on specific patterns. For example, it can learn frequently used phrases and standard phrases and suggest appropriate reply templates. It can also generate customized templates for specific senders and content by referring to past replies. This can help users process emails more efficiently and respond more quickly.

[0070] Email management systems can also visualize the progress of users' email processing and provide a dashboard that allows them to check outstanding emails and tasks at a glance. For example, unreplied emails and unprocessed tasks can be displayed in different colors and sorted by priority. Progress can also be displayed in graphs and charts, making it easier to grasp the overall situation. This allows users to check the progress of email processing at a glance and respond efficiently.

[0071] Email management systems can also collect user feedback on email processing and use it to improve the system. For example, they can collect user opinions on the content and timing of alerts and adjust system settings. They can also add new features based on user feedback. This allows them to provide a flexible system that meets user needs and improves the efficiency of email processing.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The email monitoring unit monitors emails for unreplied or unprocessed status. For example, the generation AI periodically scans the user's mailbox to detect emails that have not been replied to for a certain period of time (e.g., three days) or that have not been forwarded or acted upon. The generation AI also analyzes the content and attachments of emails to detect when important information is missing or when incorrect attachments are included. Step 2: The alert issuing unit issues an alert for unreplied or unprocessed emails identified by the email monitoring unit. For example, the generation AI issues an alert to the user via email or LINE WORKS notification function, stating, "The following email has been left unreplied. Please take action immediately." In addition, if the generation AI detects a careless mistake, it displays a notification pointing out the specific problem.

[0074] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0075] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0076] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0079] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0080] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0081] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0082] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0083] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0084] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0085] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0086] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0087] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0088] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0089] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0090] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0094] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0095] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0096] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0099] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0100] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0101] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0103] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 7, a 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.

[0109] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0115] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0121] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0124] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0125] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0126] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0127] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0128] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0129] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0130] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0131] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0132] 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.

[0133] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0134] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0135] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0136] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0137] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0138] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0139] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0140] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0141] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An email monitoring section that monitors unreplied and unprocessed emails; an alert issuing unit that issues an alert for the unreply or unprocessed email identified by the email monitoring unit. A system characterized by:

2. The email monitoring unit Analyzes the contents of emails and automatically sets the priority of the unreplied and unprocessed emails according to their importance.

2. The system of claim 1.

3. The email monitoring unit Simultaneously monitor unreplied and unprocessed status of communication tools other than email.

2. The system of claim 1.

4. The alert issuing unit Alert content is automatically generated by AI, and the optimal response method is suggested based on the user's past response history.

2. The system of claim 1.

5. The email monitoring unit The sender's emotions are estimated from the contents of the email, and the emails that are emotionally important are given priority for monitoring.

2. The system of claim 1.

6. The email monitoring unit Analyze the contents of the email, automatically generate related tasks and schedules, and manage them as unprocessed tasks.

2. The system of claim 1.

7. The alert issuing unit The timing of issuing these alerts is optimized to fit the user's schedule, minimizing business interruptions.

2. The system of claim 1.

8. The alert issuing unit Evaluate the emotional impact of the alert content on the user and translate it into positive language 2. The system of claim 1.

Images