system

Abstract

The system according to this embodiment aims to automatically set task reminders and send reminders to the recipient. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a registration unit, and a reminder unit. The collection unit collects data from at least one of chat, email, and voice messages. The analysis unit analyzes the data collected by the collection unit and extracts tasks and response dates and times. The registration unit registers the tasks and response dates and times extracted by the analysis unit into a reminder. The reminder unit sends a reminder to the recipient based on the tasks registered by the registration unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is necessary to manually set a task reminder, and there is a problem that it is easy to forget.

[0005] The system according to the embodiment aims to automatically set a task reminder and send a reminder to the other party.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a registration unit, and a reminder unit. The collection unit collects data from at least one of chat, email, and voice messages. The analysis unit analyzes the data collected by the collection unit and extracts tasks and response dates and times. The registration unit registers the tasks and response dates and times extracted by the analysis unit into a reminder. The reminder unit sends a reminder to the recipient based on the tasks registered by the registration unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically set task reminders and send reminders to the recipient. [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 including 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 task reminder system according to an embodiment of the present invention is a system that automates task reminders using AI. Unlike conventional reminder functions, this task reminder system does not require users to manually register tasks. By automatically extracting tasks from user chats, emails, voice messages, etc., and registering them as reminders, it reduces the user's workload and prevents tasks from being missed. For example, if a user says in a chat, "I have to prepare for tomorrow's meeting," the content of that statement is collected. This data is input into the AI. Next, the AI ​​analyzes the collected data and extracts the task and the corresponding date and time. For example, from the statement "I have to prepare for tomorrow's meeting," the AI ​​extracts the task "Prepare for the meeting" and the corresponding date and time "tomorrow." Natural language processing technology is used for this analysis. The extracted task and corresponding date and time are automatically registered as reminders. For example, the task "Prepare for the meeting" is registered as a reminder for "tomorrow." This saves the user the trouble of manually registering tasks and prevents tasks from being missed. Furthermore, the AI ​​can also automatically send reminders to other parties. For example, if a supervisor instructs an employee to "prepare the presentation materials for next week," the AI ​​analyzes the instruction and registers a task titled "Prepare presentation materials for next week" in the employee's reminder app. This eliminates the supervisor's need to manually remind the employee. This system reduces the burden of task management for users and allows them to complete tasks efficiently. Furthermore, since reminders are automated, communication becomes more efficient. In this way, the task reminder system reduces the burden of task management for users and prevents tasks from being overlooked.

[0029] The task reminder system according to the embodiment comprises a collection unit, an analysis unit, a registration unit, and a reminder unit. The collection unit collects at least one of the following data: chat, email, and voice messages. For example, the collection unit can collect a user's chat messages. The collection unit can also collect a user's email messages. Furthermore, the collection unit can also collect a user's voice messages. For example, the collection unit collects chat messages sent by a user in real time. The collection unit can also periodically collect new emails from a user's mailbox. Furthermore, the collection unit can convert a user's voice messages into text data using speech recognition technology and collect it. The analysis unit analyzes the data collected by the collection unit and extracts tasks and corresponding dates and times. For example, the analysis unit analyzes the data using natural language processing technology. For example, the analysis unit analyzes the data using morphological analysis. The analysis unit can also analyze the data using grammatical analysis. Furthermore, the analysis unit can also analyze the data using semantic analysis. For example, the analysis unit extracts keywords related to tasks from the data using morphological analysis. The analysis unit can also analyze the sentence structure of the data using grammatical analysis to identify tasks and corresponding dates and times. Furthermore, the analysis unit can understand the meaning of the data using semantic analysis and extract tasks and corresponding dates and times. The registration unit registers the tasks and corresponding dates and times extracted by the analysis unit into reminders. The registration unit can, for example, register tasks in a task management application. It can also register tasks in a calendar application. Furthermore, it can register tasks in a reminder application. For example, the registration unit registers a task called "Prepare for the meeting" in a task management application. It can also register the task as an appointment for "tomorrow" in a calendar application. Furthermore, it can register the task "Prepare for the meeting" as a reminder for "tomorrow" in a reminder application. The reminder unit sends reminders to recipients based on the tasks registered by the registration unit.The reminder function can, for example, remind a subordinate of a task assigned by their supervisor. It can also remind project members of tasks. Furthermore, it can remind family and friends of tasks. For instance, if a supervisor instructs a subordinate to "prepare the presentation materials for next week," the reminder function will remind the subordinate of that task. It can also remind project members to "prepare for tomorrow's meeting." And it can remind family and friends to "don't forget to do your shopping tomorrow." As a result, the task reminder system according to this embodiment can reduce the burden of task management on the user and prevent tasks from being missed.

[0030] The data collection unit collects data from at least one of the following: chat, email, and voice messages. For example, the unit can collect user chat messages. It can also collect user email messages. Furthermore, it can collect user voice messages. For instance, the unit collects chat messages sent by users in real time. It can also periodically collect new emails from users' mailboxes. Additionally, the unit can convert user voice messages into text data using speech recognition technology and collect that data. To efficiently collect this data, the unit utilizes multiple protocols and APIs. For example, to collect chat messages, it uses the chat application's API to retrieve messages in real time. For email messages, it uses email protocols such as IMAP and POP3, periodically checking the mailbox to retrieve new emails. For voice messages, it uses speech recognition technology to convert voice data into text data and collects that data. The speech recognition technology uses a deep learning-based speech recognition model to achieve high-precision speech recognition. This allows the data collection unit to collect data from various communication methods used by users and provide the necessary information for the task reminder system. Furthermore, the data collection unit centrally manages the collected data, enabling efficient access by the analysis and registration units. The collected data is stored in a secure database with appropriate access control. This allows the data collection unit to efficiently collect and manage data while protecting user privacy.

[0031] The analysis unit analyzes the data collected by the collection unit and extracts tasks and corresponding dates and times. The analysis unit analyzes the data using, for example, natural language processing techniques. For example, the analysis unit analyzes the data using morphological analysis. The analysis unit can also analyze the data using grammatical analysis. Furthermore, the analysis unit can analyze the data using semantic analysis. For example, the analysis unit extracts keywords related to tasks from the data using morphological analysis. The analysis unit can also analyze the sentence structure of the data using grammatical analysis and identify tasks and corresponding dates and times. Furthermore, the analysis unit can understand the meaning of the data using semantic analysis and extract tasks and corresponding dates and times. The analysis unit combines these analysis techniques to achieve more accurate task extraction. For example, it extracts important words such as nouns and verbs from the data using morphological analysis, and analyzes the relationships between these words using grammatical analysis. Furthermore, it understands the meaning of the entire sentence using semantic analysis and identifies tasks and corresponding dates and times. Based on these analysis results, the analysis unit can also evaluate the priority and importance of tasks. For example, the system can evaluate task priorities based on task content and deadlines, and prioritize reminders for important tasks. Furthermore, the analytics unit can learn from past data and user behavior patterns to predict and suggest tasks. This allows the analytics unit to streamline user task management and prevent tasks from being overlooked.

[0032] The registration unit registers tasks and corresponding dates extracted by the analysis unit into reminders. The registration unit can, for example, register tasks in a task management application. It can also register tasks in a calendar application. Furthermore, it can register tasks in a reminder application. For example, the registration unit registers a task called "Prepare for the meeting" in a task management application. It can also register the task as an appointment for "tomorrow" in a calendar application. Furthermore, it can register the task "Prepare for the meeting" as a reminder for "tomorrow" in a reminder application. The registration unit utilizes APIs and plugins to integrate with these applications. For example, it can use the task management application's API to automatically register tasks. It can also use the calendar application's API to register tasks as appointments. Furthermore, it can use the reminder application's API to register tasks as reminders. By integrating with these applications, the registration unit enables task management tailored to the tools the user is using. In addition, the registration unit also handles task modification and deletion. For example, if a user changes the content or deadline of a task, the registration unit updates the task in the corresponding application. Also, if a user completes a task, the registration unit deletes the task in the corresponding application. This allows the registration unit to streamline user task management and maintain task consistency.

[0033] The Reminder Unit sends reminders to recipients based on tasks registered by the Registration Unit. For example, the Reminder Unit can remind a subordinate of a task assigned by their supervisor. It can also remind project members of tasks. Furthermore, the Reminder Unit can remind family and friends of tasks. For example, if a supervisor instructs a subordinate to "prepare the presentation materials for next week," the Reminder Unit will remind the subordinate of that task. It can also remind project members to "prepare for tomorrow's meeting." Furthermore, it can remind family and friends to "don't forget to do your shopping tomorrow." To efficiently perform these reminders, the Reminder Unit utilizes multiple notification methods. For example, it uses email, chat messages, and push notifications to send reminders. The Reminder Unit can select the most suitable notification method according to the user's preferences. In addition, the Reminder Unit can adjust the timing and frequency of reminders. For example, the frequency of reminders can be increased when a task deadline is approaching or when the task is of high importance. Furthermore, the system can record the user's response after receiving a reminder and evaluate the effectiveness of the reminder. This allows the reminder unit to support the user's task management and prevent tasks from being missed. In addition, the reminder unit can continuously improve the content and timing of reminders based on user feedback. This allows the reminder unit to provide the optimal reminder for the user and reduce the burden of task management.

[0034] The analysis unit can analyze data using natural language processing techniques. For example, the analysis unit can analyze data using morphological analysis. For example, the analysis unit can extract task-related keywords from the data using morphological analysis. The analysis unit can also analyze data using grammatical analysis. For example, the analysis unit can analyze the sentence structure of the data using grammatical analysis and identify the task and corresponding date and time. The analysis unit can also analyze data using semantic analysis. For example, the analysis unit can understand the meaning of the data using semantic analysis and extract the task and corresponding date and time. This improves the accuracy of data analysis by using natural language processing techniques. Natural language processing techniques include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input data collected by the collection unit into a generation AI and have the generation AI perform the extraction of tasks and corresponding dates and times.

[0035] The data collection unit can analyze the user's past communication history and select the optimal collection method. For example, the data collection unit may prioritize collecting communication channels (chat, email, etc.) that the user has frequently used in the past. For example, the data collection unit may prioritize collecting chat messages that the user has frequently used in the past. The data collection unit can also analyze the user's past communication patterns and determine the optimal collection timing. For example, the data collection unit may identify the time periods when the user has sent messages containing important tasks in the past and collect data during those times. This allows the optimal collection method to be selected by analyzing the past communication history. Past communication history includes, but is not limited to, email history and chat history. 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 past communication history into a generating AI and have the generating AI select the optimal collection method.

[0036] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the collection unit can collect only messages related to the project the user is currently working on. For example, the collection unit can collect chat messages related to the project the user is currently working on. The collection unit can also prioritize the collection of messages containing keywords related to the user's areas of interest. For example, the collection unit can collect email messages containing keywords related to the user's areas of interest. Furthermore, if the user is focused on a specific project, the collection unit can collect only data related to that project. For example, if the user is focused on a specific project, the collection unit can collect voice messages related to that project. This allows for the collection of highly relevant data by filtering data based on the current project and areas of interest. Current projects and areas of interest include, but are not limited to, project categories and keywords related to areas of interest. Some or all of the processing described above in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input data related to the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0037] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific location, the data collection unit will prioritize the collection of data related to tasks in that location. For example, if the user is in an office, the data collection unit will prioritize the collection of chat messages related to tasks in the office. The data collection unit can also prioritize the collection of data related to tasks in the user's destination if the user is on the move. For example, if the user is on the move, the data collection unit will prioritize the collection of email messages related to the user's destination. The data collection unit can also prioritize the collection of data related to tasks performed at home if the user is at home. For example, if the user is at home, the data collection unit will prioritize the collection of voice messages related to tasks performed at home. This enables data collection tailored to the user's situation by collecting highly relevant data based on geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's geographical location information into the generating AI, allowing the AI ​​to collect highly relevant data.

[0038] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to tasks mentioned by the user on social media. For example, the data collection unit can collect chat messages related to tasks mentioned by the user on social media. The data collection unit can also prioritize collecting messages from accounts that the user follows on social media. For example, the data collection unit can prioritize collecting email messages from accounts that the user follows on social media. The data collection unit can also collect data related to events that the user participates in on social media. For example, the data collection unit can collect audio messages related to events that the user participates in on social media. This allows for the efficient collection of relevant data by analyzing social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the processing described above 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 into a generating AI and have the generating AI collect the relevant data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the tasks during the analysis. For example, the analysis unit performs a detailed analysis for high-importance tasks. For example, the analysis unit performs a detailed graphical display for high-importance tasks. The analysis unit can also perform a concise analysis for low-importance tasks. For example, the analysis unit performs a concise text display for low-importance tasks. The analysis unit can also perform a rapid analysis for urgent tasks. For example, the analysis unit performs a rapid bulleted list analysis for urgent tasks. In this way, by adjusting the level of detail of the analysis based on the importance of the tasks, a detailed analysis can be performed for important tasks. Task importance includes, but is not limited to, urgency and impact. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the task category during analysis. For example, for project management tasks, the analysis unit can apply an analysis algorithm specifically for project management. For example, for project management tasks, the analysis unit can apply a clustering algorithm specifically for project management. The analysis unit can also apply an analysis algorithm specifically for individual tasks. For example, for individual tasks, the analysis unit can apply a regression analysis algorithm specifically for individual tasks. The analysis unit can also apply an analysis algorithm specifically for team tasks. For example, for team tasks, the analysis unit can apply a classification algorithm specifically for team tasks. By applying different analysis algorithms depending on the task category, the analysis can be optimized for each category. Task categories include, but are not limited to, business tasks and individual tasks. Analysis algorithms include, but are not limited to, clustering algorithms, regression analysis, and classification algorithms. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input task category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0041] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit will prioritize the analysis of tasks with approaching deadlines. For example, the analysis unit will prioritize the analysis of tasks with approaching deadlines using graphs. The analysis unit can also postpone the analysis of tasks with distant submission dates. For example, the analysis unit will postpone the analysis of tasks with distant submission dates using text displays. Furthermore, the analysis unit can analyze tasks with unknown submission dates after the analysis of other tasks is complete. For example, the analysis unit will analyze tasks with unknown submission dates using bullet points after the analysis of other tasks is complete. This allows for prioritizing the analysis based on the data submission date, thereby prioritizing the analysis of tasks with approaching deadlines. The data submission date includes, but is not limited to, submission deadlines and submission dates. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input data submission timing data into the generating AI and have the generating AI determine the priority of the analysis.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can prioritize the analysis of highly relevant data using graph display. The analysis unit can also postpone the analysis of less relevant data. For example, the analysis unit can postpone the analysis of less relevant data using text display. The analysis unit can also group relevant data for analysis. For example, the analysis unit can group relevant data and analyze it using bullet points. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Data relevance includes, but is not limited to, common keywords and related topics. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input data relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0043] The registration unit can adjust the level of detail in the registration based on the importance of the task. For example, the registration unit registers detailed information for high-importance tasks. For example, the registration unit registers information including a detailed explanation for high-importance tasks. The registration unit can also register concise information for low-importance tasks. For example, the registration unit registers information including a concise explanation for low-importance tasks. The registration unit can also register urgent tasks quickly. For example, the registration unit quickly registers information for urgent tasks. This allows for the registration of detailed information for important tasks by adjusting the level of detail in the registration based on the importance of the task. Task importance includes, but is not limited to, urgency and impact. Some or all of the above processing in the registration unit may be performed using, for example, AI, or not using AI. For example, the registration unit can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the registration.

[0044] The registration unit can apply different registration algorithms depending on the task category during registration. For example, the registration unit can apply a registration algorithm specifically for project management to project management tasks. For example, the registration unit can apply a rule-based algorithm specifically for project management to project management tasks. The registration unit can also apply a registration algorithm specifically for individual tasks to individual tasks. For example, the registration unit can apply a machine learning algorithm specifically for individual tasks to individual tasks. The registration unit can also apply a registration algorithm specifically for team tasks to team tasks. For example, the registration unit can apply a classification algorithm specifically for team tasks to team tasks. By applying different registration algorithms depending on the task category, the system can perform registration that is optimal for each category. Task categories include, but are not limited to, business tasks and individual tasks. Registration algorithms include, but are not limited to, rule-based algorithms, machine learning algorithms, and classification algorithms. Some or all of the above-described processes in the registration unit may be performed using, for example, AI, or not using AI. For example, the registration unit can input task category data into a generating AI and have the generating AI perform the application of the registration algorithm.

[0045] The registration unit can adjust the order of registration based on the task submission date. For example, the registration unit can prioritize the registration of tasks with approaching deadlines. The registration unit can also postpone the registration of tasks with later submission dates. The registration unit can also register tasks with unknown submission dates after the registration of other tasks has been completed. This allows for prioritizing the registration of tasks with approaching deadlines by adjusting the order of registration based on the task submission dates. Task submission dates include, but are not limited to, submission deadlines and submission dates. Some or all of the above processing in the registration unit may be performed using, for example, AI, or not using AI. For example, the registration unit can input task submission date data into a generating AI and have the generating AI perform the adjustment of the registration order.

[0046] The registration unit can adjust the registration order based on the relevance of tasks during registration. For example, the registration unit can prioritize the registration of highly relevant tasks. The registration unit can also postpone the registration of less relevant tasks. The registration unit can also group related tasks together for registration. This allows for the prioritization of highly relevant tasks by adjusting the registration order based on the relevance of tasks. Task relevance includes, but is not limited to, common keywords and related topics. Some or all of the above processing in the registration unit may be performed using, for example, AI, or not using AI. For example, the registration unit can input task relevance data into a generating AI and have the generating AI adjust the registration order.

[0047] The reminder unit can adjust the level of detail of reminders based on the importance of the task. For example, the reminder unit can provide detailed reminders for high-importance tasks. For example, the reminder unit can provide detailed notifications for high-importance tasks. The reminder unit can also provide concise reminders for low-importance tasks. For example, the reminder unit can provide concise notifications for low-importance tasks. The reminder unit can also provide rapid reminders for urgent tasks. For example, the reminder unit can provide rapid notifications for urgent tasks. In this way, by adjusting the level of detail of reminders based on the importance of the task, detailed reminders can be provided for important tasks. Task importance includes, but is not limited to, urgency and impact. Some or all of the above processing in the reminder unit may be performed using, for example, AI, or not using AI. For example, the reminder unit can input task importance data into a generating AI and have the generating AI adjust the level of detail of the reminders.

[0048] The reminder unit can apply different reminder algorithms depending on the task category when sending a reminder. For example, the reminder unit can apply a project management-specific reminder algorithm to project management tasks. For example, the reminder unit can apply a rule-based algorithm specifically for project management tasks. The reminder unit can also apply a reminder algorithm specifically for individual tasks. For example, the reminder unit can apply a machine learning algorithm specifically for individual tasks. The reminder unit can also apply a reminder algorithm specifically for team tasks. For example, the reminder unit can apply a classification algorithm specifically for team tasks. By applying different reminder algorithms depending on the task category, the system can provide the most appropriate reminder for each category. Task categories include, but are not limited to, business tasks and individual tasks. Reminder algorithms include, but are not limited to, rule-based algorithms, machine learning algorithms, and classification algorithms. Some or all of the above processing in the reminder unit may be performed using, for example, AI, or without using AI. For example, the reminder unit can input task category data into the generation AI and have the generation AI execute the application of a reminder algorithm.

[0049] The reminder unit can adjust the order of reminders based on the task submission dates. For example, the reminder unit can prioritize reminding tasks with approaching deadlines. The reminder unit can also postpone reminding tasks with later submission dates. The reminder unit can also remind tasks with unknown submission dates after other tasks have been reminded. By adjusting the order of reminders based on the task submission dates, tasks with approaching deadlines can be prioritized. The task submission dates include, but are not limited to, submission deadlines and submission dates. Some or all of the above processing in the reminder unit may be performed using, for example, AI, or not. For example, the reminder unit can input task submission time data into the generating AI and have the generating AI adjust the order of reminders.

[0050] The reminder unit can adjust the order of reminders based on the relevance of tasks. For example, the reminder unit can prioritize reminding users of highly relevant tasks. The reminder unit can also postpone reminding users of less relevant tasks. The reminder unit can also group related tasks together and remind users of those groups together. This allows the system to prioritize reminding users of highly relevant tasks by adjusting the order of reminders based on the relevance of tasks. Task relevance includes, but is not limited to, common keywords and related topics. Some or all of the above processing in the reminder unit may be performed using, for example, AI, or not. For example, the reminder unit can input task relevance data into a generating AI and have the generating AI adjust the order of reminders.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] A task reminder system can analyze a user's past task history to determine the optimal reminder timing. For example, it can identify the time periods when a user has previously completed important tasks and send reminders during those times. It can also identify times when a user tends to forget tasks and strengthen reminders during those periods. Furthermore, it can analyze how often a user has completed tasks in the past and adjust reminders based on that frequency. In this way, by analyzing past task history, the system can determine the optimal reminder timing and prevent tasks from being missed.

[0053] The task reminder system can provide reminders based on the user's geographical location. For example, if the user is in the office, it can remind them of office-related tasks. If the user is at home, it can remind them of tasks to be done at home. Furthermore, if the user is traveling, it can remind them of tasks related to their destination. This allows for the provision of appropriate reminders tailored to the user's situation by using geographical location information.

[0054] A task reminder system can analyze a user's social media activity and remind them of relevant tasks. For example, it can remind users of tasks they have mentioned on social media. It can also prioritize reminders of messages from accounts the user follows on social media. Furthermore, it can remind users of tasks related to events they are participating in on social media. This allows for efficient task reminders by analyzing social media activity.

[0055] A task reminder system can provide reminders based on the user's current projects and areas of interest. For example, it can remind users of tasks related to projects they are currently working on. It can also prioritize reminders for tasks related to the user's areas of interest. Furthermore, if a user is focused on a specific project, it can remind users only of tasks related to that project. This allows for efficient reminders of highly relevant tasks by basing reminders on current projects and areas of interest.

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

[0057] Step 1: The collection unit collects data from at least one of the following: chat, email, and voice messages. For example, the collection unit can collect users' chat messages in real time, periodically collect new emails from their mailbox, and convert voice messages into text data using speech recognition technology and collect them. Step 2: The analysis unit analyzes the data collected by the collection unit and extracts the tasks and corresponding dates and times. For example, the analysis unit uses natural language processing technology to analyze the data, understanding keywords, sentence structures, and meanings related to the tasks using morphological analysis, grammatical analysis, and semantic analysis, and identifies the tasks and corresponding dates and times. Step 3: The registration unit registers the tasks and corresponding dates extracted by the analysis unit into the reminder. For example, the registration unit can register tasks in a task management application, a calendar application, or a reminder application. Step 4: The reminder unit sends reminders to recipients based on tasks registered by the registration unit. For example, the reminder unit can remind managers of tasks assigned to their subordinates, tasks for project members, and tasks for family and friends.

[0058] (Example of form 2) The task reminder system according to an embodiment of the present invention is a system that automates task reminders using AI. Unlike conventional reminder functions, this task reminder system does not require users to manually register tasks. By automatically extracting tasks from user chats, emails, voice messages, etc., and registering them as reminders, it reduces the user's workload and prevents tasks from being missed. For example, if a user says in a chat, "I have to prepare for tomorrow's meeting," the content of that statement is collected. This data is input into the AI. Next, the AI ​​analyzes the collected data and extracts the task and the corresponding date and time. For example, from the statement "I have to prepare for tomorrow's meeting," the AI ​​extracts the task "Prepare for the meeting" and the corresponding date and time "tomorrow." Natural language processing technology is used for this analysis. The extracted task and corresponding date and time are automatically registered as reminders. For example, the task "Prepare for the meeting" is registered as a reminder for "tomorrow." This saves the user the trouble of manually registering tasks and prevents tasks from being missed. Furthermore, the AI ​​can also automatically send reminders to other parties. For example, if a supervisor instructs an employee to "prepare the presentation materials for next week," the AI ​​analyzes the instruction and registers a task titled "Prepare presentation materials for next week" in the employee's reminder app. This eliminates the supervisor's need to manually remind the employee. This system reduces the burden of task management for users and allows them to complete tasks efficiently. Furthermore, since reminders are automated, communication becomes more efficient. In this way, the task reminder system reduces the burden of task management for users and prevents tasks from being overlooked.

[0059] The task reminder system according to the embodiment comprises a collection unit, an analysis unit, a registration unit, and a reminder unit. The collection unit collects at least one of the following data: chat, email, and voice messages. For example, the collection unit can collect a user's chat messages. The collection unit can also collect a user's email messages. Furthermore, the collection unit can also collect a user's voice messages. For example, the collection unit collects chat messages sent by a user in real time. The collection unit can also periodically collect new emails from a user's mailbox. Furthermore, the collection unit can convert a user's voice messages into text data using speech recognition technology and collect it. The analysis unit analyzes the data collected by the collection unit and extracts tasks and corresponding dates and times. For example, the analysis unit analyzes the data using natural language processing technology. For example, the analysis unit analyzes the data using morphological analysis. The analysis unit can also analyze the data using grammatical analysis. Furthermore, the analysis unit can also analyze the data using semantic analysis. For example, the analysis unit extracts keywords related to tasks from the data using morphological analysis. The analysis unit can also analyze the sentence structure of the data using grammatical analysis to identify tasks and corresponding dates and times. Furthermore, the analysis unit can understand the meaning of the data using semantic analysis and extract tasks and corresponding dates and times. The registration unit registers the tasks and corresponding dates and times extracted by the analysis unit into reminders. The registration unit can, for example, register tasks in a task management application. It can also register tasks in a calendar application. Furthermore, it can register tasks in a reminder application. For example, the registration unit registers a task called "Prepare for the meeting" in a task management application. It can also register the task as an appointment for "tomorrow" in a calendar application. Furthermore, it can register the task "Prepare for the meeting" as a reminder for "tomorrow" in a reminder application. The reminder unit sends reminders to recipients based on the tasks registered by the registration unit.The reminder function can, for example, remind a subordinate of a task assigned by their supervisor. It can also remind project members of tasks. Furthermore, it can remind family and friends of tasks. For instance, if a supervisor instructs a subordinate to "prepare the presentation materials for next week," the reminder function will remind the subordinate of that task. It can also remind project members to "prepare for tomorrow's meeting." And it can remind family and friends to "don't forget to do your shopping tomorrow." As a result, the task reminder system according to this embodiment can reduce the burden of task management on the user and prevent tasks from being missed.

[0060] The data collection unit collects data from at least one of the following: chat, email, and voice messages. For example, the unit can collect user chat messages. It can also collect user email messages. Furthermore, it can collect user voice messages. For instance, the unit collects chat messages sent by users in real time. It can also periodically collect new emails from users' mailboxes. Additionally, the unit can convert user voice messages into text data using speech recognition technology and collect that data. To efficiently collect this data, the unit utilizes multiple protocols and APIs. For example, to collect chat messages, it uses the chat application's API to retrieve messages in real time. For email messages, it uses email protocols such as IMAP and POP3, periodically checking the mailbox to retrieve new emails. For voice messages, it uses speech recognition technology to convert voice data into text data and collects that data. The speech recognition technology uses a deep learning-based speech recognition model to achieve high-precision speech recognition. This allows the data collection unit to collect data from various communication methods used by users and provide the necessary information for the task reminder system. Furthermore, the data collection unit centrally manages the collected data, enabling efficient access by the analysis and registration units. The collected data is stored in a secure database with appropriate access control. This allows the data collection unit to efficiently collect and manage data while protecting user privacy.

[0061] The analysis unit analyzes the data collected by the collection unit and extracts tasks and corresponding dates and times. The analysis unit analyzes the data using, for example, natural language processing techniques. For example, the analysis unit analyzes the data using morphological analysis. The analysis unit can also analyze the data using grammatical analysis. Furthermore, the analysis unit can analyze the data using semantic analysis. For example, the analysis unit extracts keywords related to tasks from the data using morphological analysis. The analysis unit can also analyze the sentence structure of the data using grammatical analysis and identify tasks and corresponding dates and times. Furthermore, the analysis unit can understand the meaning of the data using semantic analysis and extract tasks and corresponding dates and times. The analysis unit combines these analysis techniques to achieve more accurate task extraction. For example, it extracts important words such as nouns and verbs from the data using morphological analysis, and analyzes the relationships between these words using grammatical analysis. Furthermore, it understands the meaning of the entire sentence using semantic analysis and identifies tasks and corresponding dates and times. Based on these analysis results, the analysis unit can also evaluate the priority and importance of tasks. For example, the system can evaluate task priorities based on task content and deadlines, and prioritize reminders for important tasks. Furthermore, the analytics unit can learn from past data and user behavior patterns to predict and suggest tasks. This allows the analytics unit to streamline user task management and prevent tasks from being overlooked.

[0062] The registration unit registers tasks and corresponding dates extracted by the analysis unit into reminders. The registration unit can, for example, register tasks in a task management application. It can also register tasks in a calendar application. Furthermore, it can register tasks in a reminder application. For example, the registration unit registers a task called "Prepare for the meeting" in a task management application. It can also register the task as an appointment for "tomorrow" in a calendar application. Furthermore, it can register the task "Prepare for the meeting" as a reminder for "tomorrow" in a reminder application. The registration unit utilizes APIs and plugins to integrate with these applications. For example, it can use the task management application's API to automatically register tasks. It can also use the calendar application's API to register tasks as appointments. Furthermore, it can use the reminder application's API to register tasks as reminders. By integrating with these applications, the registration unit enables task management tailored to the tools the user is using. In addition, the registration unit also handles task modification and deletion. For example, if a user changes the content or deadline of a task, the registration unit updates the task in the corresponding application. Also, if a user completes a task, the registration unit deletes the task in the corresponding application. This allows the registration unit to streamline user task management and maintain task consistency.

[0063] The Reminder Unit sends reminders to recipients based on tasks registered by the Registration Unit. For example, the Reminder Unit can remind a subordinate of a task assigned by their supervisor. It can also remind project members of tasks. Furthermore, the Reminder Unit can remind family and friends of tasks. For example, if a supervisor instructs a subordinate to "prepare the presentation materials for next week," the Reminder Unit will remind the subordinate of that task. It can also remind project members to "prepare for tomorrow's meeting." Furthermore, it can remind family and friends to "don't forget to do your shopping tomorrow." To efficiently perform these reminders, the Reminder Unit utilizes multiple notification methods. For example, it uses email, chat messages, and push notifications to send reminders. The Reminder Unit can select the most suitable notification method according to the user's preferences. In addition, the Reminder Unit can adjust the timing and frequency of reminders. For example, the frequency of reminders can be increased when a task deadline is approaching or when the task is of high importance. Furthermore, the system can record the user's response after receiving a reminder and evaluate the effectiveness of the reminder. This allows the reminder unit to support the user's task management and prevent tasks from being missed. In addition, the reminder unit can continuously improve the content and timing of reminders based on user feedback. This allows the reminder unit to provide the optimal reminder for the user and reduce the burden of task management.

[0064] The analysis unit can analyze data using natural language processing techniques. For example, the analysis unit can analyze data using morphological analysis. For example, the analysis unit can extract task-related keywords from the data using morphological analysis. The analysis unit can also analyze data using grammatical analysis. For example, the analysis unit can analyze the sentence structure of the data using grammatical analysis and identify the task and corresponding date and time. The analysis unit can also analyze data using semantic analysis. For example, the analysis unit can understand the meaning of the data using semantic analysis and extract the task and corresponding date and time. This improves the accuracy of data analysis by using natural language processing techniques. Natural language processing techniques include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input data collected by the collection unit into a generation AI and have the generation AI perform the extraction of tasks and corresponding dates and times.

[0065] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For instance, if the user is stressed, the data collection unit might reduce the frequency of data collection to once a day. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to capture more tasks. For example, if the user is relaxed, the data collection unit might increase the frequency of data collection to three times a day. The data collection unit can also adjust the timing of data collection if the user is busy, so as not to interrupt the user's work. For example, if the user is busy, the data collection unit might adjust the timing of data collection to after the user has finished their work. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0066] The data collection unit can analyze the user's past communication history and select the optimal collection method. For example, the data collection unit may prioritize collecting communication channels (chat, email, etc.) that the user has frequently used in the past. For example, the data collection unit may prioritize collecting chat messages that the user has frequently used in the past. The data collection unit can also analyze the user's past communication patterns and determine the optimal collection timing. For example, the data collection unit may identify the time periods when the user has sent messages containing important tasks in the past and collect data during those times. This allows the optimal collection method to be selected by analyzing the past communication history. Past communication history includes, but is not limited to, email history and chat history. 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 past communication history into a generating AI and have the generating AI select the optimal collection method.

[0067] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the collection unit can collect only messages related to the project the user is currently working on. For example, the collection unit can collect chat messages related to the project the user is currently working on. The collection unit can also prioritize the collection of messages containing keywords related to the user's areas of interest. For example, the collection unit can collect email messages containing keywords related to the user's areas of interest. Furthermore, if the user is focused on a specific project, the collection unit can collect only data related to that project. For example, if the user is focused on a specific project, the collection unit can collect voice messages related to that project. This allows for the collection of highly relevant data by filtering data based on the current project and areas of interest. Current projects and areas of interest include, but are not limited to, project categories and keywords related to areas of interest. Some or all of the processing described above in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input data related to the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0068] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data related to high-priority tasks. For example, if the user is stressed, the data collection unit will prioritize collecting chat messages related to high-priority tasks. Alternatively, if the user is relaxed, the data collection unit can collect data related to all tasks equally. For example, if the user is relaxed, the data collection unit will collect email messages related to all tasks equally. Also, if the user is in a hurry, the data collection unit can prioritize collecting data related to high-urgency tasks. For example, if the user is in a hurry, the data collection unit will prioritize collecting voice messages related to high-urgency tasks. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. 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 user emotion data into a generating AI and have the generating AI determine the priority of the data.

[0069] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific location, the data collection unit will prioritize the collection of data related to tasks in that location. For example, if the user is in an office, the data collection unit will prioritize the collection of chat messages related to tasks in the office. The data collection unit can also prioritize the collection of data related to tasks in the user's destination if the user is on the move. For example, if the user is on the move, the data collection unit will prioritize the collection of email messages related to the user's destination. The data collection unit can also prioritize the collection of data related to tasks performed at home if the user is at home. For example, if the user is at home, the data collection unit will prioritize the collection of voice messages related to tasks performed at home. This enables data collection tailored to the user's situation by collecting highly relevant data based on geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's geographical location information into the generating AI, allowing the AI ​​to collect highly relevant data.

[0070] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to tasks mentioned by the user on social media. For example, the data collection unit can collect chat messages related to tasks mentioned by the user on social media. The data collection unit can also prioritize collecting messages from accounts that the user follows on social media. For example, the data collection unit can prioritize collecting email messages from accounts that the user follows on social media. The data collection unit can also collect data related to events that the user participates in on social media. For example, the data collection unit can collect audio messages related to events that the user participates in on social media. This allows for the efficient collection of relevant data by analyzing social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the processing described above 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 into a generating AI and have the generating AI collect the relevant data.

[0071] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is nervous, the analysis unit can provide the analysis results in a simple graph display. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, if the user is relaxed, the analysis unit can provide the analysis results in a detailed text display. The analysis unit can also provide concise analysis results that get straight to the point if the user is in a hurry. For example, if the user is in a hurry, the analysis unit can provide analysis results in a concise bulleted list that gets straight to the point. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. 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 user emotion data into the generating AI and have the generating AI adjust the way the analysis is expressed.

[0072] The analysis unit can adjust the level of detail of the analysis based on the importance of the tasks during the analysis. For example, the analysis unit performs a detailed analysis for high-importance tasks. For example, the analysis unit performs a detailed graphical display for high-importance tasks. The analysis unit can also perform a concise analysis for low-importance tasks. For example, the analysis unit performs a concise text display for low-importance tasks. The analysis unit can also perform a rapid analysis for urgent tasks. For example, the analysis unit performs a rapid bulleted list analysis for urgent tasks. In this way, by adjusting the level of detail of the analysis based on the importance of the tasks, a detailed analysis can be performed for important tasks. Task importance includes, but is not limited to, urgency and impact. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0073] The analysis unit can apply different analysis algorithms depending on the task category during analysis. For example, for project management tasks, the analysis unit can apply an analysis algorithm specifically for project management. For example, for project management tasks, the analysis unit can apply a clustering algorithm specifically for project management. The analysis unit can also apply an analysis algorithm specifically for individual tasks. For example, for individual tasks, the analysis unit can apply a regression analysis algorithm specifically for individual tasks. The analysis unit can also apply an analysis algorithm specifically for team tasks. For example, for team tasks, the analysis unit can apply a classification algorithm specifically for team tasks. By applying different analysis algorithms depending on the task category, the analysis can be optimized for each category. Task categories include, but are not limited to, business tasks and individual tasks. Analysis algorithms include, but are not limited to, clustering algorithms, regression analysis, and classification algorithms. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input task category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0074] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is in a hurry, the analysis unit can provide a short, concise bulleted list of analysis results. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is relaxed, the analysis unit can provide a detailed text display of analysis results. The analysis unit can also provide a visually stimulating analysis result if the user is excited. For example, if the user is excited, the analysis unit can provide a visually stimulating graph display of analysis results. By adjusting the length of the analysis according to the user's emotions, it is possible to provide analysis results of an appropriate length for the user. 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-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the length of the analysis.

[0075] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit will prioritize the analysis of tasks with approaching deadlines. For example, the analysis unit will prioritize the analysis of tasks with approaching deadlines using graphs. The analysis unit can also postpone the analysis of tasks with distant submission dates. For example, the analysis unit will postpone the analysis of tasks with distant submission dates using text displays. Furthermore, the analysis unit can analyze tasks with unknown submission dates after the analysis of other tasks is complete. For example, the analysis unit will analyze tasks with unknown submission dates using bullet points after the analysis of other tasks is complete. This allows for prioritizing the analysis based on the data submission date, thereby prioritizing the analysis of tasks with approaching deadlines. The data submission date includes, but is not limited to, submission deadlines and submission dates. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input data submission timing data into the generating AI and have the generating AI determine the priority of the analysis.

[0076] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can prioritize the analysis of highly relevant data using graph display. The analysis unit can also postpone the analysis of less relevant data. For example, the analysis unit can postpone the analysis of less relevant data using text display. The analysis unit can also group relevant data for analysis. For example, the analysis unit can group relevant data and analyze it using bullet points. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Data relevance includes, but is not limited to, common keywords and related topics. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input data relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0077] The registration unit can estimate the user's emotions and adjust the registration method based on the estimated emotions. For example, if the user is stressed, the registration unit can provide a simple interface and minimize the registration process. For example, if the user is stressed, the registration unit can register the task using a simple interface. Alternatively, if the user is relaxed, the registration unit can provide detailed input options and suggest a customizable registration method. For example, if the user is relaxed, the registration unit can register the task using detailed input options. Furthermore, if the user is in a hurry, the registration unit can prioritize voice input to allow for quick task registration. For example, if the user is in a hurry, the registration unit can register the task using voice input. This allows for a user-friendly registration method by adjusting the registration method 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 includes, 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 registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input user emotion data into a generating AI and have the generating AI adjust the registration method.

[0078] The registration unit can adjust the level of detail in the registration based on the importance of the task. For example, the registration unit registers detailed information for high-importance tasks. For example, the registration unit registers information including a detailed explanation for high-importance tasks. The registration unit can also register concise information for low-importance tasks. For example, the registration unit registers information including a concise explanation for low-importance tasks. The registration unit can also register urgent tasks quickly. For example, the registration unit quickly registers information for urgent tasks. This allows for the registration of detailed information for important tasks by adjusting the level of detail in the registration based on the importance of the task. Task importance includes, but is not limited to, urgency and impact. Some or all of the above processing in the registration unit may be performed using, for example, AI, or not using AI. For example, the registration unit can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the registration.

[0079] The registration unit can apply different registration algorithms depending on the task category during registration. For example, the registration unit can apply a registration algorithm specifically for project management to project management tasks. For example, the registration unit can apply a rule-based algorithm specifically for project management to project management tasks. The registration unit can also apply a registration algorithm specifically for individual tasks to individual tasks. For example, the registration unit can apply a machine learning algorithm specifically for individual tasks to individual tasks. The registration unit can also apply a registration algorithm specifically for team tasks to team tasks. For example, the registration unit can apply a classification algorithm specifically for team tasks to team tasks. By applying different registration algorithms depending on the task category, the system can perform registration that is optimal for each category. Task categories include, but are not limited to, business tasks and individual tasks. Registration algorithms include, but are not limited to, rule-based algorithms, machine learning algorithms, and classification algorithms. Some or all of the above-described processes in the registration unit may be performed using, for example, AI, or not using AI. For example, the registration unit can input task category data into a generating AI and have the generating AI perform the application of the registration algorithm.

[0080] The registration unit can estimate the user's emotions and determine the priority of registration based on the estimated emotions. For example, if the user is stressed, the registration unit will prioritize registering high-priority tasks. For example, if the user is stressed, the registration unit will prioritize registering high-priority tasks. Alternatively, if the user is relaxed, the registration unit can register all tasks evenly. For example, if the user is relaxed, the registration unit will register all tasks evenly. Alternatively, if the user is in a hurry, the registration unit can prioritize registering high-urgency tasks. For example, if the user is in a hurry, the registration unit will prioritize registering high-urgency tasks. In this way, by determining the priority of registration according to the user's emotions, important tasks can be registered preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input user emotion data into a generating AI and have the generating AI determine the priority of registrations.

[0081] The registration unit can adjust the order of registration based on the task submission date. For example, the registration unit can prioritize the registration of tasks with approaching deadlines. The registration unit can also postpone the registration of tasks with later submission dates. The registration unit can also register tasks with unknown submission dates after the registration of other tasks has been completed. This allows for prioritizing the registration of tasks with approaching deadlines by adjusting the order of registration based on the task submission dates. Task submission dates include, but are not limited to, submission deadlines and submission dates. Some or all of the above processing in the registration unit may be performed using, for example, AI, or not using AI. For example, the registration unit can input task submission date data into a generating AI and have the generating AI perform the adjustment of the registration order.

[0082] The registration unit can adjust the registration order based on the relevance of tasks during registration. For example, the registration unit can prioritize the registration of highly relevant tasks. The registration unit can also postpone the registration of less relevant tasks. The registration unit can also group related tasks together for registration. This allows for the prioritization of highly relevant tasks by adjusting the registration order based on the relevance of tasks. Task relevance includes, but is not limited to, common keywords and related topics. Some or all of the above processing in the registration unit may be performed using, for example, AI, or not using AI. For example, the registration unit can input task relevance data into a generating AI and have the generating AI adjust the registration order.

[0083] The reminder unit can estimate the user's emotions and adjust the reminder method based on the estimated emotions. For example, if the user is stressed, the reminder unit can provide a simple and highly visible reminder method. For example, if the user is stressed, the reminder unit can provide a simple notification. The reminder unit can also provide a reminder method that includes detailed information if the user is relaxed. For example, if the user is relaxed, the reminder unit can provide a detailed notification. The reminder unit can also provide a concise reminder method if the user is in a hurry. For example, if the user is in a hurry, the reminder unit can provide a concise notification that gets straight to the point. In this way, by adjusting the reminder method according to the user's emotions, the system can provide the most optimal reminder method for the user. 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 reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input user emotion data into a generating AI and have the AI ​​adjust the reminder method.

[0084] The reminder unit can adjust the level of detail of reminders based on the importance of the task. For example, the reminder unit can provide detailed reminders for high-importance tasks. For example, the reminder unit can provide detailed notifications for high-importance tasks. The reminder unit can also provide concise reminders for low-importance tasks. For example, the reminder unit can provide concise notifications for low-importance tasks. The reminder unit can also provide rapid reminders for urgent tasks. For example, the reminder unit can provide rapid notifications for urgent tasks. In this way, by adjusting the level of detail of reminders based on the importance of the task, detailed reminders can be provided for important tasks. Task importance includes, but is not limited to, urgency and impact. Some or all of the above processing in the reminder unit may be performed using, for example, AI, or not using AI. For example, the reminder unit can input task importance data into a generating AI and have the generating AI adjust the level of detail of the reminders.

[0085] The reminder unit can apply different reminder algorithms depending on the task category when sending a reminder. For example, the reminder unit can apply a project management-specific reminder algorithm to project management tasks. For example, the reminder unit can apply a rule-based algorithm specifically for project management tasks. The reminder unit can also apply a reminder algorithm specifically for individual tasks. For example, the reminder unit can apply a machine learning algorithm specifically for individual tasks. The reminder unit can also apply a reminder algorithm specifically for team tasks. For example, the reminder unit can apply a classification algorithm specifically for team tasks. By applying different reminder algorithms depending on the task category, the system can provide the most appropriate reminder for each category. Task categories include, but are not limited to, business tasks and individual tasks. Reminder algorithms include, but are not limited to, rule-based algorithms, machine learning algorithms, and classification algorithms. Some or all of the above processing in the reminder unit may be performed using, for example, AI, or without using AI. For example, the reminder unit can input task category data into the generation AI and have the generation AI execute the application of a reminder algorithm.

[0086] The reminder function can estimate the user's emotions and determine the priority of reminders based on those emotions. For example, if the user is stressed, the reminder function will prioritize reminding users of high-priority tasks. Alternatively, if the user is relaxed, the reminder function can evenly distribute reminders across all tasks. Furthermore, if the user is in a hurry, the reminder function can prioritize reminding users of high urgency tasks. This allows for prioritizing important tasks by determining reminder priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, 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 reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input user emotion data into a generating AI and have the generating AI determine the priority of reminders.

[0087] The reminder unit can adjust the order of reminders based on the task submission dates. For example, the reminder unit can prioritize reminding tasks with approaching deadlines. The reminder unit can also postpone reminding tasks with later submission dates. The reminder unit can also remind tasks with unknown submission dates after other tasks have been reminded. By adjusting the order of reminders based on the task submission dates, tasks with approaching deadlines can be prioritized. The task submission dates include, but are not limited to, submission deadlines and submission dates. Some or all of the above processing in the reminder unit may be performed using, for example, AI, or not. For example, the reminder unit can input task submission time data into the generating AI and have the generating AI adjust the order of reminders.

[0088] The reminder unit can adjust the order of reminders based on the relevance of tasks. For example, the reminder unit can prioritize reminding users of highly relevant tasks. The reminder unit can also postpone reminding users of less relevant tasks. The reminder unit can also group related tasks together and remind users of those groups together. This allows the system to prioritize reminding users of highly relevant tasks by adjusting the order of reminders based on the relevance of tasks. Task relevance includes, but is not limited to, common keywords and related topics. Some or all of the above processing in the reminder unit may be performed using, for example, AI, or not. For example, the reminder unit can input task relevance data into a generating AI and have the generating AI adjust the order of reminders.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] A task reminder system can estimate a user's emotions and dynamically change task priorities based on those emotions. For example, if a user is stressed, the system will prioritize reminding them of high-priority tasks, reducing their burden. If a user is relaxed, the system can remind them of all tasks equally. Furthermore, if a user is in a hurry, the system can prioritize reminding them of urgent tasks. By dynamically changing task priorities according to the user's emotions, the system reduces the user's burden and allows them to complete tasks efficiently.

[0091] A task reminder system can analyze a user's past task history to determine the optimal reminder timing. For example, it can identify the time periods when a user has previously completed important tasks and send reminders during those times. It can also identify times when a user tends to forget tasks and strengthen reminders during those periods. Furthermore, it can analyze how often a user has completed tasks in the past and adjust reminders based on that frequency. In this way, by analyzing past task history, the system can determine the optimal reminder timing and prevent tasks from being missed.

[0092] The task reminder system can provide reminders based on the user's geographical location. For example, if the user is in the office, it can remind them of office-related tasks. If the user is at home, it can remind them of tasks to be done at home. Furthermore, if the user is traveling, it can remind them of tasks related to their destination. This allows for the provision of appropriate reminders tailored to the user's situation by using geographical location information.

[0093] A task reminder system can analyze a user's social media activity and remind them of relevant tasks. For example, it can remind users of tasks they have mentioned on social media. It can also prioritize reminders of messages from accounts the user follows on social media. Furthermore, it can remind users of tasks related to events they are participating in on social media. This allows for efficient task reminders by analyzing social media activity.

[0094] A task reminder system can estimate a user's emotions and adjust the reminder method based on those emotions. For example, if a user is stressed, it can provide a simple and highly visible reminder. If the user is relaxed, it can provide a reminder with more detailed information. Furthermore, if the user is in a hurry, it can provide a concise reminder. By adjusting the reminder method according to the user's emotions, it can provide the most optimal reminder method for the user.

[0095] A task reminder system can estimate a user's emotions and prioritize reminders based on those emotions. For example, if a user is stressed, it will prioritize reminders for high-priority tasks. If a user is relaxed, it can evenly distribute reminders for all tasks. Furthermore, if a user is in a hurry, it can prioritize reminders for urgent tasks. This allows important tasks to be prioritized by determining reminder priorities according to the user's emotions.

[0096] A task reminder system can estimate a user's emotions and adjust the timing of reminders based on those emotions. For example, if a user is stressed, the frequency of reminders can be reduced to lessen their burden. Conversely, if a user is relaxed, the frequency of reminders can be increased to allow them to grasp more tasks. Furthermore, if a user is busy, the timing of reminders can be adjusted to avoid interrupting their work. In this way, by adjusting the timing of reminders according to the user's emotions, the burden on the user can be reduced.

[0097] A task reminder system can provide reminders based on the user's current projects and areas of interest. For example, it can remind users of tasks related to projects they are currently working on. It can also prioritize reminders for tasks related to the user's areas of interest. Furthermore, if a user is focused on a specific project, it can remind users only of tasks related to that project. This allows for efficient reminders of highly relevant tasks by basing reminders on current projects and areas of interest.

[0098] A task reminder system can estimate a user's emotions and adjust the content of the reminder based on those emotions. For example, if a user is stressed, it can provide a simple and highly visible reminder. If the user is relaxed, it can provide a reminder with more detailed information. Furthermore, if the user is in a hurry, it can provide a concise reminder. By adjusting the content of the reminder according to the user's emotions, it can provide the most optimal reminder for the user.

[0099] A task reminder system can estimate the user's emotions and adjust the reminder format based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible reminder format. If the user is relaxed, it can provide a reminder format with more detailed information. Furthermore, if the user is in a hurry, it can provide a concise reminder format. By adjusting the reminder format according to the user's emotions, it can provide the most optimal reminder format for the user.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The collection unit collects data from at least one of the following: chat, email, and voice messages. For example, the collection unit can collect users' chat messages in real time, periodically collect new emails from their mailbox, and convert voice messages into text data using speech recognition technology and collect them. Step 2: The analysis unit analyzes the data collected by the collection unit and extracts the tasks and corresponding dates and times. For example, the analysis unit uses natural language processing technology to analyze the data, understanding keywords, sentence structures, and meanings related to the tasks using morphological analysis, grammatical analysis, and semantic analysis, and identifies the tasks and corresponding dates and times. Step 3: The registration unit registers the tasks and corresponding dates extracted by the analysis unit into the reminder. For example, the registration unit can register tasks in a task management application, a calendar application, or a reminder application. Step 4: The reminder unit sends reminders to recipients based on tasks registered by the registration unit. For example, the reminder unit can remind managers of tasks assigned to their subordinates, tasks for project members, and tasks for family and friends.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] Each of the multiple elements described above, including the collection unit, analysis unit, registration unit, and reminder 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 collects chat messages, emails, and voice messages. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to extract tasks and corresponding dates and times. The registration unit is implemented by the control unit 46A of the smart device 14 and registers the extracted tasks and corresponding dates and times in the reminder. The reminder unit is implemented by the identification processing unit 290 of the data processing unit 12 and sends a reminder to the recipient based on the registered tasks. 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.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] Each of the multiple elements described above, including the collection unit, analysis unit, registration unit, and reminder 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 collects chat messages, emails, and voice messages. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to extract tasks and corresponding dates and times. The registration unit is implemented by the control unit 46A of the smart glasses 214 and registers the extracted tasks and corresponding dates and times in the reminder. The reminder unit is implemented by the identification processing unit 290 of the data processing unit 12 and sends a reminder to the recipient based on the registered tasks. 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.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements described above, including the collection unit, analysis unit, registration unit, and reminder 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 collects chat messages, emails, and voice messages. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to extract tasks and corresponding dates and times. The registration unit is implemented by the control unit 46A of the headset terminal 314 and registers the extracted tasks and corresponding dates and times in the reminder. The reminder unit is implemented by the identification processing unit 290 of the data processing unit 12 and sends a reminder to the recipient based on the registered tasks. 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.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the collection unit, analysis unit, registration unit, and reminder 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 collects chat messages, emails, and voice messages. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to extract tasks and corresponding dates and times. The registration unit is implemented by the control unit 46A of the robot 414 and registers the extracted tasks and corresponding dates and times in the reminder. The reminder unit is implemented by the identification processing unit 290 of the data processing unit 12 and sends a reminder to the recipient based on the registered tasks. 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] (Note 1) A collection unit that collects data from at least one of the following: chat, email, and voice messages, An analysis unit analyzes the data collected by the aforementioned collection unit and extracts tasks and corresponding dates and times. A registration unit registers the tasks and corresponding dates and times extracted by the analysis unit into a reminder, The system includes a reminder unit that sends reminders to the recipient based on tasks registered by the registration unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze data using natural language processing techniques. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Analyze the user's past communication history and select the appropriate data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned registration unit is The system estimates the user's emotions and adjusts the registration process based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned registration unit is When registering, adjust the level of detail in the registration based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned registration unit is During registration, different registration algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned registration unit is The system estimates user sentiment and determines registration priority based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned registration unit is During registration, the order of registration will be adjusted based on the timing of task submission. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned registration unit is During registration, the registration order is adjusted based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reminder unit, It estimates the user's emotions and adjusts the reminder method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reminder unit, When sending reminders, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reminder unit, When sending reminders, apply different reminder algorithms depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reminder unit, It estimates the user's emotions and determines the priority of reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reminder unit, When sending reminders, adjust the order of reminders based on the task submission date. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reminder unit, When sending reminders, adjust the order of reminders based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0174] 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 unit that collects data from at least one of the following: chat, email, and voice messages, An analysis unit analyzes the data collected by the aforementioned collection unit and extracts tasks and corresponding dates and times. A registration unit registers the tasks and corresponding dates and times extracted by the analysis unit into a reminder, The system includes a reminder unit that sends reminders to the recipient based on tasks registered by the registration unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze data using natural language processing techniques. The system according to feature 1.

3. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

4. The aforementioned collection unit is Analyze the user's past communication history and select the appropriate data collection method. The system according to feature 1.

5. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data based on the user's geographical location. The system according to feature 1.

8. The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system according to feature 1.

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