system

Abstract

The system according to this embodiment aims to efficiently manage and prioritize daily tasks. [Solution] The system according to the embodiment comprises a reception unit, a determination unit, a generation unit, and a provision unit. The reception unit receives task input. The determination unit analyzes the tasks received by the reception unit and determines their priority. The generation unit generates a task list based on the priority determined by the determination unit. The provision unit provides the task list generated by the generation 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to efficiently manage daily tasks and prioritize them.

[0005] The system according to the embodiment aims to efficiently manage daily tasks and prioritize them.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a determination unit, a generation unit, and a provision unit. The reception unit receives an input of a task. The determination unit analyzes the task received by the reception unit and determines a priority. The generation unit generates a task list based on the priority determined by the determination unit. The provision unit provides the task list generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage and prioritize daily tasks. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The task management system according to an embodiment of the present invention is a system that utilizes generative AI to support users in managing their tasks. This task management system provides a summary of tasks to be performed that day when the user wakes up in the morning, and also provides a summary of things to review before going to bed at night. This system is particularly targeted at people who tend to forget tasks or who have difficulty organizing tasks. For example, when a user wakes up in the morning, the generative AI provides a summary of tasks to be performed that day. The generative AI analyzes tasks from the user's input and determines their priority. For example, if the user inputs "What are my tasks today?", the generative AI generates a task list and manages deadlines. Next, before going to bed at night, the generative AI provides a summary of the day's review. The generative AI learns the user's behavior patterns and past task data to provide more personalized reminders and advice. For example, if the user inputs "Tell me what I should review today," the generative AI provides a summary of the day's task completion status and areas for improvement. Furthermore, the system incorporates speech recognition technology, providing a function to add tasks using only voice input. For example, if a user voice-inputs "Add tomorrow's meeting," the AI ​​generator will add the meeting to the task list. This system allows users to manage tasks efficiently without forgetting them. This reduces user time management and stress, and improves individual productivity. In this way, task management systems can efficiently support users in managing their tasks.

[0029] The task management system according to this embodiment comprises a reception unit, a judgment unit, a generation unit, and a provision unit. The reception unit receives task input. The reception unit allows users to input tasks using, for example, text input or voice input. The reception unit can also save the tasks entered by the user to a database. The judgment unit analyzes the tasks received by the reception unit and determines their priority. The judgment unit determines the priority based on, for example, the urgency or importance of the tasks. The judgment unit can also determine the priority of tasks using a generation AI. The generation unit generates a task list based on the priority determined by the judgment unit. The generation unit generates a task list based on the priority of tasks and provides it to the user. The generation unit can also generate a task list using a generation AI. The provision unit provides the task list generated by the generation unit to the user. The provision unit displays the task list on the user's device, for example. The provision unit can also send the task list to the user via email or notification. As a result, the task management system according to this embodiment can efficiently support the user's task management.

[0030] The reception desk accepts task inputs. For example, users can input tasks using text or voice input. Specifically, users can input tasks in text format using devices such as smartphones or computers. For voice input, users input tasks by voice through a microphone, and speech recognition technology converts them to text. This allows users to easily input tasks. Furthermore, the reception desk can save user-inputted tasks to a database. The database stores detailed data, including task content, input date and time, and user information, for later reference. This allows users to easily review previously entered tasks. The reception desk can also provide an auto-completion function during task input. For example, as a user inputs part of a task, it suggests appropriate suggestions based on past tasks and common task patterns. This allows users to input tasks efficiently. Additionally, the reception desk is designed to handle tasks smoothly even when multiple users are inputting tasks simultaneously. This allows for task management in teams and groups.

[0031] The decision unit analyzes tasks received by the reception unit and determines their priority. For example, the decision unit determines priority based on the urgency and importance of the task. Specifically, it analyzes the content of the task and evaluates how urgently it needs to be addressed or how important it is. The decision unit can also determine task priority using generative AI. Generative AI learns from past task data and user behavior patterns to automatically evaluate the urgency and importance of tasks. For example, generative AI analyzes keywords and phrases included in the content of a task and determines priority based on that. It can also adjust priority based on the user's past task completion history and the type of task. This allows the decision unit to provide the optimal task priority tailored to the user's needs. Furthermore, the decision unit also considers task dependencies and deadlines when determining priority. For example, if a task depends on the completion of other tasks, it sets priority considering those dependencies. Also, if a task's deadline is approaching, it sets a higher priority for that task. This allows the decision unit to help users manage tasks efficiently.

[0032] The generation unit generates a task list based on the priority determined by the decision unit. For example, the generation unit generates a task list based on task priority and provides it to the user. Specifically, the generation unit places high-priority tasks at the top of the list and low-priority tasks at the bottom. This allows the user to see important tasks at a glance. The generation unit can also generate task lists using generation AI. The generation AI automatically generates an optimal task list by considering the user's past task management patterns and current situation. For example, the generation AI learns what tasks the user tends to complete at a particular time and adjusts the task list based on that information. The generation AI can also optimize task assignment by considering the user's current schedule and resource utilization. This allows the generation unit to help the user complete tasks efficiently. Furthermore, the generation unit also provides a function to customize the display format of the task list. For example, the user can view the task list by day, week, or month. It is also possible to filter by task category or project. This allows the generation unit to provide a flexible task list tailored to the user's needs.

[0033] The service provider provides users with task lists generated by the generation unit. The service provider displays the task list on the user's device, for example, visually through a smartphone or PC application. Users can review the task list and manage the progress of their tasks. The service provider can also send the task list to users via email or notifications. For example, if an important task is nearing its deadline, the service provider can send a reminder notification to the user to encourage completion. It can also periodically send updates to the task list via email, ensuring users always have the latest task list information. Furthermore, the service provider provides a task list sharing function. For example, users can share task lists with team members or project members to collaboratively manage tasks. The service provider also offers a function to customize task list sharing settings. For example, it's possible to share only specific tasks or display the task list only to specific members. This allows the service provider to enable users to efficiently manage tasks and promote team collaboration. Additionally, the service provider can collect user feedback to improve task list display and notifications. This allows the service provider to offer an optimal task management experience tailored to the user's needs.

[0034] The generation unit can generate task lists using a generation AI. For example, the generation unit can analyze tasks from user input using the generation AI and generate a task list. The generation unit can also generate more personalized task lists by having the generation AI learn the user's behavior patterns and past task data. The generation unit can also have the generation AI determine the priority of tasks and generate a task list based on that priority. This improves the accuracy of task list generation by using the generation AI. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit inputs user input data into the generation AI, and the generation AI generates a task list.

[0035] The generation unit can learn user behavior patterns and past task data using a generation AI and provide reminders and advice. For example, the generation unit can analyze user behavior patterns using the generation AI and provide reminders and advice. The generation unit can also have the generation AI learn past task data and provide personalized reminders and advice to the user. The generation unit can also have the generation AI generate reminders and advice based on user behavior patterns and past task data. This allows for the provision of personalized reminders and advice to the user. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit inputs user behavior pattern data into the generation AI, and the generation AI generates reminders and advice.

[0036] The reception unit can accept voice input. For example, the reception unit can allow users to input tasks by voice. The reception unit can also convert voice input into text data using speech recognition technology. After receiving the voice input, the reception unit can also save it as text data. This allows users to easily input tasks by supporting voice input. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs voice data into the AI, and the AI ​​converts the voice data into text data.

[0037] The service provider can provide the generated task list to the user. For example, the service provider can display the task list on the user's device. The service provider can also send the task list to the user via email or notification. The service provider can also print the task list and provide it on paper. This makes task management easier by providing the user with the generated task list. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider inputs the generated task list into the AI, and the AI ​​provides the task list to the user.

[0038] The decision unit can determine the priority of tasks using a generative AI. For example, the decision unit can use the generative AI to analyze the urgency and importance of tasks and determine their priority. The decision unit can also have the generative AI determine the priority of tasks based on user input data. The decision unit can also have the generative AI determine the priority of tasks and provide the result to the user. This improves the accuracy of task priority determination by using the generative AI. Some or all of the above-described processes in the decision unit may be performed using the generative AI or not. For example, the decision unit inputs user input data into the generative AI, and the generative AI determines the priority of tasks.

[0039] The reception desk can analyze the user's past task input history and provide the optimal input interface. For example, the reception desk can automatically display tasks that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest tasks to be used during specific time periods based on the user's past input history. In this way, by analyzing past task input history, the reception desk can provide the user with the optimal input interface. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past task input history data into an AI, and the AI ​​can provide the optimal input interface.

[0040] The reception desk can filter input content based on the user's current situation and environment when tasks are entered. For example, if the user is in a meeting, the reception desk will prioritize inputting tasks related to the meeting. If the user is traveling, the reception desk can also prioritize inputting tasks related to travel. If the user is at home, the reception desk can also prioritize inputting tasks to be performed at home. This enables task input that is tailored to the user's situation and environment. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's current situation and environment data into the AI, and the AI ​​filters the input content.

[0041] The reception desk can prioritize the input of highly relevant tasks by considering the user's geographical location when tasks are entered. For example, if the user is in the office, the reception desk will prioritize tasks to be performed in the office. If the user is out, the reception desk can also prioritize tasks to be performed while out. If the user is at home, the reception desk can also prioritize tasks to be performed at home. This allows for the priority input of highly relevant tasks based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's geographical location data into the AI, and the AI ​​prioritizes the input of highly relevant tasks.

[0042] The reception desk can analyze a user's social media activity when a task is entered and enter relevant tasks. For example, the reception desk can automatically enter tasks that the user has mentioned on social media. The reception desk can also enter tasks related to events that the user has shared on social media. The reception desk can also enter tasks related to accounts that the user follows on social media. This allows relevant tasks to be entered based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity data into the AI, and the AI ​​can enter relevant tasks.

[0043] The decision-making unit can adjust its criteria for prioritizing tasks based on their importance and urgency. For example, the decision-making unit may prioritize tasks with high importance. It can also prioritize tasks with high urgency. The decision-making unit may also process tasks considering the balance between importance and urgency. This allows the decision-making unit to determine priorities based on the importance and urgency of tasks. Some or all of the above processing in the decision-making unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the decision-making unit inputs task importance and urgency data into a generative AI, and the generative AI adjusts the decision criteria.

[0044] The decision-making unit can improve the accuracy of its decision-making when determining task priorities by referring to the user's past task completion status. For example, the decision-making unit can determine priorities based on data of tasks the user has completed in the past. The decision-making unit can also prioritize tasks that are easier to complete based on the user's past task completion status. The decision-making unit can also analyze the user's past task completion status and determine the optimal priority. This improves the accuracy of priority determination by referring to the user's past task completion status. Some or all of the above processing in the decision-making unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the decision-making unit inputs data on the user's past task completion status into a generative AI, and the generative AI improves the accuracy of the decision-making.

[0045] The decision-making unit can consider the geographical distribution of tasks when determining task priorities. For example, if the user is in the office, the decision-making unit will prioritize tasks performed in the office. If the user is out of the office, the decision-making unit can also prioritize tasks performed while out of the office. If the user is at home, the decision-making unit can also prioritize tasks performed at home. This allows for more appropriate task management by considering the geographical distribution of tasks. Some or all of the above processing in the decision-making unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the decision-making unit inputs geographical distribution data of tasks into a generative AI, and the generative AI makes the decision.

[0046] The decision-making unit can improve the accuracy of its decision-making when determining task priorities by referring to relevant literature and data. For example, the decision-making unit can refer to relevant literature to determine the importance of a task. The decision-making unit can also determine the urgency of a task based on data. The decision-making unit can also analyze relevant literature and data to determine the optimal priority. As a result, the accuracy of priority determination is improved by referring to relevant literature and data. Some or all of the above processes in the decision-making unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the decision-making unit inputs relevant literature and data into a generative AI, and the generative AI improves the accuracy of the decision-making.

[0047] The generation unit can adjust the level of detail in a task list based on the importance and urgency of the tasks when generating the task list. For example, the generation unit will describe high-importance tasks in detail. The generation unit can also describe high-urgency tasks in detail. The generation unit can also adjust the level of detail in the list considering the balance between importance and urgency. By adjusting the level of detail in the list based on the importance and urgency of the tasks, a more appropriate task list is generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs task importance and urgency data into the generation AI, and the generation AI adjusts the level of detail in the list.

[0048] The generation unit can generate a more personalized list by learning the user's behavior patterns and past task data when generating a task list. For example, the generation unit can generate an optimal task list based on the user's past task data. The generation unit can also learn the user's behavior patterns and generate an efficient task list. The generation unit can also analyze the user's past task data and behavior patterns to generate a personalized list. As a result, a more personalized task list is generated by learning the user's behavior patterns and past task data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the user's behavior pattern data into a generation AI, and the generation AI generates a personalized list.

[0049] The generation unit can determine the priority of tasks in a task list based on their submission dates when generating the list. For example, the generation unit may prioritize tasks with approaching deadlines. It may also postpone tasks with later deadlines. The generation unit may also determine the priority of tasks by considering the balance of submission dates. This results in a more appropriate task list being generated by determining the priority of tasks based on their submission dates. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs task submission date data into a generation AI, and the generation AI determines the priority of the list.

[0050] The generation unit can adjust the order of tasks in a task list based on their relevance. For example, it can group highly relevant tasks together in the list. It can also distribute less relevant tasks among them. The generation unit can adjust the order of tasks based on their relevance. This allows for the generation of a more efficient task list by adjusting the order of tasks based on their relevance. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs task relevance data into a generation AI, and the generation AI adjusts the order of the list.

[0051] The service provider can select the optimal delivery method when providing a task list by referring to the user's past task completion status. For example, the service provider can select the optimal delivery method based on data of tasks the user has completed in the past. The service provider can also provide the task list in a way that is easy for the user to complete, based on the user's past task completion status. The service provider can also analyze the user's past task completion status and select the optimal delivery method. This allows the service provider to select the optimal delivery method by referring to the user's past task completion status. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past task completion status data into the AI, and the AI ​​can select the optimal delivery method.

[0052] The service provider can customize the task list provided based on the user's current situation and environment. For example, if the user is in a meeting, the service provider will provide a task list related to the meeting. If the user is traveling, the service provider can also provide a task list related to travel. If the user is at home, the service provider can also provide a task list to be done at home. By customizing the task list based on the user's current situation and environment, a more appropriate task list will be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's current situation and environment data into the AI, and the AI ​​will customize the task list.

[0053] The service provider can select the optimal service delivery method when providing a task list, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can select a delivery method that matches the screen size. If the user is using a tablet, the service provider can also select a delivery method optimized for a larger screen. If the user is using a smartwatch, the service provider can also select a concise and highly visible delivery method. In this way, the service provider can select the optimal service delivery method by taking into account the user's device information. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's device information into the AI, and the AI ​​can select the optimal service delivery method.

[0054] The service provider can analyze the user's social media activity and customize the content provided when delivering task lists. For example, the service provider can provide a customized task list based on tasks mentioned by the user on social media. The service provider can also provide task lists related to events shared by the user on social media. The service provider can also provide task lists related to accounts followed by the user on social media. This allows for the provision of more appropriate task lists by analyzing the user's social media activity. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into AI, which then customizes the content provided.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] Task management systems can acquire user health data and adjust task priorities based on their health status. For example, by analyzing a user's heart rate and sleep data, they can postpone less important tasks if the user is fatigued. Conversely, if the user is in good health, they can prioritize tasks requiring concentration. Furthermore, they can suggest appropriate break times based on the user's health data. This enables task management tailored to the user's health status, allowing for more efficient task execution.

[0057] A task management system can suggest tasks based on the user's hobbies and interests. For example, if a user enjoys reading, it will prioritize suggesting reading-related tasks. Similarly, if a user enjoys sports, it can include exercise-related tasks in the list. Furthermore, it can suggest new hobbies and activities based on the user's interests. This enables task management tailored to the user's hobbies and interests, which is expected to improve motivation.

[0058] A task management system can analyze a user's past task completion history and prioritize suggesting tasks that are easier to complete. For example, it can prioritize including similar tasks in the list based on data from tasks the user has completed in the past. It can also suggest avoiding tasks that the user has struggled with in the past. Furthermore, it can suggest the optimal combination of tasks based on the user's past task completion history. This enables task management based on the user's past performance, allowing for efficient task execution.

[0059] A task management system can suggest optimal tasks by considering the user's geographical location. For example, if the user is in the office, tasks to be performed in the office will be prioritized. If the user is out of the office, tasks to be performed while away from the office can also be included in the list. Furthermore, if the user is at home, tasks to be performed at home can be prioritized. This enables task management based on the user's geographical location, allowing for efficient task completion.

[0060] Task management systems can analyze users' social media activity and suggest relevant tasks. For example, they can include tasks related to events users have mentioned on social media in their lists. They can also suggest tasks related to accounts users follow. Furthermore, they can suggest new tasks based on content users have shared. This enables task management based on users' social media activity, allowing for efficient task completion.

[0061] A task management system can provide the optimal task display method by taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that is adapted to the screen size. If the user is using a tablet, it can provide a display method optimized for the larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. This enables task display based on the user's device information, resulting in efficient task management.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk accepts task inputs. Users can input tasks using text or voice input, and the reception desk can also save these tasks to a database. Step 2: The decision unit analyzes the tasks received by the reception unit and determines their priority. The decision unit determines priority based on the urgency and importance of the tasks, and can also determine task priority using a generation AI. Step 3: The generation unit generates a task list based on the priority determined by the decision unit. The generation unit can generate a task list based on the priority of the tasks, or it can generate a task list using a generation AI. Step 4: The providing unit provides the user with the task list generated by the generating unit. The providing unit can also display the task list on the user's device and send it to the user via email or notification.

[0064] (Example of form 2) The task management system according to an embodiment of the present invention is a system that utilizes generative AI to support users in managing their tasks. This task management system provides a summary of tasks to be performed that day when the user wakes up in the morning, and also provides a summary of things to review before going to bed at night. This system is particularly targeted at people who tend to forget tasks or who have difficulty organizing tasks. For example, when a user wakes up in the morning, the generative AI provides a summary of tasks to be performed that day. The generative AI analyzes tasks from the user's input and determines their priority. For example, if the user inputs "What are my tasks today?", the generative AI generates a task list and manages deadlines. Next, before going to bed at night, the generative AI provides a summary of the day's review. The generative AI learns the user's behavior patterns and past task data to provide more personalized reminders and advice. For example, if the user inputs "Tell me what I should review today," the generative AI provides a summary of the day's task completion status and areas for improvement. Furthermore, the system incorporates speech recognition technology, providing a function to add tasks using only voice input. For example, if a user voice-inputs "Add tomorrow's meeting," the AI ​​generator will add the meeting to the task list. This system allows users to manage tasks efficiently without forgetting them. This reduces user time management and stress, and improves individual productivity. In this way, task management systems can efficiently support users in managing their tasks.

[0065] The task management system according to this embodiment comprises a reception unit, a judgment unit, a generation unit, and a provision unit. The reception unit receives task input. The reception unit allows users to input tasks using, for example, text input or voice input. The reception unit can also save the tasks entered by the user to a database. The judgment unit analyzes the tasks received by the reception unit and determines their priority. The judgment unit determines the priority based on, for example, the urgency or importance of the tasks. The judgment unit can also determine the priority of tasks using a generation AI. The generation unit generates a task list based on the priority determined by the judgment unit. The generation unit generates a task list based on the priority of tasks and provides it to the user. The generation unit can also generate a task list using a generation AI. The provision unit provides the task list generated by the generation unit to the user. The provision unit displays the task list on the user's device, for example. The provision unit can also send the task list to the user via email or notification. As a result, the task management system according to this embodiment can efficiently support the user's task management.

[0066] The reception desk accepts task inputs. For example, users can input tasks using text or voice input. Specifically, users can input tasks in text format using devices such as smartphones or computers. For voice input, users input tasks by voice through a microphone, and speech recognition technology converts them to text. This allows users to easily input tasks. Furthermore, the reception desk can save user-inputted tasks to a database. The database stores detailed data, including task content, input date and time, and user information, for later reference. This allows users to easily review previously entered tasks. The reception desk can also provide an auto-completion function during task input. For example, as a user inputs part of a task, it suggests appropriate suggestions based on past tasks and common task patterns. This allows users to input tasks efficiently. Additionally, the reception desk is designed to handle tasks smoothly even when multiple users are inputting tasks simultaneously. This allows for task management in teams and groups.

[0067] The decision unit analyzes tasks received by the reception unit and determines their priority. For example, the decision unit determines priority based on the urgency and importance of the task. Specifically, it analyzes the content of the task and evaluates how urgently it needs to be addressed or how important it is. The decision unit can also determine task priority using generative AI. Generative AI learns from past task data and user behavior patterns to automatically evaluate the urgency and importance of tasks. For example, generative AI analyzes keywords and phrases included in the content of a task and determines priority based on that. It can also adjust priority based on the user's past task completion history and the type of task. This allows the decision unit to provide the optimal task priority tailored to the user's needs. Furthermore, the decision unit also considers task dependencies and deadlines when determining priority. For example, if a task depends on the completion of other tasks, it sets priority considering those dependencies. Also, if a task's deadline is approaching, it sets a higher priority for that task. This allows the decision unit to help users manage tasks efficiently.

[0068] The generation unit generates a task list based on the priority determined by the decision unit. For example, the generation unit generates a task list based on task priority and provides it to the user. Specifically, the generation unit places high-priority tasks at the top of the list and low-priority tasks at the bottom. This allows the user to see important tasks at a glance. The generation unit can also generate task lists using generation AI. The generation AI automatically generates an optimal task list by considering the user's past task management patterns and current situation. For example, the generation AI learns what tasks the user tends to complete at a particular time and adjusts the task list based on that information. The generation AI can also optimize task assignment by considering the user's current schedule and resource utilization. This allows the generation unit to help the user complete tasks efficiently. Furthermore, the generation unit also provides a function to customize the display format of the task list. For example, the user can view the task list by day, week, or month. It is also possible to filter by task category or project. This allows the generation unit to provide a flexible task list tailored to the user's needs.

[0069] The service provider provides users with task lists generated by the generation unit. The service provider displays the task list on the user's device, for example, visually through a smartphone or PC application. Users can review the task list and manage the progress of their tasks. The service provider can also send the task list to users via email or notifications. For example, if an important task is nearing its deadline, the service provider can send a reminder notification to the user to encourage completion. It can also periodically send updates to the task list via email, ensuring users always have the latest task list information. Furthermore, the service provider provides a task list sharing function. For example, users can share task lists with team members or project members to collaboratively manage tasks. The service provider also offers a function to customize task list sharing settings. For example, it's possible to share only specific tasks or display the task list only to specific members. This allows the service provider to enable users to efficiently manage tasks and promote team collaboration. Additionally, the service provider can collect user feedback to improve task list display and notifications. This allows the service provider to offer an optimal task management experience tailored to the user's needs.

[0070] The generation unit can generate task lists using a generation AI. For example, the generation unit can analyze tasks from user input using the generation AI and generate a task list. The generation unit can also generate more personalized task lists by having the generation AI learn the user's behavior patterns and past task data. The generation unit can also have the generation AI determine the priority of tasks and generate a task list based on that priority. This improves the accuracy of task list generation by using the generation AI. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit inputs user input data into the generation AI, and the generation AI generates a task list.

[0071] The generation unit can learn user behavior patterns and past task data using a generation AI and provide reminders and advice. For example, the generation unit can analyze user behavior patterns using the generation AI and provide reminders and advice. The generation unit can also have the generation AI learn past task data and provide personalized reminders and advice to the user. The generation unit can also have the generation AI generate reminders and advice based on user behavior patterns and past task data. This allows for the provision of personalized reminders and advice to the user. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit inputs user behavior pattern data into the generation AI, and the generation AI generates reminders and advice.

[0072] The reception unit can accept voice input. For example, the reception unit can allow users to input tasks by voice. The reception unit can also convert voice input into text data using speech recognition technology. After receiving the voice input, the reception unit can also save it as text data. This allows users to easily input tasks by supporting voice input. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs voice data into the AI, and the AI ​​converts the voice data into text data.

[0073] The service provider can provide the generated task list to the user. For example, the service provider can display the task list on the user's device. The service provider can also send the task list to the user via email or notification. The service provider can also print the task list and provide it on paper. This makes task management easier by providing the user with the generated task list. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider inputs the generated task list into the AI, and the AI ​​provides the task list to the user.

[0074] The decision unit can determine the priority of tasks using a generative AI. For example, the decision unit can use the generative AI to analyze the urgency and importance of tasks and determine their priority. The decision unit can also have the generative AI determine the priority of tasks based on user input data. The decision unit can also have the generative AI determine the priority of tasks and provide the result to the user. This improves the accuracy of task priority determination by using the generative AI. Some or all of the above-described processes in the decision unit may be performed using the generative AI or not. For example, the decision unit inputs user input data into the generative AI, and the generative AI determines the priority of tasks.

[0075] The reception desk can estimate the user's emotions and adjust the task input method based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick task input. This allows for more appropriate task input by adjusting the task input 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 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 reception desk may be performed using AI or not. For example, the reception desk inputs the user's emotion data into a generative AI, which estimates the emotions and adjusts the task input method.

[0076] The reception desk can analyze the user's past task input history and provide the optimal input interface. For example, the reception desk can automatically display tasks that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest tasks to be used during specific time periods based on the user's past input history. In this way, by analyzing past task input history, the reception desk can provide the user with the optimal input interface. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past task input history data into an AI, and the AI ​​can provide the optimal input interface.

[0077] The reception desk can filter input content based on the user's current situation and environment when tasks are entered. For example, if the user is in a meeting, the reception desk will prioritize inputting tasks related to the meeting. If the user is traveling, the reception desk can also prioritize inputting tasks related to travel. If the user is at home, the reception desk can also prioritize inputting tasks to be performed at home. This enables task input that is tailored to the user's situation and environment. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's current situation and environment data into the AI, and the AI ​​filters the input content.

[0078] The reception desk can estimate the user's emotions and determine the priority of input tasks based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize high-priority tasks. If the user is relaxed, the reception desk may also prioritize long-term tasks. If the user is in a hurry, the reception desk may also prioritize high-urgency tasks. This allows for more appropriate task management by prioritizing tasks according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs user emotion data into a generative AI, which estimates the emotions and determines the task priority.

[0079] The reception desk can prioritize the input of highly relevant tasks by considering the user's geographical location when tasks are entered. For example, if the user is in the office, the reception desk will prioritize tasks to be performed in the office. If the user is out, the reception desk can also prioritize tasks to be performed while out. If the user is at home, the reception desk can also prioritize tasks to be performed at home. This allows for the priority input of highly relevant tasks based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's geographical location data into the AI, and the AI ​​prioritizes the input of highly relevant tasks.

[0080] The reception desk can analyze a user's social media activity when a task is entered and enter relevant tasks. For example, the reception desk can automatically enter tasks that the user has mentioned on social media. The reception desk can also enter tasks related to events that the user has shared on social media. The reception desk can also enter tasks related to accounts that the user follows on social media. This allows relevant tasks to be entered based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity data into the AI, and the AI ​​can enter relevant tasks.

[0081] The decision unit can estimate the user's emotions and adjust task priorities based on the estimated emotions. For example, if the user is stressed, the decision unit will prioritize high-priority tasks. If the user is relaxed, the decision unit may also prioritize long-term tasks. If the user is in a hurry, the decision unit may also prioritize high-urgency tasks. This allows for more appropriate task management by adjusting task priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision unit may be performed using or without a generative AI. For example, the decision unit inputs user emotion data into a generative AI, the generative AI estimates the emotions, and adjusts task priorities.

[0082] The decision-making unit can adjust its criteria for prioritizing tasks based on their importance and urgency. For example, the decision-making unit may prioritize tasks with high importance. It can also prioritize tasks with high urgency. The decision-making unit may also process tasks considering the balance between importance and urgency. This allows the decision-making unit to determine priorities based on the importance and urgency of tasks. Some or all of the above processing in the decision-making unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the decision-making unit inputs task importance and urgency data into a generative AI, and the generative AI adjusts the decision criteria.

[0083] The decision-making unit can improve the accuracy of its decision-making when determining task priorities by referring to the user's past task completion status. For example, the decision-making unit can determine priorities based on data of tasks the user has completed in the past. The decision-making unit can also prioritize tasks that are easier to complete based on the user's past task completion status. The decision-making unit can also analyze the user's past task completion status and determine the optimal priority. This improves the accuracy of priority determination by referring to the user's past task completion status. Some or all of the above processing in the decision-making unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the decision-making unit inputs data on the user's past task completion status into a generative AI, and the generative AI improves the accuracy of the decision-making.

[0084] The decision unit can estimate the user's emotions and adjust the order in which tasks are displayed based on the estimated emotions. For example, if the user is stressed, the decision unit will display high-importance tasks at the top. If the user is relaxed, the decision unit may also display long-term tasks at the top. If the user is in a hurry, the decision unit may also display high-urgency tasks at the top. This allows for more appropriate task management by adjusting the order in which tasks are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision unit may be performed using or without a generative AI. For example, the decision unit inputs user emotion data into a generative AI, the generative AI estimates the emotions, and adjusts the order in which tasks are displayed.

[0085] The decision-making unit can consider the geographical distribution of tasks when determining task priorities. For example, if the user is in the office, the decision-making unit will prioritize tasks performed in the office. If the user is out of the office, the decision-making unit can also prioritize tasks performed while out of the office. If the user is at home, the decision-making unit can also prioritize tasks performed at home. This allows for more appropriate task management by considering the geographical distribution of tasks. Some or all of the above processing in the decision-making unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the decision-making unit inputs geographical distribution data of tasks into a generative AI, and the generative AI makes the decision.

[0086] The decision-making unit can improve the accuracy of its decision-making when determining task priorities by referring to relevant literature and data. For example, the decision-making unit can refer to relevant literature to determine the importance of a task. The decision-making unit can also determine the urgency of a task based on data. The decision-making unit can also analyze relevant literature and data to determine the optimal priority. As a result, the accuracy of priority determination is improved by referring to relevant literature and data. Some or all of the above processes in the decision-making unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the decision-making unit inputs relevant literature and data into a generative AI, and the generative AI improves the accuracy of the decision-making.

[0087] The generation unit can estimate the user's emotions and adjust the task list generation method based on the estimated emotions. For example, if the user is stressed, the generation unit may prioritize including high-priority tasks in the list. If the user is relaxed, the generation unit may also prioritize including long-term tasks in the list. If the user is in a hurry, the generation unit may also prioritize including high-urgency tasks in the list. This allows for the generation of a more appropriate task list by adjusting the task list generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generation AI. For example, the generation unit inputs user emotion data into a generation AI, the generation AI estimates the emotions, and adjusts the task list generation method.

[0088] The generation unit can adjust the level of detail in a task list based on the importance and urgency of the tasks when generating the task list. For example, the generation unit will describe high-importance tasks in detail. The generation unit can also describe high-urgency tasks in detail. The generation unit can also adjust the level of detail in the list considering the balance between importance and urgency. By adjusting the level of detail in the list based on the importance and urgency of the tasks, a more appropriate task list is generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs task importance and urgency data into the generation AI, and the generation AI adjusts the level of detail in the list.

[0089] The generation unit can generate a more personalized list by learning the user's behavior patterns and past task data when generating a task list. For example, the generation unit can generate an optimal task list based on the user's past task data. The generation unit can also learn the user's behavior patterns and generate an efficient task list. The generation unit can also analyze the user's past task data and behavior patterns to generate a personalized list. As a result, a more personalized task list is generated by learning the user's behavior patterns and past task data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the user's behavior pattern data into a generation AI, and the generation AI generates a personalized list.

[0090] The generation unit can estimate the user's emotions and adjust how the task list is displayed based on the estimated emotions. For example, if the user is stressed, the generation unit can provide a simple and highly visible display. If the user is relaxed, the generation unit can also provide a display that includes detailed information. If the user is in a hurry, the generation unit can also provide a display that gets straight to the point. By adjusting how the task list is displayed according to the user's emotions, a more appropriate task list is displayed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit inputs user emotion data into the generation AI, the generation AI estimates the emotions, and adjusts how the task list is displayed.

[0091] The generation unit can determine the priority of tasks in a task list based on their submission dates when generating the list. For example, the generation unit may prioritize tasks with approaching deadlines. It may also postpone tasks with later deadlines. The generation unit may also determine the priority of tasks by considering the balance of submission dates. This results in a more appropriate task list being generated by determining the priority of tasks based on their submission dates. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs task submission date data into a generation AI, and the generation AI determines the priority of the list.

[0092] The generation unit can adjust the order of tasks in a task list based on their relevance. For example, it can group highly relevant tasks together in the list. It can also distribute less relevant tasks among them. The generation unit can adjust the order of tasks based on their relevance. This allows for the generation of a more efficient task list by adjusting the order of tasks based on their relevance. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs task relevance data into a generation AI, and the generation AI adjusts the order of the list.

[0093] The service provider can estimate the user's emotions and adjust how the task list is presented based on the estimated emotions. For example, if the user is stressed, the service provider may present the task list in a simple and visually clear manner. If the user is relaxed, the service provider may also present the task list in a way that includes detailed information. If the user is in a hurry, the service provider may also present the task list in a concise manner. By adjusting how the task list is presented according to the user's emotions, a more appropriate task list is provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider inputs user emotion data into a generative AI, the generative AI estimates the emotions, and adjusts how the task list is presented.

[0094] The service provider can select the optimal delivery method when providing a task list by referring to the user's past task completion status. For example, the service provider can select the optimal delivery method based on data of tasks the user has completed in the past. The service provider can also provide the task list in a way that is easy for the user to complete, based on the user's past task completion status. The service provider can also analyze the user's past task completion status and select the optimal delivery method. This allows the service provider to select the optimal delivery method by referring to the user's past task completion status. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past task completion status data into the AI, and the AI ​​can select the optimal delivery method.

[0095] The service provider can customize the task list provided based on the user's current situation and environment. For example, if the user is in a meeting, the service provider will provide a task list related to the meeting. If the user is traveling, the service provider can also provide a task list related to travel. If the user is at home, the service provider can also provide a task list to be done at home. By customizing the task list based on the user's current situation and environment, a more appropriate task list will be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's current situation and environment data into the AI, and the AI ​​will customize the task list.

[0096] The service provider can estimate the user's emotions and adjust the frequency of task list delivery based on the estimated emotions. For example, if the user is stressed, the service provider can reduce the frequency of delivery to alleviate the burden. If the user is relaxed, the service provider can increase the frequency of delivery to provide more detailed information. If the user is in a hurry, the service provider can quickly provide only the necessary information. By adjusting the frequency of task list delivery according to the user's emotions, a more appropriate task list is provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider inputs user emotion data into a generative AI, the generative AI estimates the emotions, and adjusts the frequency of task list delivery.

[0097] The service provider can select the optimal service delivery method when providing a task list, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can select a delivery method that matches the screen size. If the user is using a tablet, the service provider can also select a delivery method optimized for a larger screen. If the user is using a smartwatch, the service provider can also select a concise and highly visible delivery method. In this way, the service provider can select the optimal service delivery method by taking into account the user's device information. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's device information into the AI, and the AI ​​can select the optimal service delivery method.

[0098] The service provider can analyze the user's social media activity and customize the content provided when delivering task lists. For example, the service provider can provide a customized task list based on tasks mentioned by the user on social media. The service provider can also provide task lists related to events shared by the user on social media. The service provider can also provide task lists related to accounts followed by the user on social media. This allows for the provision of more appropriate task lists by analyzing the user's social media activity. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into AI, which then customizes the content provided.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] Task management systems can acquire user health data and adjust task priorities based on their health status. For example, by analyzing a user's heart rate and sleep data, they can postpone less important tasks if the user is fatigued. Conversely, if the user is in good health, they can prioritize tasks requiring concentration. Furthermore, they can suggest appropriate break times based on the user's health data. This enables task management tailored to the user's health status, allowing for more efficient task execution.

[0101] A task management system can suggest tasks based on the user's hobbies and interests. For example, if a user enjoys reading, it will prioritize suggesting reading-related tasks. Similarly, if a user enjoys sports, it can include exercise-related tasks in the list. Furthermore, it can suggest new hobbies and activities based on the user's interests. This enables task management tailored to the user's hobbies and interests, which is expected to improve motivation.

[0102] A task management system can estimate a user's emotions and adjust the difficulty of tasks based on those emotions. For example, if a user is stressed, it can prioritize assigning easier tasks. Conversely, if a user is relaxed, it can assign more difficult tasks. Furthermore, if a user is in a hurry, it can prioritize suggesting tasks that can be completed quickly. This enables task management that responds to the user's emotions, leading to more efficient task execution.

[0103] A task management system can analyze a user's past task completion history and prioritize suggesting tasks that are easier to complete. For example, it can prioritize including similar tasks in the list based on data from tasks the user has completed in the past. It can also suggest avoiding tasks that the user has struggled with in the past. Furthermore, it can suggest the optimal combination of tasks based on the user's past task completion history. This enables task management based on the user's past performance, allowing for efficient task execution.

[0104] A task management system can estimate a user's emotions and set task rewards based on those emotions. For example, if a user is feeling stressed, it can provide a reward that helps them relax after completing a task. Conversely, if a user is relaxed, it can set a reward that provides a sense of accomplishment. Furthermore, if a user is in a hurry, it can provide immediate rewards for tasks that can be completed quickly. This enables reward settings tailored to the user's emotions, which is expected to improve motivation.

[0105] A task management system can suggest optimal tasks by considering the user's geographical location. For example, if the user is in the office, tasks to be performed in the office will be prioritized. If the user is out of the office, tasks to be performed while away from the office can also be included in the list. Furthermore, if the user is at home, tasks to be performed at home can be prioritized. This enables task management based on the user's geographical location, allowing for efficient task completion.

[0106] Task management systems can estimate a user's emotions and adjust task notifications based on those emotions. For example, if a user is stressed, tasks may be notified with a gentle sound. If the user is relaxed, detailed notifications may be provided. Furthermore, if the user is in a hurry, concise notifications may be provided. This enables notifications tailored to the user's emotions, resulting in more efficient task management.

[0107] Task management systems can analyze users' social media activity and suggest relevant tasks. For example, they can include tasks related to events users have mentioned on social media in their lists. They can also suggest tasks related to accounts users follow. Furthermore, they can suggest new tasks based on content users have shared. This enables task management based on users' social media activity, allowing for efficient task completion.

[0108] Task management systems can estimate a user's emotions and adjust task reminders based on those emotions. For example, if a user is stressed, the frequency of reminders can be reduced to lessen the burden. Conversely, if a user is relaxed, detailed reminders can be provided. Furthermore, if a user is in a hurry, concise reminders can be offered. This enables reminder settings tailored to the user's emotions, resulting in more efficient task management.

[0109] A task management system can provide the optimal task display method by taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that is adapted to the screen size. If the user is using a tablet, it can provide a display method optimized for the larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. This enables task display based on the user's device information, resulting in efficient task management.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception desk accepts task inputs. Users can input tasks using text or voice input, and the reception desk can also save these tasks to a database. Step 2: The decision unit analyzes the tasks received by the reception unit and determines their priority. The decision unit determines priority based on the urgency and importance of the tasks, and can also determine task priority using a generation AI. Step 3: The generation unit generates a task list based on the priority determined by the decision unit. The generation unit can generate a task list based on the priority of the tasks, or it can generate a task list using a generation AI. Step 4: The providing unit provides the user with the task list generated by the generating unit. The providing unit can also display the task list on the user's device and send it to the user via email or notification.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the reception unit, judgment unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing the user to input tasks using text input or voice input. The judgment unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the tasks received by the reception unit and determines their priority. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates a task list based on the priority determined by the judgment unit. The provision unit is implemented by the output device 40 of the smart device 14, which provides the task list generated by the generation unit to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the reception unit, judgment unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the user to input tasks using voice input. The judgment unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the tasks received by the reception unit and determines their priority. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which generates a task list based on the priority determined by the judgment unit. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214, which provides the task list generated by the generation unit to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the reception unit, judgment unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing the user to input tasks using voice input. The judgment unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the tasks received by the reception unit and determines their priority. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates a task list based on the priority determined by the judgment unit. The provision unit is implemented by the display 343 of the headset terminal 314, which provides the user with the task list generated by the generation unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, judgment unit, generation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing the user to input tasks using voice input. The judgment unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the tasks received by the reception unit and determines their priority. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which generates a task list based on the priority determined by the judgment unit. The provision unit is implemented, for example, by the speaker 240 of the robot 414, which provides the task list generated by the generation unit to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A reception desk that accepts task inputs, A judgment unit analyzes the tasks received by the aforementioned reception unit and determines their priority, A generation unit generates a task list based on the priority determined by the aforementioned determination unit, The system comprises a providing unit that provides the task list generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is A task list is generated using a generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The generative AI learns the user's behavior patterns and past task data to provide reminders and advice. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is Accepts voice input The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the generated task list to the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The unit that makes the determination said, The AI ​​generates the task prioritization. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the task input method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past task input history and provides the optimal input interface. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering a task, the input content is filtered based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the input tasks based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering tasks, the system prioritizes tasks that are highly relevant to the user, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering tasks, the system analyzes the user's social media activity and enters relevant tasks. The system described in Appendix 1, characterized by the features described herein. (Note 13) The unit that makes the determination said, It estimates the user's emotions and adjusts task priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The unit that makes the determination said, When prioritizing tasks, adjust the criteria based on the importance and urgency of each task. The system described in Appendix 1, characterized by the features described herein. (Note 15) The unit that makes the determination said, When determining task priorities, referencing the user's past task completion history improves the accuracy of the decision-making process. The system described in Appendix 1, characterized by the features described herein. (Note 16) The unit that makes the determination said, It estimates the user's emotions and adjusts the order in which tasks are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The unit that makes the determination said, When prioritizing tasks, the geographical distribution of those tasks should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The unit that makes the determination said, When prioritizing tasks, refer to relevant literature and data to improve the accuracy of the decision. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts how the task list is generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating a task list, adjust the level of detail in the list based on the importance and urgency of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating task lists, the system learns the user's behavior patterns and past task data to create more personalized lists. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts how the task list is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating a task list, prioritize the list based on the task submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating a task list, adjust the order of the list based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the task list is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing task lists, the system selects the optimal delivery method by referring to the user's past task completion status. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing task lists, customize the content based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the frequency of task list presentations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing task lists, the optimal delivery method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing task lists, the system analyzes the user's social media activity to customize the content provided. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 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 reception desk that accepts task inputs, A judgment unit analyzes the tasks received by the aforementioned reception unit and determines their priority, A generation unit generates a task list based on the priority determined by the aforementioned determination unit, The system comprises a providing unit that provides the task list generated by the generation unit. A system characterized by the following features.

2. The generating unit is A task list is generated using a generation AI. The system according to feature 1.

3. The generating unit is Generative AI learns user behavior patterns and past task data to provide reminders and advice. The system according to feature 1.

4. The aforementioned reception unit is Accepts voice input The system according to feature 1.

5. The aforementioned supply unit is, Provide the generated task list to the user. The system according to feature 1.

6. The unit that makes the determination said, Task prioritization is determined by generating AI. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the task input method based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past task input history and provides the optimal input interface. The system according to feature 1.

9. The aforementioned reception unit is When entering a task, the input content is filtered based on the user's current situation and environment. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the input tasks based on the estimated user emotions. The system according to feature 1.

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