system

Abstract

The system according to this embodiment aims to identify the most important tasks based on the user's goals and schedule, and to provide mental support. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a specification unit, and a dialogue unit. The reception unit receives input from the user regarding their goals and schedule. The analysis unit analyzes the goals and schedule entered by the reception unit. The specification unit identifies and prioritizes the most important tasks based on the goals and schedule analyzed by the analysis unit. The dialogue unit provides emotional support to the user based on the tasks identified by the specification unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that effective support according to individual needs was insufficient in achieving user goals and task management.

[0005] The system according to the embodiment aims to identify the most important tasks based on the user's goals and schedules and provide mental support.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a specification unit, and a dialogue unit. The reception unit receives the user's goals and schedule. The analysis unit analyzes the goals and schedule entered by the reception unit. The specification unit identifies and prioritizes the most important tasks based on the goals and schedule analyzed by the analysis unit. The dialogue unit provides emotional support to the user based on the tasks identified by the specification unit. [Effects of the Invention]

[0007] The system according to this embodiment can identify the most important tasks based on the user's goals and schedule, and provide mental support. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The morning routine support system according to an embodiment of the present invention is a system in which the user inputs their goals and schedule, and an AI analyzes this to identify and prioritize the most important tasks. This system maximizes the user's morning time and provides strong support for the start of the day, offering a path to achieving long-term goals. For example, if the user inputs "Today's goal is to prepare a presentation," the AI ​​will suggest related tasks based on that goal, prioritizing them. The system also provides mental support to the user. Through interaction with the AI, it promotes positive thinking and motivation, and provides advice to reduce stress and anxiety. For example, if the user inputs "I'm feeling down today," the AI ​​will suggest encouraging messages and ways to relax. Furthermore, the system continuously analyzes the user's behavior patterns and achievement status, and provides feedback and improvement suggestions based on this analysis. For example, if the user inputs "I jog every morning" and records their achievement status, the AI ​​will suggest the next goal based on that data. This allows the user to objectively understand their own progress and gradually form more effective morning routines. As a result, the morning routine support system can improve the user's productivity and well-being.

[0029] The morning activity support system according to this embodiment comprises a reception unit, an analysis unit, a selection unit, and a dialogue unit. The reception unit receives input from the user regarding their goals and schedules. The user's goals and schedules include, but are not limited to, short-term goals, long-term goals, daily schedules, and special schedules. The reception unit allows the user to input goals and schedules using, for example, voice input or text input. The reception unit can also refer to the user's past goal and schedule input history and suggest the most suitable input method. For example, the reception unit may prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The analysis unit analyzes the goals and schedules entered by the reception unit. The analysis is performed using, for example, statistical analysis, pattern recognition, machine learning algorithms, etc., but is not limited to these methods. For example, the analysis unit continuously analyzes the user's behavior patterns and achievement status and provides feedback and improvement suggestions based on this analysis. The selection unit identifies and prioritizes the most important tasks based on the goals and schedules analyzed by the analysis unit. The importance of tasks is evaluated using, for example, criteria such as urgency, impact, and feasibility, but is not limited to these methods. For example, the identification unit prioritizes suggesting relevant tasks based on the user's goals and schedule. The dialogue unit provides emotional support to the user based on the tasks identified by the identification unit. The dialogue unit provides, for example, positive messages and relaxation methods. For instance, if the user inputs "I'm feeling down today," the dialogue unit suggests encouraging messages and relaxation methods. In this way, the morning activity support system according to the embodiment can improve productivity and well-being by effectively managing the user's goals and schedule and providing emotional support.

[0030] The reception desk inputs the user's goals and schedules. These include, but are not limited to, short-term goals, long-term goals, daily schedules, and special appointments. The reception desk allows users to input goals and schedules using methods such as voice input or text input. Specifically, with voice input, users can easily input goals and schedules by speaking into their smartphone or a dedicated device. Speech recognition technology is used to convert the user's speech into text and register it in the system. With text input, users can directly input goals and schedules using a keyboard or touchscreen. Furthermore, the reception desk can suggest the optimal input method by referring to the user's past goal and schedule input history. For example, it can improve user convenience by prioritizing input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also automatically categorize user input into categories such as short-term goals, long-term goals, daily schedules, and special appointments. This makes it easier for users to organize their goals and schedules, improving the overall efficiency of the system. Additionally, the reception desk can provide relevant information and resources based on the user's input. For example, if a user types "Prepare for next week's meeting," the reception desk will suggest materials and tools that will help them prepare for the meeting. This allows users to receive support in achieving their goals and schedules.

[0031] The analytics department analyzes the goals and schedules entered by the reception department. Analysis is performed using methods such as statistical analysis, pattern recognition, and machine learning algorithms, but is not limited to these examples. Specifically, statistical analysis is used to calculate the user's goal achievement rate and schedule execution rate to understand user behavior patterns. Pattern recognition technology is used to extract common patterns from the user's past behavior data and predict future behavior. Machine learning algorithms are used to learn from user behavior data and generate optimal feedback and improvement suggestions for individual users. For example, the analytics department can identify a user's strengths and weaknesses based on data of goals achieved and schedules completed in the past, and provide specific improvement suggestions based on that. Furthermore, the analytics department can continuously monitor user behavior data and provide real-time feedback. For example, if a user is progressing well toward achieving their goals, the analytics department provides positive feedback to maintain the user's motivation. On the other hand, if a user is struggling to achieve their goals, the analytics department proposes specific improvement measures to support the user in achieving their goals. This allows the analytics department to effectively analyze user behavior and provide optimal feedback and improvement suggestions for individual users.

[0032] The task selection department identifies and prioritizes the most important tasks based on the goals and schedules analyzed by the analysis department. Task importance is evaluated using criteria such as urgency, impact, and feasibility, but is not limited to these examples. Specifically, urgent tasks are given priority, and tasks with a high impact are given a higher importance rating. Tasks that are feasible are given a higher priority because the user can complete them in a short period of time. The task selection department comprehensively evaluates these criteria to identify the most important tasks for the user. For example, the task selection department prioritizes and suggests relevant tasks based on the user's goals and schedules. If a user sets the goal of "completing the project this week," the task selection department will prioritize and suggest tasks related to the project, supporting the user in efficiently achieving their goal. Furthermore, the task selection department can dynamically adjust task priorities based on the user's past behavioral data. For example, if a user performs well on a particular task, prioritizing that task can leverage the user's strengths. The task selection department can also collect user feedback and continuously improve task priorities. This allows the specific unit to identify the most important tasks for the user and support them in efficiently achieving those goals.

[0033] The dialogue unit provides emotional support to the user based on tasks identified by the specific unit. For example, the dialogue unit offers positive messages and relaxation methods. Specifically, if a user inputs "I'm feeling down today," the dialogue unit will suggest encouraging messages and relaxation methods. For example, it might send a message such as, "You're doing a great job. Take a short break and refresh yourself." The dialogue unit can also analyze the user's emotional state and provide appropriate support. For example, if a user is feeling stressed, the dialogue unit will suggest relaxation methods and stress-relieving activities. Furthermore, the dialogue unit can collect user feedback and continuously improve its support. For example, if a user shows a positive response to a particular message or activity, the dialogue unit will use that information to adjust future support. The dialogue unit can also provide optimal support to individual users based on their preferences and past behavioral data. As a result, the dialogue unit can effectively provide emotional support to users, improving their productivity and well-being. Moreover, through dialogue with users, the dialogue unit can not only support users in achieving their goals but also improve their overall well-being.

[0034] The analysis unit can continuously analyze users' behavior patterns and achievement status. For example, the analysis unit collects data on users' daily activities, work patterns, and hobbies, and analyzes it using statistical analysis and pattern recognition technologies. For example, the analysis unit can analyze users' behavior patterns and evaluate how specific actions affect goal achievement. The analysis unit can also evaluate users' achievement status and understand goal achievement rates and progress. For example, the analysis unit can evaluate the extent to which users have achieved their set goals and suggest the next goals based on their achievement level. This allows for more appropriate feedback and improvement suggestions by continuously analyzing users' behavior patterns and achievement status. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input user behavior data into a generating AI and have the generating AI perform behavior pattern analysis.

[0035] The dialogue unit can provide encouraging messages and relaxation methods. For example, if a user inputs "I'm feeling down today," the dialogue unit will provide encouraging messages such as positive words or shared success stories. The dialogue unit can also suggest relaxation methods such as breathing exercises, meditation, and stretching if the user wants to relax. For example, the dialogue unit might send a message to the user saying, "Take a deep breath and relax." Furthermore, the dialogue unit can analyze the user's psychological state and provide advice to reduce stress and anxiety. For example, if the dialogue unit is feeling stressed, it might suggest relaxation techniques or positive thinking methods. This allows the dialogue unit to provide mental support to the user by offering positive messages and relaxation methods. Some or all of the above processing in the dialogue unit may be performed using AI, or not. For example, the dialogue unit can input user data into a generating AI, which can then generate encouraging messages and relaxation suggestions.

[0036] The analytics department can suggest the next goal based on the user's goals and plans. For example, the analytics department can collect user-set goals and plans as data and suggest the next goal based on that data. For example, if the user enters "Jogging every morning" and records its progress, the analytics department will suggest "Jogging three times a week" as the next goal. The analytics department can also suggest the next goal considering the user's interests and past goal achievements. For example, if the user enters "Reading books" and records its progress, the analytics department will suggest "Reading one book per month" as the next goal. In this way, by suggesting the next goal based on the user's goals and plans, the analytics department can support the user in achieving their goals. Some or all of the above processing in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input user goal data into a generation AI and have the generation AI suggest the next goal.

[0037] The specific unit can prioritize suggesting relevant tasks based on the user's goals and schedule. For example, if the user inputs "Today's goal is to prepare a presentation," the specific unit will prioritize suggesting relevant tasks based on that goal. For example, the specific unit might suggest tasks such as "Create presentation materials" or "Rehearse the presentation." The specific unit can also prioritize suggesting relevant tasks based on the user's goals and schedule. For example, if the user inputs "Prepare for tomorrow's meeting," the specific unit will prioritize suggesting relevant tasks based on that goal. This allows the unit to support the user in achieving their goals by prioritizing the suggestion of relevant tasks based on the user's goals and schedule. Some or all of the above processing in the specific unit may be performed using AI, for example, or not using AI. For example, the specific unit can input the user's goal data into a generating AI and have the generating AI suggest relevant tasks.

[0038] The reception desk can analyze the user's past goal and schedule input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also analyze patterns in the goals and schedules the user has entered in the past and suggest the optimal input method. For example, the reception desk can suggest the optimal input method for a specific time period based on the user's past input history. In this way, by analyzing the user's past input history, the reception desk can suggest the optimal input method and improve input efficiency. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's input history data into a generating AI and have the generating AI select the optimal input method.

[0039] The reception unit can filter the user's current lifestyle and areas of interest when they input goals and plans. For example, the reception unit can suggest relevant goals and plans based on the user's current lifestyle (work, family, etc.). It can also suggest relevant goals and plans based on the user's areas of interest (hobbies, health, etc.). For example, the reception unit can combine the user's current lifestyle and areas of interest to suggest the most suitable goals and plans. This allows for the suggestion of more appropriate goals and plans by filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0040] The reception desk can prioritize the input of highly relevant goals and plans by considering the user's geographical location when users input goals and plans. For example, the reception desk can prioritize suggesting goals and plans that can be done near the user's current location. Furthermore, if the user is in a specific region, the reception desk can suggest goals and plans related to that region. For example, if the user is on the move, the reception desk will suggest goals and plans related to their destination. This allows for the suggestion of more relevant goals and plans by considering 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 can input the user's geographical location information into a generating AI and have the AI ​​suggest highly relevant goals and plans.

[0041] The reception desk can analyze the user's social media activity when they input goals and plans, and input relevant goals and plans. For example, the reception desk can suggest goals and plans based on events and activities the user has shared on social media. The reception desk can also suggest relevant goals and plans based on the user's interests on social media. For example, the reception desk can suggest goals and plans based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to suggest more relevant goals and plans. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI generate suggestions for relevant goals and plans.

[0042] The analysis unit can adjust the level of detail of its analysis based on the importance of the goals and plans. For example, the analysis unit will perform a detailed analysis for important goals and plans, and a concise analysis for less important goals and plans. For instance, the analysis unit adjusts the depth of its analysis according to the importance of the goals and plans. By adjusting the level of detail of the analysis based on the importance of the goals and plans, it is possible to provide more appropriate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input goal and plan data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of goals and plans during analysis. For example, the analysis unit can apply an analysis algorithm that prioritizes efficiency to work-related goals and plans. It can also apply an analysis algorithm that takes health status into account to health-related goals and plans. For example, the analysis unit can apply an analysis algorithm that prioritizes enjoyment to hobby and leisure-related goals and plans. By applying different analysis algorithms depending on the category of goals and plans, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input goal and plan data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0044] The analysis department can prioritize analyses based on the submission deadlines for goals and plans. For example, the analysis department might prioritize analyzing goals and plans with approaching deadlines. It can also postpone analyzing goals and plans with later submission deadlines. For instance, the analysis department adjusts the analysis priority according to the submission deadline. This allows for more appropriate analysis results by prioritizing analyses based on the submission deadlines for goals and plans. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department could input goal and plan data into a generating AI and have the generating AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of goals and plans during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant goals and plans. It can also postpone the analysis of less relevant goals and plans. For example, the analysis unit adjusts the order of analysis according to the relevance of goals and plans. By adjusting the order of analysis based on the relevance of goals and plans, it is possible to provide more appropriate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input goal and plan data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The identification unit can adjust the level of detail of the task based on its importance during the identification process. For example, the identification unit will perform a detailed identification for important tasks, and a concise identification for less important tasks. For instance, the identification unit adjusts the depth of the identification according to the importance of the task. This allows for the identification of more appropriate tasks by adjusting the level of detail based on the importance of the task. Some or all of the above-described processes in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input task data into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0047] The identification unit can apply different identification algorithms depending on the task category at the time of identification. For example, the identification unit can apply an algorithm that prioritizes efficiency to work-related tasks. It can also apply an algorithm that takes health status into account to health-related tasks. For example, the identification unit can apply an algorithm that prioritizes enjoyment to hobby and leisure-related tasks. In this way, by applying different identification algorithms depending on the task category, more appropriate tasks can be identified. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input task data into a generating AI and have the generating AI execute the application of different identification algorithms.

[0048] The identification unit can determine specific priorities based on the submission timing of tasks at the time of identification. For example, the identification unit can prioritize tasks with approaching deadlines. It can also postpone tasks with later submission dates. For example, the identification unit can adjust specific priorities according to the submission timing. This allows for the identification of more appropriate tasks by determining specific priorities based on the submission timing of tasks. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input task data into a generating AI and have the generating AI perform the determination of specific priorities.

[0049] The identification unit can adjust the order of tasks based on their relevance at the time of identification. For example, the identification unit can prioritize identifying tasks that are highly relevant. It can also postpone tasks that are less relevant. For example, the identification unit adjusts the order of tasks according to their relevance. This allows for the identification of more appropriate tasks by adjusting the order of tasks based on their relevance. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input task data into a generating AI and have the generating AI perform the adjustment of the order of tasks.

[0050] The dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history during a conversation. For example, the dialogue unit may prioritize using a dialogue style that the user has preferred in the past. The dialogue unit can also suggest the optimal dialogue method based on the user's past dialogue history. For example, the dialogue unit may analyze the user's past dialogue history and select the most effective dialogue method. This allows for the selection of a more appropriate dialogue method by referring to the user's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's dialogue history data into a generating AI and have the generating AI select the optimal dialogue method.

[0051] The dialogue unit can select the optimal dialogue method during a conversation, taking into account the user's geographical location. For example, if the user is at home, the dialogue unit can provide a relaxed dialogue method. It can also provide an efficient dialogue method if the user is at work. For example, if the user is out, the dialogue unit can provide a concise and easy-to-understand dialogue method. This allows for the selection of a more appropriate dialogue method by considering the user's geographical location. Some or all of the above processing in the dialogue unit may be performed using AI, or without AI. For example, the dialogue unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal dialogue method.

[0052] The dialogue unit can analyze the user's social media activity during a conversation and suggest conversation content. For example, the dialogue unit can suggest conversation content based on what the user has shared on social media. It can also suggest conversation content based on the user's interests on social media. For example, the dialogue unit can suggest conversation content based on the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to suggest more appropriate conversation content. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's social media activity data into a generating AI and have the generating AI suggest conversation content.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The reception desk can suggest the most suitable input method when a user enters their goals and schedules, taking into account the user's past behavioral patterns. For example, if a user has frequently used voice input in the past, the reception desk will prioritize suggesting voice input. It can also send notifications prompting users to input during specific time slots if they tend to do so at those times. Furthermore, the reception desk can analyze the user's past input history and offer suggestions to improve input efficiency. This allows for more effective goal and schedule entry by considering the user's past behavioral patterns.

[0055] The analytics department can consider users' health data when analyzing their behavior patterns and achievements. For example, it can collect users' sleep and exercise data and provide advice based on that data to help them achieve their goals. It can also suggest realistic goal setting based on the user's health condition. Furthermore, the analytics department can offer suggestions for stress management and relaxation based on the user's health data. By considering the user's health condition, it can provide more appropriate feedback and improvement suggestions.

[0056] The analytics department can consider users' social media activity when analyzing their goals and plans. For example, it can suggest goals and plans based on events and activities that users have shared on social media. It can also suggest relevant goals and plans based on users' interests on social media. Furthermore, it can suggest goals and plans based on the activities of users' friends on social media. By considering users' social media activity, it is possible to suggest more relevant goals and plans.

[0057] The system can consider the user's geographical location when suggesting relevant tasks based on the user's goals and schedule. For example, it can prioritize tasks that can be performed near the user's current location. It can also suggest tasks related to a specific region if the user is in that region. Furthermore, if the user is on the move, it can suggest tasks related to their destination. This allows for the suggestion of more relevant tasks by considering the user's geographical location.

[0058] The analytics department can consider a user's past goal achievement record when analyzing their goals and plans. For example, it can suggest the next goal based on the goals and plans the user has achieved in the past. It can also suggest achievable goals by considering goals and plans the user has not achieved in the past. Furthermore, it can analyze the user's past goal achievement record and suggest the next goal based on the degree of achievement. This allows for more appropriate goal setting by considering the user's past goal achievement record.

[0059] The specific unit can consider the user's current life circumstances and areas of interest when prioritizing and suggesting relevant tasks based on the user's goals and schedule. For example, it can suggest relevant tasks based on the user's current life circumstances (work, family, etc.). It can also suggest relevant tasks based on the user's areas of interest (hobbies, health, etc.). Furthermore, it can combine the user's current life circumstances and areas of interest to suggest the most appropriate tasks. This allows for the suggestion of more appropriate tasks by considering the user's life circumstances and areas of interest.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk inputs the user's goals and schedule. Users can input their goals and schedules using voice input or text input. The reception desk can also refer to the user's past goal and schedule input history to suggest the most suitable input method. Step 2: The analysis department analyzes the goals and schedules entered by the reception department. The analysis is performed using methods such as statistical analysis, pattern recognition, and machine learning algorithms. The analysis department continuously analyzes user behavior patterns and achievement status, and provides feedback and improvement suggestions based on this analysis. Step 3: The Identification Department identifies and prioritizes the most important tasks based on the goals and schedules analyzed by the Analysis Department. Task importance is evaluated based on criteria such as urgency, impact, and feasibility. The Identification Department then proposes relevant tasks based on the user's goals and schedules. Step 4: The dialogue unit provides emotional support to the user based on the task identified by the specific unit. The dialogue unit offers positive messages and relaxation methods. For example, if the user inputs "I'm feeling down today," it will suggest encouraging messages and relaxation methods.

[0062] (Example of form 2) The morning routine support system according to an embodiment of the present invention is a system in which the user inputs their goals and schedule, and an AI analyzes this to identify and prioritize the most important tasks. This system maximizes the user's morning time and provides strong support for the start of the day, offering a path to achieving long-term goals. For example, if the user inputs "Today's goal is to prepare a presentation," the AI ​​will suggest related tasks based on that goal, prioritizing them. The system also provides mental support to the user. Through interaction with the AI, it promotes positive thinking and motivation, and provides advice to reduce stress and anxiety. For example, if the user inputs "I'm feeling down today," the AI ​​will suggest encouraging messages and ways to relax. Furthermore, the system continuously analyzes the user's behavior patterns and achievement status, and provides feedback and improvement suggestions based on this analysis. For example, if the user inputs "I jog every morning" and records their achievement status, the AI ​​will suggest the next goal based on that data. This allows the user to objectively understand their own progress and gradually form more effective morning routines. As a result, the morning routine support system can improve the user's productivity and well-being.

[0063] The morning activity support system according to this embodiment comprises a reception unit, an analysis unit, a selection unit, and a dialogue unit. The reception unit receives input from the user regarding their goals and schedules. The user's goals and schedules include, but are not limited to, short-term goals, long-term goals, daily schedules, and special schedules. The reception unit allows the user to input goals and schedules using, for example, voice input or text input. The reception unit can also refer to the user's past goal and schedule input history and suggest the most suitable input method. For example, the reception unit may prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The analysis unit analyzes the goals and schedules entered by the reception unit. The analysis is performed using, for example, statistical analysis, pattern recognition, machine learning algorithms, etc., but is not limited to these methods. For example, the analysis unit continuously analyzes the user's behavior patterns and achievement status and provides feedback and improvement suggestions based on this analysis. The selection unit identifies and prioritizes the most important tasks based on the goals and schedules analyzed by the analysis unit. The importance of tasks is evaluated using, for example, criteria such as urgency, impact, and feasibility, but is not limited to these methods. For example, the identification unit prioritizes suggesting relevant tasks based on the user's goals and schedule. The dialogue unit provides emotional support to the user based on the tasks identified by the identification unit. The dialogue unit provides, for example, positive messages and relaxation methods. For instance, if the user inputs "I'm feeling down today," the dialogue unit suggests encouraging messages and relaxation methods. In this way, the morning activity support system according to the embodiment can improve productivity and well-being by effectively managing the user's goals and schedule and providing emotional support.

[0064] The reception desk inputs the user's goals and schedules. These include, but are not limited to, short-term goals, long-term goals, daily schedules, and special appointments. The reception desk allows users to input goals and schedules using methods such as voice input or text input. Specifically, with voice input, users can easily input goals and schedules by speaking into their smartphone or a dedicated device. Speech recognition technology is used to convert the user's speech into text and register it in the system. With text input, users can directly input goals and schedules using a keyboard or touchscreen. Furthermore, the reception desk can suggest the optimal input method by referring to the user's past goal and schedule input history. For example, it can improve user convenience by prioritizing input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also automatically categorize user input into categories such as short-term goals, long-term goals, daily schedules, and special appointments. This makes it easier for users to organize their goals and schedules, improving the overall efficiency of the system. Additionally, the reception desk can provide relevant information and resources based on the user's input. For example, if a user types "Prepare for next week's meeting," the reception desk will suggest materials and tools that will help them prepare for the meeting. This allows users to receive support in achieving their goals and schedules.

[0065] The analytics department analyzes the goals and schedules entered by the reception department. Analysis is performed using methods such as statistical analysis, pattern recognition, and machine learning algorithms, but is not limited to these examples. Specifically, statistical analysis is used to calculate the user's goal achievement rate and schedule execution rate to understand user behavior patterns. Pattern recognition technology is used to extract common patterns from the user's past behavior data and predict future behavior. Machine learning algorithms are used to learn from user behavior data and generate optimal feedback and improvement suggestions for individual users. For example, the analytics department can identify a user's strengths and weaknesses based on data of goals achieved and schedules completed in the past, and provide specific improvement suggestions based on that. Furthermore, the analytics department can continuously monitor user behavior data and provide real-time feedback. For example, if a user is progressing well toward achieving their goals, the analytics department provides positive feedback to maintain the user's motivation. On the other hand, if a user is struggling to achieve their goals, the analytics department proposes specific improvement measures to support the user in achieving their goals. This allows the analytics department to effectively analyze user behavior and provide optimal feedback and improvement suggestions for individual users.

[0066] The task selection department identifies and prioritizes the most important tasks based on the goals and schedules analyzed by the analysis department. Task importance is evaluated using criteria such as urgency, impact, and feasibility, but is not limited to these examples. Specifically, urgent tasks are given priority, and tasks with a high impact are given a higher importance rating. Tasks that are feasible are given a higher priority because the user can complete them in a short period of time. The task selection department comprehensively evaluates these criteria to identify the most important tasks for the user. For example, the task selection department prioritizes and suggests relevant tasks based on the user's goals and schedules. If a user sets the goal of "completing the project this week," the task selection department will prioritize and suggest tasks related to the project, supporting the user in efficiently achieving their goal. Furthermore, the task selection department can dynamically adjust task priorities based on the user's past behavioral data. For example, if a user performs well on a particular task, prioritizing that task can leverage the user's strengths. The task selection department can also collect user feedback and continuously improve task priorities. This allows the specific unit to identify the most important tasks for the user and support them in efficiently achieving those goals.

[0067] The dialogue unit provides emotional support to the user based on tasks identified by the specific unit. For example, the dialogue unit offers positive messages and relaxation methods. Specifically, if a user inputs "I'm feeling down today," the dialogue unit will suggest encouraging messages and relaxation methods. For example, it might send a message such as, "You're doing a great job. Take a short break and refresh yourself." The dialogue unit can also analyze the user's emotional state and provide appropriate support. For example, if a user is feeling stressed, the dialogue unit will suggest relaxation methods and stress-relieving activities. Furthermore, the dialogue unit can collect user feedback and continuously improve its support. For example, if a user shows a positive response to a particular message or activity, the dialogue unit will use that information to adjust future support. The dialogue unit can also provide optimal support to individual users based on their preferences and past behavioral data. As a result, the dialogue unit can effectively provide emotional support to users, improving their productivity and well-being. Moreover, through dialogue with users, the dialogue unit can not only support users in achieving their goals but also improve their overall well-being.

[0068] The analysis unit can continuously analyze users' behavior patterns and achievement status. For example, the analysis unit collects data on users' daily activities, work patterns, and hobbies, and analyzes it using statistical analysis and pattern recognition technologies. For example, the analysis unit can analyze users' behavior patterns and evaluate how specific actions affect goal achievement. The analysis unit can also evaluate users' achievement status and understand goal achievement rates and progress. For example, the analysis unit can evaluate the extent to which users have achieved their set goals and suggest the next goals based on their achievement level. This allows for more appropriate feedback and improvement suggestions by continuously analyzing users' behavior patterns and achievement status. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input user behavior data into a generating AI and have the generating AI perform behavior pattern analysis.

[0069] The dialogue unit can provide encouraging messages and relaxation methods. For example, if a user inputs "I'm feeling down today," the dialogue unit will provide encouraging messages such as positive words or shared success stories. The dialogue unit can also suggest relaxation methods such as breathing exercises, meditation, and stretching if the user wants to relax. For example, the dialogue unit might send a message to the user saying, "Take a deep breath and relax." Furthermore, the dialogue unit can analyze the user's psychological state and provide advice to reduce stress and anxiety. For example, if the dialogue unit is feeling stressed, it might suggest relaxation techniques or positive thinking methods. This allows the dialogue unit to provide mental support to the user by offering positive messages and relaxation methods. Some or all of the above processing in the dialogue unit may be performed using AI, or not. For example, the dialogue unit can input user data into a generating AI, which can then generate encouraging messages and relaxation suggestions.

[0070] The analytics department can suggest the next goal based on the user's goals and plans. For example, the analytics department can collect user-set goals and plans as data and suggest the next goal based on that data. For example, if the user enters "Jogging every morning" and records its progress, the analytics department will suggest "Jogging three times a week" as the next goal. The analytics department can also suggest the next goal considering the user's interests and past goal achievements. For example, if the user enters "Reading books" and records its progress, the analytics department will suggest "Reading one book per month" as the next goal. In this way, by suggesting the next goal based on the user's goals and plans, the analytics department can support the user in achieving their goals. Some or all of the above processing in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input user goal data into a generation AI and have the generation AI suggest the next goal.

[0071] The dialogue unit can analyze the user's psychological state and provide specific advice to reduce stress and anxiety. For example, if the user inputs "I'm feeling stressed," the dialogue unit will suggest relaxation techniques and positive thinking methods. The dialogue unit can also analyze the user's psychological state and provide advice to reduce stress and anxiety. For example, if the user inputs "I'm feeling anxious," the dialogue unit will suggest relaxation techniques and positive thinking methods. In this way, by analyzing the user's psychological state and providing advice to reduce stress and anxiety, it can support the user's mental well-being. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input user input data into a generative AI and have the generative AI suggest advice to reduce stress and anxiety.

[0072] The specific unit can prioritize suggesting relevant tasks based on the user's goals and schedule. For example, if the user inputs "Today's goal is to prepare a presentation," the specific unit will prioritize suggesting relevant tasks based on that goal. For example, the specific unit might suggest tasks such as "Create presentation materials" or "Rehearse the presentation." The specific unit can also prioritize suggesting relevant tasks based on the user's goals and schedule. For example, if the user inputs "Prepare for tomorrow's meeting," the specific unit will prioritize suggesting relevant tasks based on that goal. This allows the unit to support the user in achieving their goals by prioritizing the suggestion of relevant tasks based on the user's goals and schedule. Some or all of the above processing in the specific unit may be performed using AI, for example, or not using AI. For example, the specific unit can input the user's goal data into a generating AI and have the generating AI suggest relevant tasks.

[0073] The reception desk can estimate the user's emotions and optimize the timing of goal and schedule input based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can prompt them to input goals and schedules during a time when they can relax. The reception desk can also prompt the user to input goals and schedules when they are concentrating. For example, if the user is tired, the reception desk can prompt them to input goals and schedules after resting. This allows for more effective goal setting by adjusting the timing of goal and schedule input based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 can input user emotion data into a generative AI and have the generative AI optimize the timing of goal and schedule input.

[0074] The reception desk can analyze the user's past goal and schedule input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also analyze patterns in the goals and schedules the user has entered in the past and suggest the optimal input method. For example, the reception desk can suggest the optimal input method for a specific time period based on the user's past input history. In this way, by analyzing the user's past input history, the reception desk can suggest the optimal input method and improve input efficiency. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's input history data into a generating AI and have the generating AI select the optimal input method.

[0075] The reception unit can filter the user's current lifestyle and areas of interest when they input goals and plans. For example, the reception unit can suggest relevant goals and plans based on the user's current lifestyle (work, family, etc.). It can also suggest relevant goals and plans based on the user's areas of interest (hobbies, health, etc.). For example, the reception unit can combine the user's current lifestyle and areas of interest to suggest the most suitable goals and plans. This allows for the suggestion of more appropriate goals and plans by filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0076] The reception desk can estimate the user's emotions and determine the priority of the goals and appointments to be entered based on the estimated emotions. For example, if the user is feeling stressed, the reception desk will prioritize suggesting goals and appointments that promote relaxation. Conversely, if the user is focused, the reception desk can prioritize suggesting important goals and appointments. For example, if the user is tired, the reception desk will suggest goals and appointments that prioritize rest. This allows for more effective goal setting by prioritizing goals and appointments based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 can input user emotion data into a generative AI and have the generative AI determine the priority of goals and appointments.

[0077] The reception desk can prioritize the input of highly relevant goals and plans by considering the user's geographical location when users input goals and plans. For example, the reception desk can prioritize suggesting goals and plans that can be done near the user's current location. Furthermore, if the user is in a specific region, the reception desk can suggest goals and plans related to that region. For example, if the user is on the move, the reception desk will suggest goals and plans related to their destination. This allows for the suggestion of more relevant goals and plans by considering 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 can input the user's geographical location information into a generating AI and have the AI ​​suggest highly relevant goals and plans.

[0078] The reception desk can analyze the user's social media activity when they input goals and plans, and input relevant goals and plans. For example, the reception desk can suggest goals and plans based on events and activities the user has shared on social media. The reception desk can also suggest relevant goals and plans based on the user's interests on social media. For example, the reception desk can suggest goals and plans based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to suggest more relevant goals and plans. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI generate suggestions for relevant goals and plans.

[0079] The analysis unit can estimate the user's emotions and optimize the analysis method for goals and schedules based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide concise and easy-to-understand analysis results. It can also provide detailed analysis results if the user is focused. For example, if the user is tired, the analysis unit can provide analysis results that highlight only the important points. This allows for more appropriate analysis results by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI optimize the analysis method.

[0080] The analysis unit can adjust the level of detail of its analysis based on the importance of the goals and plans. For example, the analysis unit will perform a detailed analysis for important goals and plans, and a concise analysis for less important goals and plans. For instance, the analysis unit adjusts the depth of its analysis according to the importance of the goals and plans. By adjusting the level of detail of the analysis based on the importance of the goals and plans, it is possible to provide more appropriate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input goal and plan data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the category of goals and plans during analysis. For example, the analysis unit can apply an analysis algorithm that prioritizes efficiency to work-related goals and plans. It can also apply an analysis algorithm that takes health status into account to health-related goals and plans. For example, the analysis unit can apply an analysis algorithm that prioritizes enjoyment to hobby and leisure-related goals and plans. By applying different analysis algorithms depending on the category of goals and plans, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input goal and plan data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0082] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit will prioritize analyzing goals and plans that help reduce stress. Similarly, if the user is focused, the analysis unit can prioritize analyzing important goals and plans. For example, if the user is tired, the analysis unit will prioritize analyzing goals and plans that prioritize rest. This allows for more appropriate analysis results by prioritizing the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of the analysis.

[0083] The analysis department can prioritize analyses based on the submission deadlines for goals and plans. For example, the analysis department might prioritize analyzing goals and plans with approaching deadlines. It can also postpone analyzing goals and plans with later submission deadlines. For instance, the analysis department adjusts the analysis priority according to the submission deadline. This allows for more appropriate analysis results by prioritizing analyses based on the submission deadlines for goals and plans. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department could input goal and plan data into a generating AI and have the generating AI determine the analysis priority.

[0084] The analysis unit can adjust the order of analysis based on the relevance of goals and plans during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant goals and plans. It can also postpone the analysis of less relevant goals and plans. For example, the analysis unit adjusts the order of analysis according to the relevance of goals and plans. By adjusting the order of analysis based on the relevance of goals and plans, it is possible to provide more appropriate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input goal and plan data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0085] The identification unit can estimate the user's emotions and optimize the task identification method based on the estimated user emotions. For example, if the user is feeling stressed, the identification unit will prioritize identifying tasks that help reduce stress. The identification unit can also prioritize identifying important tasks if the user is focused. For example, if the user is tired, the identification unit will prioritize identifying tasks that require rest. This allows for the identification of more appropriate tasks by adjusting the task identification method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 identification unit may be performed using AI, or not. For example, the identification unit can input user emotion data into a generative AI and have the generative AI optimize the task identification method.

[0086] The identification unit can adjust the level of detail of the task based on its importance during the identification process. For example, the identification unit will perform a detailed identification for important tasks, and a concise identification for less important tasks. For instance, the identification unit adjusts the depth of the identification according to the importance of the task. This allows for the identification of more appropriate tasks by adjusting the level of detail based on the importance of the task. Some or all of the above-described processes in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input task data into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0087] The identification unit can apply different identification algorithms depending on the task category at the time of identification. For example, the identification unit can apply an algorithm that prioritizes efficiency to work-related tasks. It can also apply an algorithm that takes health status into account to health-related tasks. For example, the identification unit can apply an algorithm that prioritizes enjoyment to hobby and leisure-related tasks. In this way, by applying different identification algorithms depending on the task category, more appropriate tasks can be identified. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input task data into a generating AI and have the generating AI execute the application of different identification algorithms.

[0088] The identification unit can estimate the user's emotions and determine the priority of tasks to be identified based on the estimated emotions. For example, if the user is feeling stressed, the identification unit will prioritize tasks that help reduce stress. The identification unit can also prioritize important tasks if the user is focused. For example, if the user is tired, the identification unit will prioritize tasks that require rest. This allows for the identification of more appropriate tasks by prioritizing tasks based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the identification unit may be performed using AI or not. For example, the identification unit can input user emotion data into a generative AI and have the generative AI determine task priorities.

[0089] The identification unit can determine specific priorities based on the submission timing of tasks at the time of identification. For example, the identification unit can prioritize tasks with approaching deadlines. It can also postpone tasks with later submission dates. For example, the identification unit can adjust specific priorities according to the submission timing. This allows for the identification of more appropriate tasks by determining specific priorities based on the submission timing of tasks. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input task data into a generating AI and have the generating AI perform the determination of specific priorities.

[0090] The identification unit can adjust the order of tasks based on their relevance at the time of identification. For example, the identification unit can prioritize identifying tasks that are highly relevant. It can also postpone tasks that are less relevant. For example, the identification unit adjusts the order of tasks according to their relevance. This allows for the identification of more appropriate tasks by adjusting the order of tasks based on their relevance. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input task data into a generating AI and have the generating AI perform the adjustment of the order of tasks.

[0091] The dialogue unit can estimate the user's emotions and optimize the way the dialogue is expressed based on those emotions. For example, if the user is stressed, the dialogue unit will use a calm tone. It can also use a clear and concise tone if the user is focused. For example, if the user is tired, the dialogue unit will use a gentle tone. This allows for more appropriate dialogue by adjusting the way the dialogue is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 dialogue unit may be performed using AI, or not. For example, the dialogue unit can input user emotion data into a generative AI and have the generative AI optimize the way the dialogue is expressed.

[0092] The dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history during a conversation. For example, the dialogue unit may prioritize using a dialogue style that the user has preferred in the past. The dialogue unit can also suggest the optimal dialogue method based on the user's past dialogue history. For example, the dialogue unit may analyze the user's past dialogue history and select the most effective dialogue method. This allows for the selection of a more appropriate dialogue method by referring to the user's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's dialogue history data into a generating AI and have the generating AI select the optimal dialogue method.

[0093] The dialogue unit can customize the content of the conversation based on the user's current psychological state. For example, if the user is feeling anxious, the dialogue unit will engage in conversation that provides reassurance. Similarly, if the user needs motivation, the dialogue unit can engage in encouraging conversation. For example, if the user is relaxed, the dialogue unit will engage in conversation that helps maintain that relaxation. This allows for more appropriate conversations by customizing the content based on the user's current psychological state. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the dialogue unit may be performed using AI, or not. For example, the dialogue unit can input user emotion data into a generative AI and have the generative AI customize the content of the conversation.

[0094] The dialogue unit can estimate the user's emotions and determine the priority of the dialogue based on the estimated emotions. For example, if the user is feeling stressed, the dialogue unit will prioritize dialogue that helps reduce stress. The dialogue unit can also prioritize important dialogue if the user is focused. For example, if the user is tired, the dialogue unit will prioritize dialogue that promotes relaxation. This allows for more appropriate dialogue by prioritizing dialogue based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 dialogue unit may be performed using AI, or not. For example, the dialogue unit can input user emotion data into a generative AI and have the generative AI determine the priority of the dialogue.

[0095] The dialogue unit can select the optimal dialogue method during a conversation, taking into account the user's geographical location. For example, if the user is at home, the dialogue unit can provide a relaxed dialogue method. It can also provide an efficient dialogue method if the user is at work. For example, if the user is out, the dialogue unit can provide a concise and easy-to-understand dialogue method. This allows for the selection of a more appropriate dialogue method by considering the user's geographical location. Some or all of the above processing in the dialogue unit may be performed using AI, or without AI. For example, the dialogue unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal dialogue method.

[0096] The dialogue unit can analyze the user's social media activity during a conversation and suggest conversation content. For example, the dialogue unit can suggest conversation content based on what the user has shared on social media. It can also suggest conversation content based on the user's interests on social media. For example, the dialogue unit can suggest conversation content based on the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to suggest more appropriate conversation content. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's social media activity data into a generating AI and have the generating AI suggest conversation content.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The reception desk can suggest the most suitable input method when a user enters their goals and schedules, taking into account the user's past behavioral patterns. For example, if a user has frequently used voice input in the past, the reception desk will prioritize suggesting voice input. It can also send notifications prompting users to input during specific time slots if they tend to do so at those times. Furthermore, the reception desk can analyze the user's past input history and offer suggestions to improve input efficiency. This allows for more effective goal and schedule entry by considering the user's past behavioral patterns.

[0099] The analytics department can consider users' health data when analyzing their behavior patterns and achievements. For example, it can collect users' sleep and exercise data and provide advice based on that data to help them achieve their goals. It can also suggest realistic goal setting based on the user's health condition. Furthermore, the analytics department can offer suggestions for stress management and relaxation based on the user's health data. By considering the user's health condition, it can provide more appropriate feedback and improvement suggestions.

[0100] The dialogue unit can estimate the user's emotions and customize the content of the conversation based on those emotions. For example, if the user is feeling stressed, it can offer relaxation methods and advice on stress reduction. If the user needs motivation, it can also offer encouraging messages and share success stories. Furthermore, if the user is relaxed, it can suggest ways to maintain that state of relaxation. By customizing the content of the conversation based on the user's emotions, it can provide more effective mental support.

[0101] The analytics department can consider users' social media activity when analyzing their goals and plans. For example, it can suggest goals and plans based on events and activities that users have shared on social media. It can also suggest relevant goals and plans based on users' interests on social media. Furthermore, it can suggest goals and plans based on the activities of users' friends on social media. By considering users' social media activity, it is possible to suggest more relevant goals and plans.

[0102] The dialogue unit can estimate the user's emotions and optimize the dialogue's presentation based on those emotions. For example, if the user is stressed, the dialogue can be delivered in a calm tone. If the user is focused, the dialogue can be clear and concise. Furthermore, if the user is tired, the dialogue can be delivered in a gentle tone. By adjusting the dialogue's presentation based on the user's emotions, a more appropriate dialogue can be achieved.

[0103] The system can consider the user's geographical location when suggesting relevant tasks based on the user's goals and schedule. For example, it can prioritize tasks that can be performed near the user's current location. It can also suggest tasks related to a specific region if the user is in that region. Furthermore, if the user is on the move, it can suggest tasks related to their destination. This allows for the suggestion of more relevant tasks by considering the user's geographical location.

[0104] The reception system can estimate the user's emotions and optimize the timing of goal and schedule input based on those estimates. For example, if a user is feeling stressed, it can prompt them to input goals and schedules during a time when they can relax. It can also prompt users to input goals and schedules when they are focused. Furthermore, if a user is tired, it can prompt them to input goals and schedules after they have rested. By adjusting the timing of goal and schedule input based on the user's emotions, more effective goal setting becomes possible.

[0105] The analytics department can consider a user's past goal achievement record when analyzing their goals and plans. For example, it can suggest the next goal based on the goals and plans the user has achieved in the past. It can also suggest achievable goals by considering goals and plans the user has not achieved in the past. Furthermore, it can analyze the user's past goal achievement record and suggest the next goal based on the degree of achievement. This allows for more appropriate goal setting by considering the user's past goal achievement record.

[0106] The dialogue unit can estimate the user's emotions and prioritize dialogue based on those emotions. For example, if the user is stressed, it will prioritize dialogue that helps reduce stress. It can also prioritize important dialogue if the user is focused. Furthermore, if the user is tired, it can prioritize dialogue that promotes relaxation. This allows for more appropriate dialogue by prioritizing dialogue based on the user's emotions.

[0107] The specific unit can consider the user's current life circumstances and areas of interest when prioritizing and suggesting relevant tasks based on the user's goals and schedule. For example, it can suggest relevant tasks based on the user's current life circumstances (work, family, etc.). It can also suggest relevant tasks based on the user's areas of interest (hobbies, health, etc.). Furthermore, it can combine the user's current life circumstances and areas of interest to suggest the most appropriate tasks. This allows for the suggestion of more appropriate tasks by considering the user's life circumstances and areas of interest.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk inputs the user's goals and schedule. Users can input their goals and schedules using voice input or text input. The reception desk can also refer to the user's past goal and schedule input history to suggest the most suitable input method. Step 2: The analysis department analyzes the goals and schedules entered by the reception department. The analysis is performed using methods such as statistical analysis, pattern recognition, and machine learning algorithms. The analysis department continuously analyzes user behavior patterns and achievement status, and provides feedback and improvement suggestions based on this analysis. Step 3: The Identification Department identifies and prioritizes the most important tasks based on the goals and schedules analyzed by the Analysis Department. Task importance is evaluated based on criteria such as urgency, impact, and feasibility. The Identification Department then proposes relevant tasks based on the user's goals and schedules. Step 4: The dialogue unit provides emotional support to the user based on the task identified by the specific unit. The dialogue unit offers positive messages and relaxation methods. For example, if the user inputs "I'm feeling down today," it will suggest encouraging messages and relaxation methods.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, and dialogue 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, which allows the user to input their goals and schedules by voice or text. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the input goals and schedules using statistical analysis and machine learning algorithms. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12, which identifies and prioritizes the most important tasks based on the analysis results. The dialogue unit is implemented by the control unit 46A of the smart device 14, which provides the user with positive messages and ways to relax. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, and dialogue unit, is implemented 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, which allows the user to input their goals and schedules by voice. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the input goals and schedules using statistical analysis and machine learning algorithms. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12, which identifies and prioritizes the most important tasks based on the analysis results. The dialogue unit is implemented by the control unit 46A of the smart glasses 214, which provides the user with positive messages and ways to relax. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, and dialogue unit, is implemented by, for example, 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 their goals and schedules by voice. The analysis unit is implemented by, for example, the identification unit 290 of the data processing unit 12, which analyzes the input goals and schedules using statistical analysis and machine learning algorithms. The identification unit is implemented by, for example, the identification unit 290 of the data processing unit 12, which identifies and prioritizes the most important tasks based on the analysis results. The dialogue unit is implemented by, for example, the control unit 46A of the headset terminal 314, which provides the user with positive messages and ways to relax. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, and dialogue unit, is implemented by, for example, 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, which allows the user to input their goals and schedules by voice. The analysis unit is implemented by, for example, the identification unit 290 of the data processing unit 12, which analyzes the input goals and schedules using statistical analysis and machine learning algorithms. The identification unit is implemented by, for example, the identification unit 290 of the data processing unit 12, which identifies and prioritizes the most important tasks based on the analysis results. The dialogue unit is implemented by, for example, the control unit 46A of the robot 414, which provides the user with positive messages and ways to relax. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) A reception area where users input their goals and schedules, An analysis unit that analyzes the goals and schedules entered by the reception unit, The identification unit identifies and prioritizes the most important tasks based on the goals and schedules analyzed by the aforementioned analysis unit, The system includes an interactive unit that provides emotional support to the user based on the tasks identified by the aforementioned specific unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Continuously analyze user behavior patterns and achievement status. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned dialogue unit, Provides encouraging messages and relaxation methods. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is Based on the user's goals and plans, we suggest the next objective. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned dialogue unit, It analyzes the user's psychological state and provides specific advice to reduce stress and anxiety. The system described in Appendix 1, characterized by the features described herein. (Note 6) The specified part is, Prioritize and suggest relevant tasks based on the user's goals and schedule. 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 optimizes the timing of goal and appointment input 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 Analyze the user's past goal and schedule input history to select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter goals or plans, the system filters them based on their current lifestyle and areas of interest. 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 goals and plans to be entered 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 users enter goals and plans, the system prioritizes the entry of highly relevant goals and plans, 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 users enter goals and plans, the system analyzes their social media activity and inputs relevant goals and plans. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates user sentiment and optimizes goal and schedule analysis methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the goals and plans. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of goals and plans. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is We estimate the user's emotions and prioritize the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is When conducting the analysis, prioritize the analysis based on the submission deadlines for goals and plans. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relevance of goals and plans. The system described in Appendix 1, characterized by the features described herein. (Note 19) The specified part is, It estimates user emotions and optimizes task identification methods based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The specified part is, At specific times, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 21) The specified part is, At specific times, apply different specific algorithms depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The specified part is, Estimate the user's emotions and prioritize tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The specified part is, At specific times, determine specific priorities based on the submission timing of tasks. The system described in Appendix 1, characterized by the features described herein. (Note 24) The specified part is, At specific times, adjust the order of tasks based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned dialogue unit, It estimates the user's emotions and optimizes the way the dialogue is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned dialogue unit, During a conversation, the system selects the optimal conversation method by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned dialogue unit, During a conversation, the content of the conversation is customized based on the user's current psychological state. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned dialogue unit, It estimates the user's emotions and determines the priority of the conversation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned dialogue unit, During the interaction, the system selects the optimal interaction method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned dialogue unit, During conversations, the system analyzes the user's social media activity and suggests conversation topics. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 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 area where users input their goals and schedules, An analysis unit that analyzes the goals and schedules entered by the reception unit, The identification unit identifies and prioritizes the most important tasks based on the goals and schedules analyzed by the aforementioned analysis unit, The system includes an interactive unit that provides emotional support to the user based on the tasks identified by the aforementioned specific unit. A system characterized by the following features.

2. The aforementioned analysis unit is Continuously analyze user behavior patterns and achievement status. The system according to feature 1.

3. The aforementioned dialogue unit, Provides encouraging messages and relaxation methods. The system according to feature 1.

4. The aforementioned analysis unit is Based on the user's goals and plans, we suggest the next objective. The system according to feature 1.

5. The aforementioned dialogue unit, It analyzes the user's psychological state and provides specific advice to reduce stress and anxiety. The system according to feature 1.

6. The specified part is, Prioritize and suggest relevant tasks based on the user's goals and schedule. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and optimizes the timing of goal and appointment input based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past goal and schedule input history to select the optimal input method. The system according to feature 1.

Images