System

Abstract

An object of a system according to an embodiment is to enable a user to efficiently learn skills and knowledge necessary for achieving a goal and to maintain an optimal schedule.SOLUTION: A system according to an embodiment includes an objective setting unit, a skill proposing unit, a schedule generating unit, an alarm setting unit, a schedule changing unit, and an execution checking unit. The goal setting unit sets a goal of the user. The skill proposal unit proposes a necessary skill or knowledge on the basis of the objective set by the objective setting unit. The schedule generation unit generates an optimal daily schedule on the basis of the skills and knowledge proposed by the skill proposal unit. The alarm setting unit sets an alarm based on the schedule generated by the schedule generation unit. The schedule change unit changes the schedule in real time based on the alarm set by the alarm setting unit. The execution confirmation unit confirms execution of the schedule changed by the schedule change unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem of making it difficult for users to efficiently acquire the skills and knowledge necessary to achieve their goals and maintain an optimal schedule.

[0005] The system according to the embodiment aims to enable a user to efficiently acquire the skills and knowledge necessary to achieve a goal and maintain an optimal schedule. [Means for solving the problem]

[0006] The system according to the embodiment includes a goal setting unit, a skill suggestion unit, a schedule generation unit, an alarm setting unit, a schedule change unit, and an execution confirmation unit. The goal setting unit sets a goal for the user. The skill suggestion unit suggests necessary skills and knowledge based on the goal set by the goal setting unit. The schedule generation unit generates an optimal daily schedule based on the skills and knowledge suggested by the skill suggestion unit. The alarm setting unit sets an alarm based on the schedule generated by the schedule generation unit. The schedule change unit changes the schedule in real time based on the alarm set by the alarm setting unit. The execution confirmation unit confirms the execution of the schedule changed by the schedule change unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to efficiently acquire the skills and knowledge necessary to achieve a goal and maintain an optimal schedule. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A time management system according to an embodiment of the present invention is a system in which a generative AI designs an optimal way to spend a day and supports the execution of that plan to help a user achieve their goals. This allows the time management system to efficiently support the user in achieving their goals.

[0029] A time management system according to an embodiment includes a goal setting unit, a skill suggestion unit, a schedule generation unit, an alarm setting unit, a schedule change unit, and an execution confirmation unit. The goal setting unit sets a goal for a user. For example, the user sets a goal such as "I want to be able to speak English fluently." The skill suggestion unit suggests necessary skills and knowledge based on the goal set by the goal setting unit. For example, the skill suggestion unit makes specific suggestions such as "30 minutes of listening practice every day" and "three English conversation lessons per week" to learn English. The schedule generation unit generates an optimal daily schedule based on the skills and knowledge suggested by the skill suggestion unit. For example, if a user works from 8:00 a.m. to 5:00 p.m., the schedule generation unit proposes schedules such as "exercise from 6:00 a.m. to 7:00 a.m." and "study English from 8:00 p.m. to 9:00 p.m." by utilizing the time before and after the workday. The alarm setting unit sets an alarm based on the schedule generated by the schedule generation unit. For example, the alarm setting unit sets an alarm to start exercising at 6:00 a.m. and an alarm to start studying English at 8:00 p.m. The schedule change unit changes the schedule in real time based on the alarm set by the alarm setting unit. For example, if the user fails to carry out the proposed schedule, the generation AI readjusts the schedule in real time to ensure time to achieve the goal. The execution confirmation unit confirms the execution of the schedule changed by the schedule change unit. For example, it asks questions such as "Have you completed your exercise?" or "Have you finished your English study?", and provides positive feedback such as "Well done!" if the user has carried out the schedule. In this way, the time management system according to the embodiment can design an optimal way to spend the day to help the user achieve their goals and support the execution of that plan.

[0030] The skill suggestion unit can analyze a user's past behavioral history and learning history to suggest individually optimized skills and knowledge. For example, the generation AI in the skill suggestion unit analyzes a user's past behavioral history to find specific patterns. For example, it analyzes what learning methods the user has tried in the past and what time of day was most effective for learning, and based on that, suggests optimal skills and knowledge. The generation AI also analyzes a user's past learning history to evaluate their learning progress and results. For example, it analyzes what learning materials the user has used in the past and their level of understanding, and based on that, suggests optimal skills and knowledge. The generation AI also analyzes a user's behavioral history and learning history in combination to suggest individually optimized skills and knowledge. For example, it makes new suggestions based on the user's past successful learning methods and time periods. This allows optimal skills and knowledge to be suggested based on the user's past behavioral history and learning history.

[0031] The skill suggestion unit monitors the user's interests in real time and dynamically updates the skill and knowledge suggestions based on them. For example, the generation AI in the skill suggestion unit monitors the user's social media and browsing history in real time to analyze their interests. For example, it suggests related skills and knowledge based on the websites the user frequently visits and the accounts they follow. The generation AI in the skill suggestion unit also monitors the user's real-time behavior and analyzes changes in their interests. For example, it dynamically updates the skill and knowledge suggestions based on new topics or activities the user has become interested in. The generation AI in the skill suggestion unit also monitors the user's interests in real time and optimizes the suggestions through a feedback loop. For example, it analyzes how the user responds to suggested skills and knowledge and updates the suggestions based on that. This allows the skill and knowledge suggestions to be dynamically updated based on the user's interests.

[0032] The schedule generation unit can analyze the user's biological rhythms and sleep patterns and design an optimal daily schedule based on them. In the schedule generation unit, for example, a generation AI analyzes the user's biological rhythms and designs an optimal daily schedule. For example, the most effective study and exercise times are suggested based on the user's sleep patterns. In addition, the schedule generation unit analyzes the user's sleep patterns and evaluates the quality and duration of sleep. For example, the optimal schedule is suggested based on the user's sleep cycle and the ratio of REM to non-REM sleep. In addition, the schedule generation unit analyzes the user's biological rhythms and sleep patterns in combination and designs an optimal daily schedule. For example, new suggestions are made based on the user's activity rhythm and hormone balance. This makes it possible to design an optimal daily schedule based on the user's biological rhythms and sleep patterns.

[0033] The schedule generation unit can learn the user's past schedule execution history and propose schedule patterns with a high success rate. In the schedule generation unit, for example, the generation AI learns the user's past schedule execution history and proposes schedule patterns with a high success rate. For example, a new proposal is made based on schedules that the user has successfully executed in the past. In addition, the schedule generation unit uses the generation AI to analyze the user's schedule execution history and evaluate the achievement rate and execution time. For example, it analyzes what kind of schedules the user has executed in the past and the success rate, and proposes an optimal schedule based on that. In addition, the schedule generation unit uses the generation AI to analyze the user's schedule execution history in combination with the success rate and propose schedule patterns with a high success rate. For example, a new proposal is made based on schedule patterns that the user has successfully executed in the past. In this way, it is possible to propose schedule patterns with a high success rate based on the user's past schedule execution history.

[0034] The schedule generation unit visualizes the user's daily activities and can propose a schedule that is visually easy to understand. In the schedule generation unit, for example, the generation AI visualizes the user's daily activities and proposes a schedule that is visually easy to understand. For example, the schedule is displayed using graphs and charts. In addition, the schedule generation unit analyzes the user's activity log and displays the schedule in timeline format. For example, the user's daily activities are displayed along a timeline and proposed in a format that is visually easy to understand. In addition, the schedule generation unit visualizes the user's daily activities and proposes a schedule using an interactive UI. For example, the user can adjust the schedule by drag and drop. This makes it possible to visualize the user's daily activities and propose a schedule that is visually easy to understand.

[0035] The alarm setting unit can analyze the user's real-time location information and set the alarm at the optimal timing. In the alarm setting unit, for example, the generation AI analyzes the user's real-time location information and sets the alarm at the optimal timing. For example, different alarms are set when the user is at home and when the user is out. In addition, the alarm setting unit optimizes the timing of the alarm based on the user's location information. For example, the alarm is set to sound when the user arrives at a specific location. In addition, the alarm setting unit develops an algorithm for analyzing the user's real-time location information and optimizing the timing of the alarm. For example, the generation AI analyzes the user's movement patterns and sets the alarm at the optimal timing. This makes it possible to set the alarm at the optimal timing based on the user's real-time location information.

[0036] The alarm setting unit learns the user's past alarm execution history and can set alarms with a high success rate. In the alarm setting unit, for example, the generation AI learns the user's past alarm execution history and sets alarms with a high success rate. For example, it makes new suggestions based on the user's successful alarm settings in the past. In addition, the alarm setting unit analyzes the user's alarm execution history and evaluates the achievement rate and execution time. For example, it analyzes what kind of alarms the user has set in the past and what the success rate was, and based on that, suggests the optimal alarm. In addition, the alarm setting unit analyzes the user's alarm execution history in combination with the success rate and sets alarms with a high success rate. For example, it makes new suggestions based on the user's successful alarm settings in the past. This makes it possible to set alarms with a high success rate based on the user's past alarm execution history.

[0037] The alarm setting unit links the user's schedule with other devices and enables alarms to be set from multiple devices. For example, the alarm setting unit constructs a system in which the generation AI links the user's schedule with other devices and sets alarms from multiple devices. For example, alarms can be set simultaneously from a smartphone and a smartwatch. The alarm setting unit also optimizes how the generation AI synchronizes the user's devices. For example, it centrally manages notifications between devices and sets alarms from multiple devices. The alarm setting unit also develops an algorithm for the generation AI to link the user's schedule with other devices and optimize alarm settings. For example, it strengthens the link between the user's devices and sets alarms at optimal times. This allows the user's schedule to be linked with other devices and alarms to be set from multiple devices.

[0038] The alarm setting unit can optimize real-time changes by sharing the user's schedule with other users and receiving mutual feedback. The alarm setting unit, for example, builds a system in which the generation AI shares the user's schedule with other users and receives mutual feedback. For example, the schedule is shared through an online platform. The alarm setting unit also has the generation AI share the user's schedule and collect feedback from other users. For example, the alarm setting unit receives comments and ratings on the user's schedule and optimizes the schedule based on that. The alarm setting unit also has the generation AI share the user's schedule with other users and receives mutual feedback, thereby optimizing real-time changes. For example, the user compares schedules with other users and incorporates best practices. This allows the user's schedule to be shared with other users and receive mutual feedback, thereby optimizing real-time changes.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The time management system can also include a health management unit that monitors the user's health status and makes suggestions for maintaining health. For example, it can monitor the user's heart rate and blood pressure in real time and suggest rest if abnormalities are detected. The health management unit can also analyze the user's food records and suggest nutritionally balanced meal plans. It can also suggest appropriate exercise programs based on the user's exercise history. This allows for comprehensive management of the user's health status and supports them in achieving their goals.

[0041] The time management system may further include a relaxation suggestion unit that suggests relaxation times based on the user's hobbies and interests. For example, if the user likes listening to music, the relaxation suggestion unit may suggest listening to music at a specific time period. If the user likes reading, the relaxation suggestion unit may incorporate reading time into the schedule. Furthermore, if the user enjoys outdoor activities, the relaxation suggestion unit may suggest hiking or picnics on the weekend. In this way, relaxation times can be suggested based on the user's hobbies and interests, thereby reducing stress.

[0042] The time management system may further include a social suggestion unit to support the user's social activities. For example, if the user values ​​socializing with friends, the social suggestion unit may suggest scheduling regular meetings with friends. If the user likes to meet new people, the social suggestion unit may suggest local events or club activities. Furthermore, if the user likes to interact online, the social suggestion unit may suggest online meetings or chat sessions. This supports the user's social activities and allows them to live a fulfilling life.

[0043] The time management system may further include a learning style suggestion unit that suggests an optimal learning method based on the user's learning style. For example, if the user prefers visual learning, the learning style suggestion unit may suggest learning materials using visual aids. If the user prefers auditory learning, the learning style suggestion unit may suggest audiobooks or podcasts. Furthermore, if the user prefers practical learning, the learning style suggestion unit may suggest practical workshops or hands-on sessions. This allows the system to suggest optimal learning methods based on the user's learning style, thereby improving learning effectiveness.

[0044] The time management system can further include a visualization unit that visualizes the user's daily activities and proposes a schedule that is easy to understand visually. For example, the generation AI displays the user's daily activities in graphs and charts, proposing a schedule that is easy to understand visually. The visualization unit can also display the user's activity log in a timeline format and propose a schedule along the time axis. Furthermore, the visualization unit can use an interactive UI to allow the user to adjust the schedule by dragging and dropping. This makes it possible to visualize the user's daily activities and propose a schedule that is easy to understand visually.

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

[0046] Step 1: The goal setting unit sets a goal for the user. For example, the user sets a goal such as "I want to be able to speak English fluently." Step 2: The Skill Suggestion Department suggests the necessary skills and knowledge based on the goals set by the Goal Setting Department. For example, it makes specific suggestions such as "30 minutes of listening practice every day" and "three English conversation lessons per week" to learn English. Step 3: The schedule generation unit generates an optimal daily schedule based on the skills and knowledge suggested by the skill suggestion unit. For example, if a user works from 8:00 AM to 5:00 PM, the unit will suggest schedules that utilize the time before and after that, such as "exercise from 6:00 AM to 7:00 AM" or "study English from 8:00 PM to 9:00 PM." Step 4: The alarm setting unit sets alarms based on the schedule generated by the schedule generation unit. For example, an alarm for starting exercise at 6:00 AM or an alarm for starting English study at 8:00 PM is set. Step 5: The schedule change unit changes the schedule in real time based on the alarm set by the alarm setting unit. For example, if the user fails to carry out the proposed schedule, the generation AI readjusts the schedule in real time to ensure time to achieve the goal. Step 6: The execution confirmation unit checks the execution of the schedule changed by the schedule change unit. For example, it checks "Have you completed your exercise?" or "Have you finished your English study?" and provides positive feedback such as "Well done!" if the user has executed the schedule.

[0047] (Example 2) A time management system according to an embodiment of the present invention is a system in which a generative AI designs an optimal way to spend a day and supports the execution of that plan to help a user achieve their goals. This allows the time management system to efficiently support the user in achieving their goals.

[0048] A time management system according to an embodiment includes a goal setting unit, a skill suggestion unit, a schedule generation unit, an alarm setting unit, a schedule change unit, and an execution confirmation unit. The goal setting unit sets a goal for a user. For example, the user sets a goal such as "I want to be able to speak English fluently." The skill suggestion unit suggests necessary skills and knowledge based on the goal set by the goal setting unit. For example, the skill suggestion unit makes specific suggestions such as "30 minutes of listening practice every day" and "three English conversation lessons per week" to learn English. The schedule generation unit generates an optimal daily schedule based on the skills and knowledge suggested by the skill suggestion unit. For example, if a user works from 8:00 a.m. to 5:00 p.m., the schedule generation unit proposes schedules such as "exercise from 6:00 a.m. to 7:00 a.m." and "study English from 8:00 p.m. to 9:00 p.m." by utilizing the time before and after the workday. The alarm setting unit sets an alarm based on the schedule generated by the schedule generation unit. For example, the alarm setting unit sets an alarm to start exercising at 6:00 a.m. and an alarm to start studying English at 8:00 p.m. The schedule change unit changes the schedule in real time based on the alarm set by the alarm setting unit. For example, if the user fails to carry out the proposed schedule, the generation AI readjusts the schedule in real time to ensure time to achieve the goal. The execution confirmation unit confirms the execution of the schedule changed by the schedule change unit. For example, it asks questions such as "Have you completed your exercise?" or "Have you finished your English study?", and provides positive feedback such as "Well done!" if the user has carried out the schedule. In this way, the time management system according to the embodiment can design an optimal way to spend the day to help the user achieve their goals and support the execution of that plan.

[0049] The skill suggestion unit can analyze a user's past behavioral history and learning history to suggest individually optimized skills and knowledge. For example, the generation AI in the skill suggestion unit analyzes a user's past behavioral history to find specific patterns. For example, it analyzes what learning methods the user has tried in the past and what time of day was most effective for learning, and based on that, suggests optimal skills and knowledge. The generation AI also analyzes a user's past learning history to evaluate their learning progress and results. For example, it analyzes what learning materials the user has used in the past and their level of understanding, and based on that, suggests optimal skills and knowledge. The generation AI also analyzes a user's behavioral history and learning history in combination to suggest individually optimized skills and knowledge. For example, it makes new suggestions based on the user's past successful learning methods and time periods. This allows optimal skills and knowledge to be suggested based on the user's past behavioral history and learning history.

[0050] The skill suggestion unit monitors the user's interests in real time and dynamically updates the skill and knowledge suggestions based on them. For example, the generation AI in the skill suggestion unit monitors the user's social media and browsing history in real time to analyze their interests. For example, it suggests related skills and knowledge based on the websites the user frequently visits and the accounts they follow. The generation AI in the skill suggestion unit also monitors the user's real-time behavior and analyzes changes in their interests. For example, it dynamically updates the skill and knowledge suggestions based on new topics or activities the user has become interested in. The generation AI in the skill suggestion unit also monitors the user's interests in real time and optimizes the suggestions through a feedback loop. For example, it analyzes how the user responds to suggested skills and knowledge and updates the suggestions based on that. This allows the skill and knowledge suggestions to be dynamically updated based on the user's interests.

[0051] The skill suggestion unit uses the emotion estimation function to suggest skills and knowledge that evoke the most positive emotions in the user, thereby increasing motivation. For example, the skill suggestion unit uses the emotion estimation function to analyze the user's emotions toward proposed skills and knowledge. For example, it prioritizes suggesting skills and knowledge for which the user evokes positive emotions. The skill suggestion unit also uses the emotion estimation function to develop an algorithm for suggesting skills and knowledge for which the user evokes the most positive emotions. For example, it analyzes the user's facial expressions and voice and makes suggestions based on emotion scores. The skill suggestion unit also uses the emotion estimation function to optimize skill and knowledge suggestions based on the user's emotions. For example, if the user evokes positive emotions toward the proposed skills and knowledge, it reinforces the suggestions. This draws out the user's positive emotions and increases their motivation.

[0052] The schedule generation unit can analyze the user's biological rhythms and sleep patterns and design an optimal daily schedule based on them. In the schedule generation unit, for example, a generation AI analyzes the user's biological rhythms and designs an optimal daily schedule. For example, the most effective study and exercise times are suggested based on the user's sleep patterns. In addition, the schedule generation unit analyzes the user's sleep patterns and evaluates the quality and duration of sleep. For example, the optimal schedule is suggested based on the user's sleep cycle and the ratio of REM to non-REM sleep. In addition, the schedule generation unit analyzes the user's biological rhythms and sleep patterns in combination and designs an optimal daily schedule. For example, new suggestions are made based on the user's activity rhythm and hormone balance. This makes it possible to design an optimal daily schedule based on the user's biological rhythms and sleep patterns.

[0053] The schedule generation unit can learn the user's past schedule execution history and propose schedule patterns with a high success rate. In the schedule generation unit, for example, the generation AI learns the user's past schedule execution history and proposes schedule patterns with a high success rate. For example, a new proposal is made based on schedules that the user has successfully executed in the past. In addition, the schedule generation unit uses the generation AI to analyze the user's schedule execution history and evaluate the achievement rate and execution time. For example, it analyzes what kind of schedules the user has executed in the past and the success rate, and proposes an optimal schedule based on that. In addition, the schedule generation unit uses the generation AI to analyze the user's schedule execution history in combination with the success rate and propose schedule patterns with a high success rate. For example, a new proposal is made based on schedule patterns that the user has successfully executed in the past. In this way, it is possible to propose schedule patterns with a high success rate based on the user's past schedule execution history.

[0054] The schedule generation unit uses the emotion estimation function to design a schedule that will make the user feel the most positive emotions, thereby increasing motivation to perform. The schedule generation unit, for example, uses the emotion estimation function to design a schedule that will make the user feel the most positive emotions. For example, important tasks are placed in time periods when the user feels positive emotions. The schedule generation unit also uses the emotion estimation function to optimize the schedule based on the user's emotions. For example, it analyzes how the user feels about a proposed schedule and updates the schedule based on that. The schedule generation unit also uses the emotion estimation function to design a schedule that will draw out positive emotions from the user. For example, it incorporates activities that make the user feel the most positive emotions into the schedule. This draws out positive emotions from the user and increases motivation to perform.

[0055] The schedule generation unit visualizes the user's daily activities and can propose a schedule that is visually easy to understand. In the schedule generation unit, for example, the generation AI visualizes the user's daily activities and proposes a schedule that is visually easy to understand. For example, the schedule is displayed using graphs and charts. In addition, the schedule generation unit analyzes the user's activity log and displays the schedule in timeline format. For example, the user's daily activities are displayed along a timeline and proposed in a format that is visually easy to understand. In addition, the schedule generation unit visualizes the user's daily activities and proposes a schedule using an interactive UI. For example, the user can adjust the schedule by drag and drop. This makes it possible to visualize the user's daily activities and propose a schedule that is visually easy to understand.

[0056] The schedule generation unit uses the emotion estimation function to analyze how the user feels about the proposed schedule and can propose an optimal schedule. The schedule generation unit, for example, uses the emotion estimation function to analyze in real time how the user feels about the proposed schedule. For example, it preferentially proposes schedules in which the user expresses positive emotions. The schedule generation unit also uses the emotion estimation function to optimize the schedule based on the user's emotions. For example, it analyzes how the user feels about the proposed schedule and updates the schedule based on that. The schedule generation unit also uses the emotion estimation function to develop an algorithm for optimizing the schedule based on the user's emotions. For example, it analyzes the user's facial expressions and voice and makes suggestions based on emotion scores. This makes it possible to propose an optimal schedule based on the user's emotions.

[0057] The alarm setting unit can analyze the user's real-time location information and set the alarm at the optimal timing. In the alarm setting unit, for example, the generation AI analyzes the user's real-time location information and sets the alarm at the optimal timing. For example, different alarms are set when the user is at home and when the user is out. In addition, the alarm setting unit optimizes the timing of the alarm based on the user's location information. For example, the alarm is set to sound when the user arrives at a specific location. In addition, the alarm setting unit develops an algorithm for analyzing the user's real-time location information and optimizing the timing of the alarm. For example, the generation AI analyzes the user's movement patterns and sets the alarm at the optimal timing. This makes it possible to set the alarm at the optimal timing based on the user's real-time location information.

[0058] The alarm setting unit learns the user's past alarm execution history and can set alarms with a high success rate. In the alarm setting unit, for example, the generation AI learns the user's past alarm execution history and sets alarms with a high success rate. For example, it makes new suggestions based on the user's successful alarm settings in the past. In addition, the alarm setting unit analyzes the user's alarm execution history and evaluates the achievement rate and execution time. For example, it analyzes what kind of alarms the user has set in the past and what the success rate was, and based on that, suggests the optimal alarm. In addition, the alarm setting unit analyzes the user's alarm execution history in combination with the success rate and sets alarms with a high success rate. For example, it makes new suggestions based on the user's successful alarm settings in the past. This makes it possible to set alarms with a high success rate based on the user's past alarm execution history.

[0059] The alarm setting unit uses the emotion estimation function to set an alarm sound or message that evokes the most positive emotion in the user, thereby increasing motivation to act. The alarm setting unit, for example, uses the emotion estimation function to set an alarm sound or message that evokes the most positive emotion in the user. For example, music or a message that expresses a positive emotion in the user is set as the alarm. The alarm setting unit also uses the emotion estimation function to optimize the alarm sound or message based on the user's emotion. For example, the alarm setting unit analyzes the emotion the user evokes in a proposed alarm sound or message, and sets the alarm based on that. The alarm setting unit also uses the emotion estimation function to develop an algorithm for optimizing the alarm sound or message based on the user's emotion. For example, the alarm setting unit analyzes the user's facial expression and voice, and sets the alarm based on an emotion score. This makes it possible to elicit positive emotion in the user and increase motivation to act.

[0060] The alarm setting unit links the user's schedule with other devices and enables alarms to be set from multiple devices. For example, the alarm setting unit constructs a system in which the generation AI links the user's schedule with other devices and sets alarms from multiple devices. For example, alarms can be set simultaneously from a smartphone and a smartwatch. The alarm setting unit also optimizes how the generation AI synchronizes the user's devices. For example, it centrally manages notifications between devices and sets alarms from multiple devices. The alarm setting unit also develops an algorithm for the generation AI to link the user's schedule with other devices and optimize alarm settings. For example, it strengthens the link between the user's devices and sets alarms at optimal times. This allows the user's schedule to be linked with other devices and alarms to be set from multiple devices.

[0061] The alarm setting unit can optimize real-time changes by sharing the user's schedule with other users and receiving mutual feedback. The alarm setting unit, for example, builds a system in which the generation AI shares the user's schedule with other users and receives mutual feedback. For example, the schedule is shared through an online platform. The alarm setting unit also has the generation AI share the user's schedule and collect feedback from other users. For example, the alarm setting unit receives comments and ratings on the user's schedule and optimizes the schedule based on that. The alarm setting unit also has the generation AI share the user's schedule with other users and receives mutual feedback, thereby optimizing real-time changes. For example, the user compares schedules with other users and incorporates best practices. This allows the user's schedule to be shared with other users and receive mutual feedback, thereby optimizing real-time changes.

[0062] The alarm setting unit uses the emotion estimation function to analyze how the user feels about the proposed alarm or schedule change and can suggest optimal changes. The alarm setting unit, for example, uses the emotion estimation function to analyze in real time how the user feels about the proposed alarm or schedule change. For example, it prioritizes suggesting changes that show the user's positive emotions. The alarm setting unit also uses the emotion estimation function to optimize the alarm or schedule change based on the user's emotions. For example, it analyzes how the user feels about the proposed alarm or schedule change and suggests changes based on that. The alarm setting unit also uses the emotion estimation function to develop an algorithm for optimizing the alarm or schedule change based on the user's emotions. For example, it analyzes the user's facial expressions and voice and suggests changes based on an emotion score. This makes it possible to suggest optimal alarms and schedule changes based on the user's emotions.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The time management system can also include a health management unit that monitors the user's health status and makes suggestions for maintaining health. For example, it can monitor the user's heart rate and blood pressure in real time and suggest rest if abnormalities are detected. The health management unit can also analyze the user's food records and suggest nutritionally balanced meal plans. It can also suggest appropriate exercise programs based on the user's exercise history. This allows for comprehensive management of the user's health status and supports them in achieving their goals.

[0065] The time management system may further include a relaxation suggestion unit that suggests relaxation times based on the user's hobbies and interests. For example, if the user likes listening to music, the relaxation suggestion unit may suggest listening to music at a specific time period. If the user likes reading, the relaxation suggestion unit may incorporate reading time into the schedule. Furthermore, if the user enjoys outdoor activities, the relaxation suggestion unit may suggest hiking or picnics on the weekend. In this way, relaxation times can be suggested based on the user's hobbies and interests, thereby reducing stress.

[0066] The time management system may further include a social suggestion unit to support the user's social activities. For example, if the user values ​​socializing with friends, the social suggestion unit may suggest scheduling regular meetings with friends. If the user likes to meet new people, the social suggestion unit may suggest local events or club activities. Furthermore, if the user likes to interact online, the social suggestion unit may suggest online meetings or chat sessions. This supports the user's social activities and allows them to live a fulfilling life.

[0067] The time management system may further include a learning style suggestion unit that suggests an optimal learning method based on the user's learning style. For example, if the user prefers visual learning, the learning style suggestion unit may suggest learning materials using visual aids. If the user prefers auditory learning, the learning style suggestion unit may suggest audiobooks or podcasts. Furthermore, if the user prefers practical learning, the learning style suggestion unit may suggest practical workshops or hands-on sessions. This allows the system to suggest optimal learning methods based on the user's learning style, thereby improving learning effectiveness.

[0068] The time management system may further include a relaxation suggestion unit that estimates the user's emotions and suggests relaxation time based on the emotions. For example, if the user is feeling stressed, the relaxation suggestion unit may suggest time to meditate or take deep breaths. Also, if the user is feeling tired, the relaxation suggestion unit may suggest time to take a short nap or listen to relaxing music. Furthermore, if the user is showing positive emotions, the relaxation suggestion unit may suggest activities to maintain those emotions. In this way, relaxation time can be suggested based on the user's emotions, thereby reducing stress.

[0069] The time management system may further include a learning style suggestion unit that estimates the user's emotions and suggests a study method based on the emotions. For example, if the user shows positive emotions, the learning style suggestion unit may suggest a challenging task. Alternatively, if the user shows negative emotions, the learning style suggestion unit may suggest a relaxing study method. Furthermore, if the user lacks concentration, the learning style suggestion unit may suggest a short study session. This allows the system to suggest an optimal study method based on the user's emotions and improve the learning effect.

[0070] The time management system may further include a social suggestion unit that estimates the user's emotions and suggests social activities based on the emotions. For example, if the user feels lonely, the social suggestion unit may suggest meeting up with friends or chatting online. If the user shows positive emotions, the social suggestion unit may suggest meeting new people. Furthermore, if the user feels stressed, the social suggestion unit may suggest relaxing social activities. In this way, optimal social activities are suggested based on the user's emotions, enabling the user to live a fulfilling life.

[0071] The time management system may further include a health management unit that estimates the user's emotions and makes suggestions for maintaining health based on the emotions. For example, if the user is feeling stressed, the health management unit may suggest relaxing exercise or meditation. If the user is feeling tired, the health management unit may also suggest rest or nutritional supplementation. Furthermore, if the user is expressing positive emotions, the health management unit may also suggest activities to maintain those emotions. In this way, suggestions for maintaining health based on the user's emotions can be made, enabling comprehensive management of the user's health condition.

[0072] The time management system may further include an alarm setting unit that estimates the user's emotions and sets an alarm sound or message based on the user's emotions. For example, if the user is feeling positive, the alarm setting unit may set uplifting music or an encouraging message. If the user is feeling negative, the alarm setting unit may set relaxing music or a gentle message. Furthermore, if the user is lacking in concentration, the alarm setting unit may set music or a message that will help the user concentrate. In this way, the optimal alarm sound or message can be set based on the user's emotions, increasing motivation to perform.

[0073] The time management system can further include a visualization unit that visualizes the user's daily activities and proposes a schedule that is easy to understand visually. For example, the generation AI displays the user's daily activities in graphs and charts, proposing a schedule that is easy to understand visually. The visualization unit can also display the user's activity log in a timeline format and propose a schedule along the time axis. Furthermore, the visualization unit can use an interactive UI to allow the user to adjust the schedule by dragging and dropping. This makes it possible to visualize the user's daily activities and propose a schedule that is easy to understand visually.

[0074] The processing flow of the second embodiment will be briefly explained below.

[0075] Step 1: The goal setting unit sets a goal for the user. For example, the user sets a goal such as "I want to be able to speak English fluently." Step 2: The Skill Suggestion Department suggests the necessary skills and knowledge based on the goals set by the Goal Setting Department. For example, it makes specific suggestions such as "30 minutes of listening practice every day" and "three English conversation lessons per week" to learn English. Step 3: The schedule generation unit generates an optimal daily schedule based on the skills and knowledge suggested by the skill suggestion unit. For example, if a user works from 8:00 AM to 5:00 PM, the unit will suggest schedules that utilize the time before and after that, such as "exercise from 6:00 AM to 7:00 AM" or "study English from 8:00 PM to 9:00 PM." Step 4: The alarm setting unit sets alarms based on the schedule generated by the schedule generation unit. For example, an alarm for starting exercise at 6:00 AM or an alarm for starting English study at 8:00 PM is set. Step 5: The schedule change unit changes the schedule in real time based on the alarm set by the alarm setting unit. For example, if the user fails to carry out the proposed schedule, the generation AI readjusts the schedule in real time to ensure time to achieve the goal. Step 6: The execution confirmation unit checks the execution of the schedule changed by the schedule change unit. For example, it checks "Have you completed your exercise?" or "Have you finished your English study?" and provides positive feedback such as "Well done!" if the user has executed the schedule.

[0076] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0077] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0078] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0079] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0080] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0081] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0082] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0083] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0084] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0085] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0086] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0087] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0088] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0089] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0090] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0091] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0092] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0094] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0095] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0096] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0097] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0098] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0099] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0100] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0101] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0102] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0106] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0109] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0110] 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.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0117] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0126] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0127] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0128] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0129] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0130] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0131] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0132] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0133] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0134] 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.

[0135] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0136] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0137] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0138] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0139] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0140] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0141] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0142] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0143] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a goal setting unit that sets a goal for the user; a skill suggestion unit that suggests necessary skills and knowledge based on the goal set by the goal setting unit; a schedule generation unit that generates an optimal daily schedule based on the skills and knowledge proposed by the skill proposal unit; an alarm setting unit that sets an alarm based on the schedule generated by the schedule generating unit; a schedule change unit that changes the schedule in real time based on the alarm set by the alarm setting unit; an execution confirmation unit that confirms the execution of the schedule changed by the schedule change unit; A system characterized by:

2. The skill suggestion unit Analyze the user's past behavioral and learning history and propose individually optimized skills and knowledge.

2. The system of claim 1.

3. The schedule generation unit Analyze the user's biological rhythms and sleep patterns and design an optimal daily schedule based on that.

2. The system of claim 1.

4. The alarm setting unit Analyzing the user's real-time location information and setting an alarm at the optimal timing 2. The system of claim 1.

5. The skill suggestion unit Propose skills and knowledge that the user feels most positive about, thereby increasing motivation.

2. The system of claim 1.

6. The schedule generation unit Design a schedule that gives the user the most positive feelings and increases motivation to carry it out 2. The system of claim 1.

7. The alarm setting unit Set alarm sounds and messages that evoke the most positive feelings for the user, motivating them to take action 2. The system of claim 1.

8. The alarm setting unit Analyze how the user feels about the proposed alarm or schedule change and suggest the optimal change 2. The system of claim 1.

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