system

Abstract

The system according to this embodiment aims to suggest the most suitable exercise, lessons, and events to the user. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a suggestion unit. The collection unit collects information such as the user's personality, preferences, hobbies, and schedule. The analysis unit analyzes the information collected by the collection unit and suggests the most suitable exercise, lessons, or events for the user. The suggestion unit suggests the most suitable activities based on the results of the analysis performed by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for a user to find exercises, hobbies, and events suitable for themselves.

[0005] The system according to the embodiment aims to propose exercises, hobbies, and events optimal for the user.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects information such as the user's personality, preferences, hobbies, and schedule. The analysis unit analyzes the information collected by the collection unit and proposes exercises, hobbies, and events optimal for the user. The proposal unit proposes an optimal activity based on the result analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest optimal exercises, lessons, and events to the user. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The activity suggestion system according to an embodiment of the present invention is a system that suggests optimized exercise, lessons, and events based on the user's personality, preferences, hobbies, schedule, etc. The activity suggestion system collects information such as the user's personality, preferences, hobbies, and schedule, analyzes the collected information, and suggests the most suitable exercise, lessons, and events for the user. Furthermore, it suggests activities that are best suited to each individual user based on specific rankings and activity results. It analyzes and generates activity requests directly from conversations with the user and suggests new activities in real time based on activity data of people around the world. This mechanism provides an environment in which users can spend their time more meaningfully and lead fulfilling lives. For example, the activity suggestion system collects information such as the user's personality, preferences, hobbies, and schedule. At this time, it collects detailed information such as what kind of activities the user likes, what kind of hobbies they have, and what kind of schedule they live on. For example, if the user likes sports, it collects information such as the type of sport, frequency, and time of day. This makes it possible to understand the user's personality, preferences, hobbies, and schedule. Next, the collected information is analyzed by AI. Based on the collected information, the AI ​​suggests the most suitable exercise, lessons, and events for the user. For example, if a user enjoys sports, the system will suggest events and classes related to that sport. It can also suggest activities at the most suitable times based on the user's schedule. This allows users to find activities that suit them. Furthermore, it suggests activities best suited to each individual user based on specific rankings and activity results. For instance, it can suggest optimal activities in a ranked format based on past activity results and evaluations from other users. This makes it easier for users to choose activities that suit them. Additionally, it can analyze and generate activity requests directly from conversations with users and suggest new activities in real time based on activity data from people worldwide. For example, if a user requests to "find a new hobby," the AI ​​can analyze that request and suggest new hobbies based on activity data from people around the world. This ensures users are always discovering new activities.This system provides users with an environment where they can spend their time more meaningfully and lead fulfilling lives. Users can find activities that suit them and enjoy them in accordance with their lifestyle schedule. For example, it can meet the needs of people who are working from home or have more free time and want to use their time meaningfully. It also solves the problem of difficulty in making the best choice among the increasing number of activity options. As a result, users can spend their time more meaningfully and lead fulfilling lives. The activity suggestion system can suggest the most suitable activities based on the user's personality, preferences, hobbies, and schedule.

[0029] The activity suggestion system according to the embodiment comprises a collection unit, an analysis unit, and a suggestion unit. The collection unit collects information such as the user's personality, preferences, hobbies, and schedule. For example, the collection unit collects detailed information such as what kind of activities the user likes, what hobbies they have, and what kind of schedule they live. For example, if the user likes sports, the collection unit collects information such as the type of sport, frequency, and time of day. The collection unit can also conduct psychological tests and questionnaires to understand the user's personality, preferences, hobbies, and schedule. For example, the collection unit conducts an online questionnaire for the user to collect information about their personality, preferences, hobbies, and schedule. Furthermore, the collection unit can also collect detailed information by analyzing the user's past behavioral history and social media posts. For example, the collection unit analyzes the user's past event participation history and social media posts to understand the user's interests and concerns. The analysis unit analyzes the information collected by the collection unit and suggests the most suitable exercise, lessons, or events for the user. For example, the analysis unit suggests the most suitable exercise, lessons, or events for the user based on the collected information. For example, if a user enjoys sports, the analysis unit can suggest events or lessons related to that sport. The analysis unit can also suggest activities at the optimal time based on the user's schedule. For instance, it can analyze the user's schedule and suggest exercise, lessons, or events at the most suitable time. Furthermore, based on the collected information, the analysis unit can provide customized suggestions tailored to the user's personality, preferences, hobbies, and schedule. For example, it can suggest individually customized exercise, lessons, or events based on the user's personality, preferences, hobbies, and schedule. The suggestion unit proposes optimal activities based on the results analyzed by the analysis unit. For example, the suggestion unit can suggest optimal activities for individual users based on specific rankings and activity results. For instance, it can suggest optimal activities in a ranking format based on past activity results and evaluations from other users. The suggestion unit can also analyze and generate activity requests directly from conversations with users and suggest new activities in real time based on activity data from people worldwide.For example, if a user requests to "find a new hobby," the suggestion unit can analyze the request and suggest a new hobby based on activity data from people around the world. This allows the activity suggestion system according to the embodiment to suggest the most suitable activity based on the user's personality, preferences, hobbies, and schedule.

[0030] The data collection unit collects information about users' personalities, preferences, hobbies, and schedules. Specifically, it collects detailed information such as what activities users enjoy, what hobbies they have, and what their daily schedules are like. For example, if a user enjoys sports, it collects information such as the type of sport, frequency, and time of day. The data collection unit can also conduct psychological tests and questionnaires to understand users' personalities, preferences, hobbies, and schedules. For example, it can conduct online questionnaires for users to collect information about their personalities, preferences, hobbies, and schedules. Furthermore, the data collection unit can also collect detailed information by analyzing users' past behavioral history and social media posts. For example, it can analyze users' past event participation history and social media posts to understand their interests and concerns. This allows the data collection unit to comprehensively collect multifaceted information about users and gain a detailed understanding of their personalities, preferences, hobbies, and schedules. The data collection unit also has the ability to automatically acquire data from users' devices and applications. For example, it can automatically collect users' location information and activity history through smartphone applications. This makes it possible to collect accurate data while minimizing the effort required from the user. Furthermore, the data collection unit has mechanisms in place to protect user privacy by anonymizing and encrypting data, and to manage the data securely. This allows users to use the system with peace of mind.

[0031] The analysis unit analyzes the information collected by the data collection unit and proposes the most suitable exercise, lessons, and events to the user. Specifically, based on the collected information, it proposes the most suitable exercise, lessons, and events to the user. For example, if the user likes sports, the analysis unit will propose events and lessons related to that sport. The analysis unit can also propose activities at the most suitable time according to the user's schedule. For example, the analysis unit will analyze the user's schedule and propose exercise, lessons, and events at the most suitable time. Furthermore, based on the collected information, the analysis unit can also make customized suggestions tailored to the user's personality, preferences, hobbies, and schedule. For example, the analysis unit will propose individually customized exercise, lessons, and events based on the user's personality, preferences, hobbies, and schedule. The analysis unit utilizes AI to perform advanced analysis of the collected data. For example, it uses natural language processing technology to analyze the content of the user's social media posts and understand the user's interests. It also uses machine learning algorithms to analyze the user's past behavior history and predict future interests. This allows the analysis unit to provide users with more accurate suggestions. Furthermore, the analysis unit can analyze data in real time and quickly provide suggestions tailored to the user's situation. For example, if a user's schedule changes, the analysis unit can immediately provide suggestions based on the new schedule. This allows the analysis unit to flexibly respond to user needs and provide optimal suggestions.

[0032] The suggestion department proposes optimal activities based on the results analyzed by the analysis department. Specifically, the suggestion department proposes the most suitable activities for individual users based on specific rankings and activity result information. For example, the suggestion department can propose optimal activities in a ranked format based on past activity results and evaluations from other users. Furthermore, the suggestion department can analyze and generate activity requests directly from conversations with users and propose new activities in real time based on activity data from people worldwide. For example, if a user requests to "find a new hobby," the suggestion department can analyze the request and propose a new hobby based on activity data from people worldwide. The suggestion department displays the proposed content clearly to the user through the user interface. For example, detailed information, evaluations, and reviews of proposed activities can be displayed through smartphone applications or websites. This allows users to make informed decisions about participating in proposed activities. In addition, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, after a user participates in a proposed activity, the suggestion department can collect evaluations and feedback on that activity and incorporate them into future suggestions. This allows the suggestion department to always provide users with the most suitable suggestions. Furthermore, the proposal department has a system in place to securely manage proposal content and feedback data in order to protect user privacy. This allows users to use the system with peace of mind.

[0033] The suggestion unit can propose activities best suited to individual users based on specific rankings and activity results. For example, the suggestion unit can propose optimal activities in a ranking format based on past activity results and evaluations from other users. For example, the suggestion unit can propose the most suitable exercise, lessons, or events to a user based on popularity rankings. The suggestion unit can also propose the most suitable activities to a user based on evaluation rankings. Furthermore, the suggestion unit can propose the most suitable activities to a user based on activity results information. For example, the suggestion unit can analyze past activity results and propose the most suitable exercise, lessons, or events to a user. This allows the suggestion unit to propose the most suitable activities to a user based on specific rankings and activity results information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can propose optimal activities using an AI model that takes past activity results and evaluations from other users as input and outputs the most suitable activities.

[0034] The suggestion unit can analyze and generate activity requests directly from conversations with users and propose new activities in real time based on activity data of people around the world. For example, if a user requests to "find a new hobby," the suggestion unit can analyze that request and propose a new hobby based on activity data of people around the world. The suggestion unit can analyze requests obtained from conversations with users and propose the most suitable exercise, lessons, or events for the user. Furthermore, the suggestion unit can propose new activities in real time based on activity data of people around the world. For example, the suggestion unit can analyze social media posts and event participation history to propose the most suitable activities for the user. This allows the suggestion unit to analyze and generate activity requests directly from conversations with users and propose new activities in real time. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can take conversation data with users as input and propose new activities using an AI model that analyzes and generates activity requests.

[0035] The data collection unit can collect detailed information about the user's personality, preferences, hobbies, and schedule. For example, the data collection unit can analyze the user's past behavioral history and social media posts to collect detailed information. For example, the data collection unit can analyze the user's past event participation history and social media posts to understand the user's interests and concerns. The data collection unit can also conduct online surveys with users to collect information about their personality, preferences, hobbies, and schedule. For example, the data collection unit can conduct psychological tests and surveys with users to collect information about their personality, preferences, hobbies, and schedule. This allows for the collection of detailed information about the user's personality, preferences, hobbies, and schedule. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media posts into a generating AI and have the generating AI generate information to understand the user's interests and concerns.

[0036] The analysis unit can suggest the most suitable exercise, lessons, or events for the user based on the collected information. For example, the analysis unit can suggest the most suitable exercise, lessons, or events for the user based on the collected information. For example, if the user likes sports, the analysis unit can suggest events or lessons related to that sport. The analysis unit can also suggest activities at the most suitable time according to the user's schedule. For example, the analysis unit can analyze the user's schedule and suggest exercise, lessons, or events at the most suitable time. Furthermore, the analysis unit can make customized suggestions based on the collected information, tailored to the user's personality, preferences, hobbies, and schedule. For example, the analysis unit can suggest individually customized exercise, lessons, or events based on the user's personality, preferences, hobbies, and schedule. This allows the analysis unit to suggest the most suitable exercise, lessons, or events for the user based on the collected information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can suggest the most suitable activities using an AI model that takes collected information as input and outputs the most suitable exercise, lessons, or events.

[0037] The suggestion unit can propose activities at the optimal time according to the user's schedule. For example, the suggestion unit can analyze the user's schedule and propose exercise, lessons, or events at the optimal time. For example, the suggestion unit can analyze the user's free time and propose activities that are best suited to that time slot. Furthermore, the suggestion unit can propose activities at the optimal time according to the user's lifestyle. For example, the suggestion unit can analyze the user's lifestyle and propose exercise, lessons, or events at the optimal time. This allows the suggestion unit to propose activities at the optimal time according to the user's schedule. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can take the user's schedule data as input and propose optimal activities using an AI model that proposes activities at the optimal time.

[0038] The data collection unit can analyze the user's past activity history and select the optimal information collection method. For example, the data collection unit can prioritize collecting information on similar events based on the user's past event participation history. For example, the data collection unit can analyze the types of exercise the user has preferred in the past and collect information related to those exercises. The data collection unit can also exclude activities the user has avoided in the past and collect information on new activities that might interest them. In this way, the optimal information collection method can be selected by analyzing the user's past activity history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past activity history data into a generating AI and have the generating AI perform analysis to select the optimal information collection method.

[0039] The data collection unit can filter information based on the user's current lifestyle and areas of interest. For example, if the user is currently busy, the data collection unit will prioritize collecting information on activities that can be done in a short amount of time. If the user is looking for a new hobby, the data collection unit can collect information related to that hobby. Also, if the user is concerned about their health, the data collection unit can collect information on activities that are good for their health. By filtering information based on the user's current lifestyle and areas of interest, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current lifestyle data into a generating AI and have the generating AI perform the information filtering.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit can prioritize the collection of event information held near the user's current location. For example, if the user is traveling, the data collection unit can prioritize the collection of activity information at their travel destination. Furthermore, if the user is at home, the data collection unit can prioritize the collection of activity information available around their home. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform analysis to prioritize the collection of highly relevant information.

[0041] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect event information that the user has shown interest in on social media. For example, the data collection unit can collect activity information from accounts that the user follows. The data collection unit can also collect activity information from social media groups that the user has joined. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform analysis to collect relevant information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on important information. For example, the analysis unit can perform a concise analysis on general information. Furthermore, the analysis unit can also perform a detailed analysis on information of high interest to the user. By adjusting the level of detail of the analysis based on the importance of the collected information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input importance data of the collected information into a generating AI and have the generating AI perform an analysis to adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a sports-specific analysis algorithm to sports-related information. For example, it can apply an event-specific analysis algorithm to event-related information. Furthermore, the analysis unit can apply an analysis algorithm specifically for extracurricular activity-related information. By applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI perform analysis to apply different analysis algorithms.

[0044] The analysis unit can determine the priority of analysis based on the timing of information submission during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent information. For example, the analysis unit may prioritize the analysis of information submitted within a time frame specified by the user. The analysis unit can also adjust the analysis priority according to the user's schedule. This allows for the provision of more appropriate analysis results by determining the analysis priority based on the timing of information submission. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input information submission timing data into a generating AI and have the generating AI perform an analysis to determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit may prioritize the analysis of information of high interest to the user. For example, the analysis unit may prioritize the analysis of information that is highly relevant based on the user's past activity history. Furthermore, the analysis unit may prioritize the analysis of information that is highly relevant based on the user's current living situation. By adjusting the order of analysis based on the relevance of the information, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input information relevance data into a generating AI and have the generating AI perform analysis to adjust the order of analysis.

[0046] The proposal unit can adjust the level of detail of its proposals based on the importance of the activity. For example, it can provide detailed proposals for important activities, and concise proposals for general activities. It can also provide detailed proposals for activities of high user interest. By adjusting the level of detail of proposals based on the importance of the activity, it can provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input activity importance data into a generating AI and have the generating AI perform analysis to adjust the level of detail of the proposals.

[0047] The proposal unit can apply different proposal algorithms depending on the activity category when making a proposal. For example, the proposal unit can apply a sports-specific proposal algorithm to sports-related activities. For example, it can apply an event-specific proposal algorithm to event-related activities. Furthermore, it can apply a lesson-specific proposal algorithm to lesson-related activities. By applying different proposal algorithms depending on the activity category, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input activity category data into a generating AI and have the generating AI perform analysis to apply different proposal algorithms.

[0048] The proposal department can determine the priority of proposals based on the submission timing of the activities. For example, the proposal department may prioritize the most recent activities. For example, the proposal department may prioritize activities submitted within a time frame specified by the user. The proposal department can also adjust the priority of proposals to match the user's schedule. This allows for the provision of more appropriate proposals by prioritizing proposals based on the submission timing of activities. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department may input activity submission timing data into a generating AI and have the generating AI perform analysis to determine the priority of proposals.

[0049] The suggestion unit can adjust the order of suggestions based on the relevance of the activities when making suggestions. For example, the suggestion unit can prioritize suggesting activities that are of high interest to the user. For example, the suggestion unit can prioritize suggesting activities that are highly relevant based on the user's past activity history. Furthermore, the suggestion unit can prioritize suggesting activities that are highly relevant based on the user's current living situation. By adjusting the order of suggestions based on the relevance of the activities, it is possible to provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input activity relevance data into a generating AI and have the generating AI perform analysis to adjust the order of suggestions.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The activity suggestion system can also collect user health data and incorporate it into its suggestions. For example, the data collection unit collects health data such as the user's heart rate, sleep patterns, and exercise level. Based on this data, the analysis unit can suggest exercises, lessons, and events that are best suited to the user's health condition. For example, if the user's heart rate is high, the suggestion unit can suggest relaxing yoga or meditation classes. It can also suggest activities that help improve sleep if the user's sleep patterns are disrupted. This allows the system to suggest more appropriate activities based on the user's health condition.

[0052] The activity suggestion system can further optimize the locations of suggested activities by utilizing the user's geographical location information. For example, the data collection unit collects information about the user's current location and frequently visited places. Based on this information, the analysis unit can suggest activities that can be performed within the user's range of movement. The suggestion unit can, for example, suggest fitness classes or events that the user can take near their home. If the user is traveling, it can also suggest tourist spots and activities at their travel destination. This allows for the suggestion of more convenient and appropriate activities based on the user's geographical location information.

[0053] The activity suggestion system can further utilize the user's social network information to suggest activities that can be enjoyed with friends and family. For example, the data collection unit collects information about friends and family from the user's social media accounts. Based on this information, the analysis unit can suggest activities that the user and their friends and family share common interests in. The suggestion unit can suggest, for example, sports events or workshops that the user and their friends can participate in together. It can also suggest outdoor activities that the whole family can enjoy. In this way, more enjoyable activities can be suggested based on the user's social network information.

[0054] The activity suggestion system can further analyze the user's past activity history to improve the accuracy of its suggestions. For example, the data collection unit collects the user's past participation history in events and lessons. The analysis unit can analyze the user's preferred activity trends based on this history. The suggestion unit can, for example, suggest new activities similar to those the user has given high ratings to in the past. It can also eliminate activities the user has avoided in the past and suggest new activities that might interest them. This allows the system to suggest more appropriate activities based on the user's past activity history.

[0055] The activity suggestion system can further analyze the user's lifestyle and optimize the time of day for suggested activities. For example, the data collection unit collects the user's sleep patterns and daily activity times. Based on this data, the analysis unit can identify the time of day when the user is most active. The suggestion unit, for example, suggests activities that can be done in the morning if the user is a morning person. It can also suggest activities that can be done in the evening if the user is a night owl. This allows the system to suggest activities at more appropriate times based on the user's lifestyle.

[0056] The activity suggestion system can further explore the user's hobbies and interests and suggest new activities. For example, the data collection unit gathers detailed information about the user's hobbies and interests. Based on this information, the analysis unit can identify activities that the user hasn't tried yet but might be interested in. The suggestion unit, for example, can suggest a new instrument lesson if the user is interested in music. It can also suggest new hiking trails or campsites if the user enjoys outdoor activities. In this way, the system can further explore the user's hobbies and interests and suggest new activities.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit collects information about the user's personality, preferences, hobbies, and schedule. For example, the data collection unit collects detailed information such as what activities the user likes, what hobbies they have, and what their daily schedule is like. Furthermore, the data collection unit can also conduct psychological tests and questionnaires to collect information about the user's personality, preferences, hobbies, and schedule. They can also collect detailed information by analyzing the user's past behavioral history and social media posts. Step 2: The analysis unit analyzes the information collected by the collection unit and suggests the most suitable exercise, lessons, and events for the user. For example, based on the collected information, if the user likes sports, it will suggest events and lessons related to that sport. It can also suggest activities at the most suitable time according to the user's schedule. Furthermore, it can provide customized suggestions tailored to the user's personality, preferences, hobbies, and schedule. Step 3: The proposal unit proposes the optimal activities based on the results analyzed by the analysis unit. For example, it proposes the most suitable activities for individual users based on specific rankings and activity results. Furthermore, it can analyze and generate activity requests directly from conversations with users and propose new activities in real time based on activity data from people around the world.

[0059] (Example of form 2) The activity suggestion system according to an embodiment of the present invention is a system that suggests optimized exercise, lessons, and events based on the user's personality, preferences, hobbies, schedule, etc. The activity suggestion system collects information such as the user's personality, preferences, hobbies, and schedule, analyzes the collected information, and suggests the most suitable exercise, lessons, and events for the user. Furthermore, it suggests activities that are best suited to each individual user based on specific rankings and activity results. It analyzes and generates activity requests directly from conversations with the user and suggests new activities in real time based on activity data of people around the world. This mechanism provides an environment in which users can spend their time more meaningfully and lead fulfilling lives. For example, the activity suggestion system collects information such as the user's personality, preferences, hobbies, and schedule. At this time, it collects detailed information such as what kind of activities the user likes, what kind of hobbies they have, and what kind of schedule they live on. For example, if the user likes sports, it collects information such as the type of sport, frequency, and time of day. This makes it possible to understand the user's personality, preferences, hobbies, and schedule. Next, the collected information is analyzed by AI. Based on the collected information, the AI ​​suggests the most suitable exercise, lessons, and events for the user. For example, if a user enjoys sports, the system will suggest events and classes related to that sport. It can also suggest activities at the most suitable times based on the user's schedule. This allows users to find activities that suit them. Furthermore, it suggests activities best suited to each individual user based on specific rankings and activity results. For instance, it can suggest optimal activities in a ranked format based on past activity results and evaluations from other users. This makes it easier for users to choose activities that suit them. Additionally, it can analyze and generate activity requests directly from conversations with users and suggest new activities in real time based on activity data from people worldwide. For example, if a user requests to "find a new hobby," the AI ​​can analyze that request and suggest new hobbies based on activity data from people around the world. This ensures users are always discovering new activities.This system provides users with an environment where they can spend their time more meaningfully and lead fulfilling lives. Users can find activities that suit them and enjoy them in accordance with their lifestyle schedule. For example, it can meet the needs of people who are working from home or have more free time and want to use their time meaningfully. It also solves the problem of difficulty in making the best choice among the increasing number of activity options. As a result, users can spend their time more meaningfully and lead fulfilling lives. The activity suggestion system can suggest the most suitable activities based on the user's personality, preferences, hobbies, and schedule.

[0060] The activity suggestion system according to the embodiment comprises a collection unit, an analysis unit, and a suggestion unit. The collection unit collects information such as the user's personality, preferences, hobbies, and schedule. For example, the collection unit collects detailed information such as what kind of activities the user likes, what hobbies they have, and what kind of schedule they live. For example, if the user likes sports, the collection unit collects information such as the type of sport, frequency, and time of day. The collection unit can also conduct psychological tests and questionnaires to understand the user's personality, preferences, hobbies, and schedule. For example, the collection unit conducts an online questionnaire for the user to collect information about their personality, preferences, hobbies, and schedule. Furthermore, the collection unit can also collect detailed information by analyzing the user's past behavioral history and social media posts. For example, the collection unit analyzes the user's past event participation history and social media posts to understand the user's interests and concerns. The analysis unit analyzes the information collected by the collection unit and suggests the most suitable exercise, lessons, or events for the user. For example, the analysis unit suggests the most suitable exercise, lessons, or events for the user based on the collected information. For example, if a user enjoys sports, the analysis unit can suggest events or lessons related to that sport. The analysis unit can also suggest activities at the optimal time based on the user's schedule. For instance, it can analyze the user's schedule and suggest exercise, lessons, or events at the most suitable time. Furthermore, based on the collected information, the analysis unit can provide customized suggestions tailored to the user's personality, preferences, hobbies, and schedule. For example, it can suggest individually customized exercise, lessons, or events based on the user's personality, preferences, hobbies, and schedule. The suggestion unit proposes optimal activities based on the results analyzed by the analysis unit. For example, the suggestion unit can suggest optimal activities for individual users based on specific rankings and activity results. For instance, it can suggest optimal activities in a ranking format based on past activity results and evaluations from other users. The suggestion unit can also analyze and generate activity requests directly from conversations with users and suggest new activities in real time based on activity data from people worldwide.For example, if a user requests to "find a new hobby," the suggestion unit can analyze the request and suggest a new hobby based on activity data from people around the world. This allows the activity suggestion system according to the embodiment to suggest the most suitable activity based on the user's personality, preferences, hobbies, and schedule.

[0061] The data collection unit collects information about users' personalities, preferences, hobbies, and schedules. Specifically, it collects detailed information such as what activities users enjoy, what hobbies they have, and what their daily schedules are like. For example, if a user enjoys sports, it collects information such as the type of sport, frequency, and time of day. The data collection unit can also conduct psychological tests and questionnaires to understand users' personalities, preferences, hobbies, and schedules. For example, it can conduct online questionnaires for users to collect information about their personalities, preferences, hobbies, and schedules. Furthermore, the data collection unit can also collect detailed information by analyzing users' past behavioral history and social media posts. For example, it can analyze users' past event participation history and social media posts to understand their interests and concerns. This allows the data collection unit to comprehensively collect multifaceted information about users and gain a detailed understanding of their personalities, preferences, hobbies, and schedules. The data collection unit also has the ability to automatically acquire data from users' devices and applications. For example, it can automatically collect users' location information and activity history through smartphone applications. This makes it possible to collect accurate data while minimizing the effort required from the user. Furthermore, the data collection unit has mechanisms in place to protect user privacy by anonymizing and encrypting data, and to manage the data securely. This allows users to use the system with peace of mind.

[0062] The analysis unit analyzes the information collected by the data collection unit and proposes the most suitable exercise, lessons, and events to the user. Specifically, based on the collected information, it proposes the most suitable exercise, lessons, and events to the user. For example, if the user likes sports, the analysis unit will propose events and lessons related to that sport. The analysis unit can also propose activities at the most suitable time according to the user's schedule. For example, the analysis unit will analyze the user's schedule and propose exercise, lessons, and events at the most suitable time. Furthermore, based on the collected information, the analysis unit can also make customized suggestions tailored to the user's personality, preferences, hobbies, and schedule. For example, the analysis unit will propose individually customized exercise, lessons, and events based on the user's personality, preferences, hobbies, and schedule. The analysis unit utilizes AI to perform advanced analysis of the collected data. For example, it uses natural language processing technology to analyze the content of the user's social media posts and understand the user's interests. It also uses machine learning algorithms to analyze the user's past behavior history and predict future interests. This allows the analysis unit to provide users with more accurate suggestions. Furthermore, the analysis unit can analyze data in real time and quickly provide suggestions tailored to the user's situation. For example, if a user's schedule changes, the analysis unit can immediately provide suggestions based on the new schedule. This allows the analysis unit to flexibly respond to user needs and provide optimal suggestions.

[0063] The suggestion department proposes optimal activities based on the results analyzed by the analysis department. Specifically, the suggestion department proposes the most suitable activities for individual users based on specific rankings and activity result information. For example, the suggestion department can propose optimal activities in a ranked format based on past activity results and evaluations from other users. Furthermore, the suggestion department can analyze and generate activity requests directly from conversations with users and propose new activities in real time based on activity data from people worldwide. For example, if a user requests to "find a new hobby," the suggestion department can analyze the request and propose a new hobby based on activity data from people worldwide. The suggestion department displays the proposed content clearly to the user through the user interface. For example, detailed information, evaluations, and reviews of proposed activities can be displayed through smartphone applications or websites. This allows users to make informed decisions about participating in proposed activities. In addition, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, after a user participates in a proposed activity, the suggestion department can collect evaluations and feedback on that activity and incorporate them into future suggestions. This allows the suggestion department to always provide users with the most suitable suggestions. Furthermore, the proposal department has a system in place to securely manage proposal content and feedback data in order to protect user privacy. This allows users to use the system with peace of mind.

[0064] The suggestion unit can propose activities best suited to individual users based on specific rankings and activity results. For example, the suggestion unit can propose optimal activities in a ranking format based on past activity results and evaluations from other users. For example, the suggestion unit can propose the most suitable exercise, lessons, or events to a user based on popularity rankings. The suggestion unit can also propose the most suitable activities to a user based on evaluation rankings. Furthermore, the suggestion unit can propose the most suitable activities to a user based on activity results information. For example, the suggestion unit can analyze past activity results and propose the most suitable exercise, lessons, or events to a user. This allows the suggestion unit to propose the most suitable activities to a user based on specific rankings and activity results information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can propose optimal activities using an AI model that takes past activity results and evaluations from other users as input and outputs the most suitable activities.

[0065] The suggestion unit can analyze and generate activity requests directly from conversations with users and propose new activities in real time based on activity data of people around the world. For example, if a user requests to "find a new hobby," the suggestion unit can analyze that request and propose a new hobby based on activity data of people around the world. The suggestion unit can analyze requests obtained from conversations with users and propose the most suitable exercise, lessons, or events for the user. Furthermore, the suggestion unit can propose new activities in real time based on activity data of people around the world. For example, the suggestion unit can analyze social media posts and event participation history to propose the most suitable activities for the user. This allows the suggestion unit to analyze and generate activity requests directly from conversations with users and propose new activities in real time. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can take conversation data with users as input and propose new activities using an AI model that analyzes and generates activity requests.

[0066] The data collection unit can collect detailed information about the user's personality, preferences, hobbies, and schedule. For example, the data collection unit can analyze the user's past behavioral history and social media posts to collect detailed information. For example, the data collection unit can analyze the user's past event participation history and social media posts to understand the user's interests and concerns. The data collection unit can also conduct online surveys with users to collect information about their personality, preferences, hobbies, and schedule. For example, the data collection unit can conduct psychological tests and surveys with users to collect information about their personality, preferences, hobbies, and schedule. This allows for the collection of detailed information about the user's personality, preferences, hobbies, and schedule. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media posts into a generating AI and have the generating AI generate information to understand the user's interests and concerns.

[0067] The analysis unit can suggest the most suitable exercise, lessons, or events for the user based on the collected information. For example, the analysis unit can suggest the most suitable exercise, lessons, or events for the user based on the collected information. For example, if the user likes sports, the analysis unit can suggest events or lessons related to that sport. The analysis unit can also suggest activities at the most suitable time according to the user's schedule. For example, the analysis unit can analyze the user's schedule and suggest exercise, lessons, or events at the most suitable time. Furthermore, the analysis unit can make customized suggestions based on the collected information, tailored to the user's personality, preferences, hobbies, and schedule. For example, the analysis unit can suggest individually customized exercise, lessons, or events based on the user's personality, preferences, hobbies, and schedule. This allows the analysis unit to suggest the most suitable exercise, lessons, or events for the user based on the collected information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can suggest the most suitable activities using an AI model that takes collected information as input and outputs the most suitable exercise, lessons, or events.

[0068] The suggestion unit can propose activities at the optimal time according to the user's schedule. For example, the suggestion unit can analyze the user's schedule and propose exercise, lessons, or events at the optimal time. For example, the suggestion unit can analyze the user's free time and propose activities that are best suited to that time slot. Furthermore, the suggestion unit can propose activities at the optimal time according to the user's lifestyle. For example, the suggestion unit can analyze the user's lifestyle and propose exercise, lessons, or events at the optimal time. This allows the suggestion unit to propose activities at the optimal time according to the user's schedule. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can take the user's schedule data as input and propose optimal activities using an AI model that proposes activities at the optimal time.

[0069] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect information during a relaxed time. If the user is excited, the data collection unit can start collecting information immediately and provide suggestions in real time. The data collection unit can also adjust the schedule to collect information after the user has rested if the user is tired. By adjusting the timing of information collection based on the user's emotions, more appropriate information collection becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0070] The data collection unit can analyze the user's past activity history and select the optimal information collection method. For example, the data collection unit can prioritize collecting information on similar events based on the user's past event participation history. For example, the data collection unit can analyze the types of exercise the user has preferred in the past and collect information related to those exercises. The data collection unit can also exclude activities the user has avoided in the past and collect information on new activities that might interest them. In this way, the optimal information collection method can be selected by analyzing the user's past activity history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past activity history data into a generating AI and have the generating AI perform analysis to select the optimal information collection method.

[0071] The data collection unit can filter information based on the user's current lifestyle and areas of interest. For example, if the user is currently busy, the data collection unit will prioritize collecting information on activities that can be done in a short amount of time. If the user is looking for a new hobby, the data collection unit can collect information related to that hobby. Also, if the user is concerned about their health, the data collection unit can collect information on activities that are good for their health. By filtering information based on the user's current lifestyle and areas of interest, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current lifestyle data into a generating AI and have the generating AI perform the information filtering.

[0072] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting information on relaxing activities. For example, if the user is excited, the data collection unit may prioritize collecting information on active activities. Also, if the user is tired, the data collection unit may prioritize collecting information on refreshing activities. By prioritizing information based on the user's emotions, more appropriate information can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0073] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit can prioritize the collection of event information held near the user's current location. For example, if the user is traveling, the data collection unit can prioritize the collection of activity information at their travel destination. Furthermore, if the user is at home, the data collection unit can prioritize the collection of activity information available around their home. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform analysis to prioritize the collection of highly relevant information.

[0074] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect event information that the user has shown interest in on social media. For example, the data collection unit can collect activity information from accounts that the user follows. The data collection unit can also collect activity information from social media groups that the user has joined. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform analysis to collect relevant information.

[0075] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, for example, the analysis unit can provide concise analysis results. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0076] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on important information. For example, the analysis unit can perform a concise analysis on general information. Furthermore, the analysis unit can also perform a detailed analysis on information of high interest to the user. By adjusting the level of detail of the analysis based on the importance of the collected information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input importance data of the collected information into a generating AI and have the generating AI perform an analysis to adjust the level of detail of the analysis.

[0077] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a sports-specific analysis algorithm to sports-related information. For example, it can apply an event-specific analysis algorithm to event-related information. Furthermore, the analysis unit can apply an analysis algorithm specifically for extracurricular activity-related information. By applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI perform analysis to apply different analysis algorithms.

[0078] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0079] The analysis unit can determine the priority of analysis based on the timing of information submission during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent information. For example, the analysis unit may prioritize the analysis of information submitted within a time frame specified by the user. The analysis unit can also adjust the analysis priority according to the user's schedule. This allows for the provision of more appropriate analysis results by determining the analysis priority based on the timing of information submission. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input information submission timing data into a generating AI and have the generating AI perform an analysis to determine the analysis priority.

[0080] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit may prioritize the analysis of information of high interest to the user. For example, the analysis unit may prioritize the analysis of information that is highly relevant based on the user's past activity history. Furthermore, the analysis unit may prioritize the analysis of information that is highly relevant based on the user's current living situation. By adjusting the order of analysis based on the relevance of the information, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input information relevance data into a generating AI and have the generating AI perform analysis to adjust the order of analysis.

[0081] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. Furthermore, if the user is excited, the suggestion unit can provide visually appealing suggestions. By adjusting the presentation of suggestions based on the user's emotions, it can provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0082] The proposal unit can adjust the level of detail of its proposals based on the importance of the activity. For example, it can provide detailed proposals for important activities, and concise proposals for general activities. It can also provide detailed proposals for activities of high user interest. By adjusting the level of detail of proposals based on the importance of the activity, it can provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input activity importance data into a generating AI and have the generating AI perform analysis to adjust the level of detail of the proposals.

[0083] The proposal unit can apply different proposal algorithms depending on the activity category when making a proposal. For example, the proposal unit can apply a sports-specific proposal algorithm to sports-related activities. For example, it can apply an event-specific proposal algorithm to event-related activities. Furthermore, it can apply a lesson-specific proposal algorithm to lesson-related activities. By applying different proposal algorithms depending on the activity category, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input activity category data into a generating AI and have the generating AI perform analysis to apply different proposal algorithms.

[0084] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is excited, the suggestion unit can provide visually appealing suggestions. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0085] The proposal department can determine the priority of proposals based on the submission timing of the activities. For example, the proposal department may prioritize the most recent activities. For example, the proposal department may prioritize activities submitted within a time frame specified by the user. The proposal department can also adjust the priority of proposals to match the user's schedule. This allows for the provision of more appropriate proposals by prioritizing proposals based on the submission timing of activities. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department may input activity submission timing data into a generating AI and have the generating AI perform analysis to determine the priority of proposals.

[0086] The suggestion unit can adjust the order of suggestions based on the relevance of the activities when making suggestions. For example, the suggestion unit can prioritize suggesting activities that are of high interest to the user. For example, the suggestion unit can prioritize suggesting activities that are highly relevant based on the user's past activity history. Furthermore, the suggestion unit can prioritize suggesting activities that are highly relevant based on the user's current living situation. By adjusting the order of suggestions based on the relevance of the activities, it is possible to provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input activity relevance data into a generating AI and have the generating AI perform analysis to adjust the order of suggestions.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The activity suggestion system can also collect user health data and incorporate it into its suggestions. For example, the data collection unit collects health data such as the user's heart rate, sleep patterns, and exercise level. Based on this data, the analysis unit can suggest exercises, lessons, and events that are best suited to the user's health condition. For example, if the user's heart rate is high, the suggestion unit can suggest relaxing yoga or meditation classes. It can also suggest activities that help improve sleep if the user's sleep patterns are disrupted. This allows the system to suggest more appropriate activities based on the user's health condition.

[0089] The activity suggestion system can further estimate the user's emotions and adjust the suggestions based on those emotions. For example, if the analysis unit is feeling stressed, it can suggest relaxing activities. If the suggestion unit is excited, it can suggest active sports or events. If the user is sad, it can suggest fun activities to lift their spirits. This allows the system to suggest more appropriate activities based on the user's emotions.

[0090] The activity suggestion system can further optimize the locations of suggested activities by utilizing the user's geographical location information. For example, the data collection unit collects information about the user's current location and frequently visited places. Based on this information, the analysis unit can suggest activities that can be performed within the user's range of movement. The suggestion unit can, for example, suggest fitness classes or events that the user can take near their home. If the user is traveling, it can also suggest tourist spots and activities at their travel destination. This allows for the suggestion of more convenient and appropriate activities based on the user's geographical location information.

[0091] The activity suggestion system can further utilize the user's social network information to suggest activities that can be enjoyed with friends and family. For example, the data collection unit collects information about friends and family from the user's social media accounts. Based on this information, the analysis unit can suggest activities that the user and their friends and family share common interests in. The suggestion unit can suggest, for example, sports events or workshops that the user and their friends can participate in together. It can also suggest outdoor activities that the whole family can enjoy. In this way, more enjoyable activities can be suggested based on the user's social network information.

[0092] The activity suggestion system can further analyze the user's past activity history to improve the accuracy of its suggestions. For example, the data collection unit collects the user's past participation history in events and lessons. The analysis unit can analyze the user's preferred activity trends based on this history. The suggestion unit can, for example, suggest new activities similar to those the user has given high ratings to in the past. It can also eliminate activities the user has avoided in the past and suggest new activities that might interest them. This allows the system to suggest more appropriate activities based on the user's past activity history.

[0093] The activity suggestion system can further estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, the data collection unit collects the user's facial expressions and voice data to estimate their emotions. The analysis unit can then make suggestions during times when the user is relaxed. The suggestion unit, for example, can suggest relaxing activities if the user is feeling stressed. It can also suggest active activities if the user is excited. This allows the system to suggest activities at a more appropriate time based on the user's emotions.

[0094] The activity suggestion system can further analyze the user's lifestyle and optimize the time of day for suggested activities. For example, the data collection unit collects the user's sleep patterns and daily activity times. Based on this data, the analysis unit can identify the time of day when the user is most active. The suggestion unit, for example, suggests activities that can be done in the morning if the user is a morning person. It can also suggest activities that can be done in the evening if the user is a night owl. This allows the system to suggest activities at more appropriate times based on the user's lifestyle.

[0095] The activity suggestion system can further estimate the user's emotions and personalize the suggestions based on those emotions. For example, the analysis unit can suggest relaxing activities if the user is relaxed. The suggestion unit can suggest activities that help relieve stress if the user is stressed. It can also suggest energetic activities if the user is excited. This allows for more personalized activity suggestions based on the user's emotions.

[0096] The activity suggestion system can further explore the user's hobbies and interests and suggest new activities. For example, the data collection unit gathers detailed information about the user's hobbies and interests. Based on this information, the analysis unit can identify activities that the user hasn't tried yet but might be interested in. The suggestion unit, for example, can suggest a new instrument lesson if the user is interested in music. It can also suggest new hiking trails or campsites if the user enjoys outdoor activities. In this way, the system can further explore the user's hobbies and interests and suggest new activities.

[0097] The activity suggestion system can further estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions. Furthermore, if the user is excited, it can provide visually appealing suggestions. This allows the system to suggest activities in a more appropriate way based on the user's emotions.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The data collection unit collects information about the user's personality, preferences, hobbies, and schedule. For example, the data collection unit collects detailed information such as what activities the user likes, what hobbies they have, and what their daily schedule is like. Furthermore, the data collection unit can also conduct psychological tests and questionnaires to collect information about the user's personality, preferences, hobbies, and schedule. They can also collect detailed information by analyzing the user's past behavioral history and social media posts. Step 2: The analysis unit analyzes the information collected by the collection unit and suggests the most suitable exercise, lessons, and events for the user. For example, based on the collected information, if the user likes sports, it will suggest events and lessons related to that sport. It can also suggest activities at the most suitable time according to the user's schedule. Furthermore, it can provide customized suggestions tailored to the user's personality, preferences, hobbies, and schedule. Step 3: The proposal unit proposes the optimal activities based on the results analyzed by the analysis unit. For example, it proposes the most suitable activities for individual users based on specific rankings and activity results. Furthermore, it can analyze and generate activity requests directly from conversations with users and propose new activities in real time based on activity data from people around the world.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect information such as the user's personality, preferences, hobbies, and schedule. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected information to suggest the most suitable exercise, lessons, or events for the user. The suggestion unit is implemented in the control unit 46A of the smart device 14, and based on the analysis results, suggests the most suitable activities based on specific rankings and activity result information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect information such as the user's personality, preferences, hobbies, and schedule. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, and analyzes the collected information to suggest the most suitable exercise, lessons, or events for the user. The suggestion unit is implemented in the control unit 46A of the smart glasses 214, and based on the analysis results, suggests the most suitable activities based on specific rankings and activity results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect information such as the user's personality, preferences, hobbies, and schedule. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the collected information to suggest the most suitable exercise, lessons, or events for the user. The suggestion unit is implemented in the control unit 46A of the headset terminal 314, which suggests the most suitable activities based on the analysis results and specific rankings and activity results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the robot 414 to collect information such as the user's personality, preferences, hobbies, and schedule. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected information to suggest the most suitable exercise, lessons, or events for the user. The suggestion unit is implemented in the control unit 46A of the robot 414, and based on the analysis results, suggests the most suitable activities based on specific rankings and activity result information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0153] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0162] 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.

[0163] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0165] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) A data collection unit that collects information such as the user's personality, preferences, hobbies, and schedule, The analysis unit analyzes the information collected by the aforementioned collection unit and proposes the most suitable exercise, lessons, and events for the user. The system includes a proposal unit that proposes the optimal activity based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Based on specific rankings and activity results, we propose activities best suited to each individual user. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, It analyzes and generates activity requests directly from conversations with users, and proposes new activities in real time based on activity data from people around the world. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect detailed information about the user, such as their personality, preferences, hobbies, and schedule. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Based on the collected information, we suggest the most suitable exercises, lessons, and events for the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We suggest activities at the optimal time to match the user's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past activity history and select the optimal method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the activity. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the activity category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on the submission timing of the activities. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the activities. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects information such as the user's personality, preferences, hobbies, and schedule, The analysis unit analyzes the information collected by the aforementioned collection unit and proposes the most suitable exercise, lessons, and events for the user. The system includes a proposal unit that proposes the optimal activity based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned proposal section is, Based on specific rankings and activity results, we propose activities best suited to each individual user. The system according to feature 1.

3. The aforementioned proposal section is, It analyzes and generates activity requests directly from conversations with users, and proposes new activities in real time based on activity data from people around the world. The system according to feature 1.

4. The aforementioned collection unit is Collect detailed information about the user, such as their personality, preferences, hobbies, and schedule. The system according to feature 1.

5. The aforementioned analysis unit, Based on the collected information, we suggest the most suitable exercises, lessons, and events for the user. The system according to feature 1.

6. The aforementioned proposal section is, We suggest activities at the optimal time to match the user's schedule. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past activity history and select the optimal method for collecting information. The system according to feature 1.

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