System

Abstract

An object of the system according to the embodiment is to propose an optimal course or spot based on the user's preference or past history according to the purpose of going out or going out.SOLUTION: A system includes a course proposal part, a recommendation part, an advertisement provision part, a proposal reception part, and a point management part. The course suggestion unit suggests an optimum course according to the purpose of going out or going out. The recommendation unit analyzes the user's preference and past history and recommends a recommended spot, gourmet food, and shopping information. The advertisement providing unit provides an advertisement or a coupon of a commercial facility. The proposal receiving unit receives a proposal or an introduction from the user. The point management unit manages points based on the information provided by the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately suggest optimal routes and spots based on a user's preferences and past history when planning an outing or trip, and there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal routes and spots based on the user's preferences and past history, depending on the purpose of the outing or trip. [Means for solving the problem]

[0006] The system according to the embodiment includes a course suggestion unit, a recommendation unit, an advertisement provision unit, a proposal reception unit, and a points management unit. The course suggestion unit proposes the optimal course depending on the purpose of the outing or trip. The recommendation unit analyzes the user's preferences and past history to recommend recommended spots, gourmet food, and shopping information. The advertisement provision unit provides advertisements and coupons for commercial facilities. The proposal reception unit receives proposals and introductions from the user. The points management unit manages points based on information provided by the user. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal routes and spots based on the user's preferences and past history, depending on the purpose of the outing or trip. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​system according to the embodiment of the present invention is a system that compiles and recommends optimal routes, recommended spots, gourmet food, shopping, and useful information according to the purpose of an outing or trip, thereby enabling the user to plan outings or trips more enjoyably and conveniently.

[0029] The AI ​​system according to the embodiment includes a course suggestion unit, a recommendation unit, an advertisement provision unit, a proposal acceptance unit, and a point management unit. The course suggestion unit proposes an optimal course based on the purpose of the outing or trip. For example, if a user requests a "one-day course that the whole family can enjoy," the generation AI proposes a course that combines a park, a zoo, a family restaurant, etc. The recommendation unit analyzes the user's preferences and past history to recommend recommended spots, gourmet food, and shopping information. For example, if a user inputs "I like Japanese food," the generation AI suggests nearby Japanese restaurants based on that information. The advertisement provision unit provides advertisements and coupons for commercial facilities. For example, when a user visits a specific commercial facility, the user can receive discount coupons and special offer information. The proposal acceptance unit allows the user to make suggestions and introductions. For example, by introducing recommended local spots and hidden gems, the system provides other users with useful information. The point management unit manages points based on information provided by the user. For example, when a user proposes a course or provides useful information, points are added, and bonus points are awarded based on feedback from other users. This allows the AI ​​system to optimally coordinate users' outings and trips, and evaluate their contributions through point management.

[0030] The course suggestion unit can learn the user's past behavioral history and preferences and propose individually customized courses. For example, the generation AI in the course suggestion unit analyzes the user's past behavioral history to learn frequently visited places and favorite spots. For example, the unit proposes the optimal course for the user's next outing based on data on restaurants and tourist spots the user has visited in the past. In addition, to learn the user's preferences, the generation AI analyzes the user's social media posts and ratings on review sites. For example, the unit proposes courses that match the user's preferences based on photos and comments the user has shared on social media. In addition, the generation AI in the course suggestion unit learns the user's behavioral patterns and proposes courses tailored to specific time periods and days of the week. For example, the unit proposes courses that can be enjoyed with the family on weekends or courses that allow users to relax after work on weekdays. This allows the unit to propose optimal courses that match the user's individual preferences.

[0031] The course suggestion unit can dynamically adjust the optimal course based on real-time traffic and weather information. For example, the generation AI of the course suggestion unit obtains real-time traffic information and dynamically adjusts the course to avoid congestion and traffic restrictions. For example, if congestion occurs, an alternative route is suggested. The course suggestion unit also uses weather information to suggest a course that suits the weather at the destination. For example, if it is raining, it will suggest a course centered on indoor facilities, and if it is sunny, it will suggest a course that includes outdoor activities. The course suggestion unit also monitors the operation status of public transportation in real time and suggests the optimal means of transportation if delays or cancellations occur. For example, it will suggest a route that uses a bus or taxi based on train delay information. This makes it possible to provide the optimal course based on real-time information.

[0032] The recommendation unit can recommend appropriate restaurants based on the user's dietary restrictions and allergy information. For example, the generation AI analyzes the user's dietary restriction information and recommends appropriate restaurants. For example, it may suggest restaurants that are suitable for vegetarians or vegans. The recommendation unit also takes into account the user's allergy information and recommends restaurants where the generation AI can safely dine. For example, it may suggest restaurants that offer nut-free menus for a user with a nut allergy. The recommendation unit also takes into account the user's health condition and recommends restaurants where the generation AI can serve healthy meals. For example, it may suggest restaurants that offer low-calorie or gluten-free menus. This makes it possible to provide appropriate restaurants based on the user's dietary restrictions and allergy information.

[0033] The recommendation unit can analyze a user's purchasing history and recommend shopping spots that suit their preferences. For example, the generation AI analyzes a user's purchasing history and suggests shopping spots that suit their preferences. For example, it may recommend related stores based on products or brands purchased in the past. The recommendation unit also uses the generation AI to suggest new shopping spots based on the user's purchasing history. For example, it may provide information on new stores and sales that the user may be interested in. The recommendation unit also uses the generation AI to learn the user's purchasing patterns and suggest shopping spots that suit specific seasons or events. For example, it may recommend gift shops that are recommended during the Christmas season. This makes it possible to provide the optimal shopping spots based on the user's purchasing history.

[0034] The advertisement providing unit can analyze a user's purchasing history and behavioral patterns and provide individually customized coupons. For example, the generation AI in the advertisement providing unit analyzes a user's purchasing history and provides individually customized coupons. For example, it provides discount coupons related to products or brands purchased in the past. Furthermore, the advertisement providing unit uses the generation AI to provide coupons tailored to specific time periods or days of the week based on the user's behavioral patterns. For example, it provides coupons for restaurants that can be used during weekday lunchtimes. Furthermore, the advertisement providing unit uses the generation AI to learn the user's purchasing history and provide coupons tailored to specific events or seasons. For example, it provides coupons for gift shops that can be used during the Christmas season. This makes it possible to provide optimal coupons based on the user's purchasing history and behavioral patterns.

[0035] The advertisement provision unit can grasp the congestion status of a commercial facility in real time and issue coupons to avoid congestion. For example, the advertisement provision unit uses a generation AI to monitor the congestion status of a commercial facility in real time and provide coupons to avoid congestion. For example, a discount coupon that can be used during less crowded times is provided. The advertisement provision unit also analyzes the congestion status of a commercial facility that a user plans to visit and provides coupons for alternative facilities to avoid congestion. For example, instead of a crowded shopping mall, it provides a coupon for a nearby store. The advertisement provision unit also uses a generation AI to predict congestion at a commercial facility and provides coupons to avoid times when congestion is expected. For example, a coupon that can be used on weekdays is provided to avoid weekend congestion. This makes it possible to provide optimal coupons to avoid congestion at commercial facilities.

[0036] The suggestion receiving unit can evaluate feedback from other users to evaluate the reliability of information provided by the user. In the suggestion receiving unit, for example, the generation AI analyzes feedback from other users and evaluates the reliability of the information provided by the user. For example, information with a lot of positive feedback is given a high rating. The suggestion receiving unit also scores the feedback on the information provided by the user and evaluates the reliability based on the score. For example, information with a high score is preferentially displayed. In the suggestion receiving unit, the generation AI analyzes the content of the feedback and extracts specific evaluation points. For example, reliability is evaluated based on feedback on useful or accurate information. This makes it possible to evaluate the reliability of the information provided by the user.

[0037] The proposal acceptance unit can automatically categorize information provided by users, making it easier to search. For example, the proposal acceptance unit has the generation AI analyze the information provided by users and automatically categorize it. For example, it may categorize it into categories such as tourist attractions, restaurants, shopping, etc. The proposal acceptance unit also has the generation AI extract related keywords based on the information provided by users, making it easier to search. For example, it may display related information when a specific keyword is searched. The proposal acceptance unit also has the generation AI learn from the information provided by users and improve the accuracy of categorization. For example, it may automatically select the most appropriate category based on past data. This makes it easier to search for the information provided by users.

[0038] The point management unit can evaluate the quality and usefulness of the information provided in order to evaluate the user's contribution in detail. For example, the point management unit analyzes the quality of the information provided by the generation AI and evaluates the contribution in detail. For example, it calculates a score based on the accuracy and detail of the information. The point management unit also evaluates the contribution of the generation AI based on the usefulness of the information provided by the user. For example, it assigns a high score to information that other users found useful. The point management unit also learns the quality and usefulness of the information provided by the generation AI and continuously improves the evaluation criteria. For example, it sets optimal evaluation criteria based on past data. This allows the user's contribution to be evaluated in detail.

[0039] The point management unit can automatically analyze the content of the feedback and provide specific areas for improvement and praise to the user. For example, the point management unit uses a generation AI to analyze feedback from other users and extract specific areas for improvement and praise. For example, the point management unit notifies the user of points that need improvement and excellent points based on the content of the feedback. The point management unit also automatically analyzes the content of the feedback and provides specific advice to the user. For example, it makes specific suggestions to increase the level of detail of the information. The point management unit also uses a generation AI to learn the content of the feedback and provide feedback to the user that emphasizes areas of praise. For example, it highlights points that have received high praise from other users. This allows the user to receive specific feedback on areas for improvement and praise.

[0040] The point management unit can compare a user's contribution with other users and display it in a ranking format. For example, the generation AI in the point management unit analyzes a user's contribution and displays it in a ranking format compared to other users. For example, it creates a ranking based on the quality of the information provided and the amount of feedback. The point management unit also builds a system that scores users' contribution and displays it in a ranking format. For example, it displays users with high contributions at the top. The point management unit also allows the generation AI to learn users' contributions and improve the accuracy of the rankings. For example, it sets optimal ranking criteria based on past data. This makes it possible to display users' contributions in a ranking format.

[0041] The point management unit can award additional points by theme when a user contributes on a specific theme or category. For example, the point management unit allows the generation AI to analyze the user's contribution and award bonus points when the user contributes on a specific theme or category. For example, bonus points are awarded for providing information about tourist attractions or restaurants. The point management unit also builds a system in which the generation AI awards bonus points by theme when a user contributes on a specific theme or category. For example, bonus points are awarded for providing information about seasonal events. The point management unit also allows the generation AI to learn the user's contributions and continuously improve the criteria for awarding bonus points by theme. For example, the point management unit sets optimal criteria for awarding bonus points based on past data. This makes it possible to award bonus points when the user contributes on a specific theme or category.

[0042] The point management unit can analyze point usage history and recommend the optimal usage method to the user. In the point management unit, for example, the generation AI analyzes the user's point usage history and suggests the optimal usage method. For example, it suggests the most effective way for the user to use points based on past usage patterns. In addition, the point management unit has the generation AI suggest new usage methods based on the user's point usage history. For example, it suggests ways for the user to use points for services or products that they have not yet used. In addition, the point management unit has the generation AI learn the user's point usage patterns and continuously suggest the optimal usage methods. For example, it suggests the optimal timing and method of use based on past data. This makes it possible to suggest the optimal way to use points to the user.

[0043] The point management unit can display the value of the Regional Token DAO in real time, allowing users to use it at the optimal time. For example, the point management unit allows the generation AI to display the value of the Regional Token DAO in real time, allowing users to use it at the optimal time. For example, it may suggest using it when the token value is high. The point management unit also builds a system in which the generation AI suggests the optimal time when users use the Regional Token DAO. For example, it may suggest using it when the token value is rising. The point management unit also allows the generation AI to learn the value of the Regional Token DAO and continuously suggest the optimal time to use it to users. For example, it may predict the optimal time to use it based on past data. This allows users to use the Regional Token DAO at the optimal time.

[0044] The point management unit can provide a marketplace where points can be exchanged with other users. For example, the point management unit builds a system that provides a marketplace where the generation AI can exchange points with other users. For example, it provides a platform where users can list points and other users can purchase them. In addition, when a user exchanges points with other users, the generation AI proposes the optimal exchange rate. For example, it sets the exchange rate based on supply and demand. In addition, the point management unit allows the generation AI to analyze point exchange history and propose the optimal exchange partner for the user. For example, it proposes a reliable exchange partner based on past transaction history. This allows users to exchange points with other users.

[0045] The point management unit can enable points to be used as tickets to participate in local events and activities. The point management unit, for example, builds a system in which the generation AI can use points as tickets to participate in local events and activities. For example, it makes it possible to purchase tickets for local festivals and workshops with points. Furthermore, when a user uses points to participate in a local event, the point management unit allows the generation AI to suggest the most suitable event. For example, it recommends events based on the user's interests and preferences. Furthermore, the point management unit allows the generation AI to analyze local event information and suggest the most suitable event participation method for the user. For example, it suggests a method of using points to participate in multiple events. This allows the user to use points as tickets to participate in local events and activities.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The course suggestion unit can also monitor the user's health condition and suggest health-conscious courses. For example, the generation AI analyzes the user's number of steps and heart rate to suggest courses that allow for moderate exercise. The course suggestion unit also suggests courses that take calorie consumption into consideration based on the user's health goals. For example, for a user who is on a diet, it will suggest courses that allow for walking or jogging. The course suggestion unit also suggests relaxing courses based on the user's health data. For example, it will suggest a nature walk course to reduce stress. This makes it possible to provide the optimal course according to the user's health condition.

[0048] The recommendation unit can also recommend spots along specific themes based on the user's hobbies and interests. For example, the generation AI analyzes the user's hobbies and suggests museums and galleries to a user who loves art. The recommendation unit also recommends specific events and exhibitions based on the user's interests. For example, it suggests concerts and live events to a user who loves music. The recommendation unit also learns the user's interests and suggests spots that will spark new hobbies and interests. For example, it suggests cooking classes and gourmet festivals to a user who loves cooking. This makes it possible to provide the best spots according to the user's hobbies and interests.

[0049] The ad serving unit can analyze a user's purchasing history and behavioral patterns to provide individually customized coupons. For example, the generation AI analyzes a user's purchasing history to provide individually customized coupons. For example, it provides discount coupons related to products or brands purchased in the past. The ad serving unit also provides coupons tailored to specific time periods or days of the week based on the user's behavioral patterns. For example, it provides coupons for restaurants that can be used during weekday lunchtimes. The ad serving unit also provides coupons tailored to specific events or seasons by having the generation AI learn the user's purchasing history. For example, it provides coupons for gift shops that can be used during the Christmas season. This makes it possible to provide optimal coupons based on the user's purchasing history and behavioral patterns.

[0050] The suggestion receiving unit can evaluate feedback from other users to evaluate the reliability of information provided by the user. For example, the generation AI analyzes feedback from other users and evaluates the reliability of the information provided by the user. For example, information with a lot of positive feedback is given a high rating. The suggestion receiving unit also scores the feedback on the information provided by the user and evaluates the reliability based on the score. For example, information with a high score is displayed preferentially. The suggestion receiving unit also analyzes the content of the feedback and extracts specific evaluation points. For example, reliability is evaluated based on feedback on useful or accurate information. This makes it possible to evaluate the reliability of the information provided by the user.

[0051] The point management unit can evaluate the quality and usefulness of the information provided in order to evaluate the user's contribution in detail. For example, the generation AI analyzes the quality of the information provided by the user and evaluates the contribution in detail. For example, it calculates a score based on the accuracy and detail of the information. The point management unit also evaluates the generation AI's contribution based on the usefulness of the information provided by the user. For example, it assigns a high score to information that other users found useful. The point management unit also allows the generation AI to learn the quality and usefulness of the information provided and continuously improve the evaluation criteria. For example, it sets optimal evaluation criteria based on past data. This allows the user's contribution to be evaluated in detail.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The course suggestion unit suggests the optimal course based on the purpose of the outing or trip. For example, if the user requests a "one-day course that the whole family can enjoy," the generation AI will suggest a course that combines parks, zoos, family restaurants, etc. Step 2: The recommendation section analyzes the user's preferences and past history to recommend recommended spots, gourmet food, and shopping information. For example, if a user inputs "I like Japanese food," the generation AI will use that information to suggest nearby Japanese restaurants. Step 3: The advertisement providing unit provides advertisements and coupons for commercial facilities. For example, when the user visits a specific commercial facility, the user can receive discount coupons and special offer information. Step 4: The suggestion receiving unit allows users to make suggestions and recommendations. For example, by introducing recommended local spots or hidden gems, users can provide other users with useful information. Step 5: The point management unit manages points based on the information provided by the user. For example, points are added when a user provides course suggestions or useful information, and bonus points are awarded based on feedback from other users.

[0054] (Example 2) The AI ​​system according to the embodiment of the present invention is a system that compiles and recommends optimal routes, recommended spots, gourmet food, shopping, and useful information according to the purpose of an outing or trip, thereby enabling the user to plan outings or trips more enjoyably and conveniently.

[0055] The AI ​​system according to the embodiment includes a course suggestion unit, a recommendation unit, an advertisement provision unit, a proposal acceptance unit, and a point management unit. The course suggestion unit proposes an optimal course based on the purpose of the outing or trip. For example, if a user requests a "one-day course that the whole family can enjoy," the generation AI proposes a course that combines a park, a zoo, a family restaurant, etc. The recommendation unit analyzes the user's preferences and past history to recommend recommended spots, gourmet food, and shopping information. For example, if a user inputs "I like Japanese food," the generation AI suggests nearby Japanese restaurants based on that information. The advertisement provision unit provides advertisements and coupons for commercial facilities. For example, when a user visits a specific commercial facility, the user can receive discount coupons and special offer information. The proposal acceptance unit allows the user to make suggestions and introductions. For example, by introducing recommended local spots and hidden gems, the system provides other users with useful information. The point management unit manages points based on information provided by the user. For example, when a user proposes a course or provides useful information, points are added, and bonus points are awarded based on feedback from other users. This allows the AI ​​system to optimally coordinate users' outings and trips, and evaluate their contributions through point management.

[0056] The course suggestion unit can learn the user's past behavioral history and preferences and propose individually customized courses. For example, the generation AI in the course suggestion unit analyzes the user's past behavioral history to learn frequently visited places and favorite spots. For example, the unit proposes the optimal course for the user's next outing based on data on restaurants and tourist spots the user has visited in the past. In addition, to learn the user's preferences, the generation AI analyzes the user's social media posts and ratings on review sites. For example, the unit proposes courses that match the user's preferences based on photos and comments the user has shared on social media. In addition, the generation AI in the course suggestion unit learns the user's behavioral patterns and proposes courses tailored to specific time periods and days of the week. For example, the unit proposes courses that can be enjoyed with the family on weekends or courses that allow users to relax after work on weekdays. This allows the unit to propose optimal courses that match the user's individual preferences.

[0057] The course suggestion unit can dynamically adjust the optimal course based on real-time traffic and weather information. For example, the generation AI of the course suggestion unit obtains real-time traffic information and dynamically adjusts the course to avoid congestion and traffic restrictions. For example, if congestion occurs, an alternative route is suggested. The course suggestion unit also uses weather information to suggest a course that suits the weather at the destination. For example, if it is raining, it will suggest a course centered on indoor facilities, and if it is sunny, it will suggest a course that includes outdoor activities. The course suggestion unit also monitors the operation status of public transportation in real time and suggests the optimal means of transportation if delays or cancellations occur. For example, it will suggest a route that uses a bus or taxi based on train delay information. This makes it possible to provide the optimal course based on real-time information.

[0058] The course suggestion unit can use the emotion estimation function to suggest a relaxing course or an active course based on the user's current emotional state. For example, the course suggestion unit uses the emotion estimation function to analyze the user's current emotional state and suggest a relaxing course. For example, for a user who is feeling stressed, the course suggestion unit suggests a course that includes quiet cafes and parks. The course suggestion unit also suggests an active course based on the user's emotional state. For example, for a user who is feeling energetic, the course suggestion unit suggests a course that includes sports facilities and activities. The course suggestion unit also uses the emotion estimation function to monitor the user's emotional state in real time and dynamically adjust the course if the user's emotions change while out. For example, if the user feels tired, the course suggestion unit adds a rest spot. This makes it possible to provide an optimal course according to the user's emotional state.

[0059] The recommendation unit can recommend appropriate restaurants based on the user's dietary restrictions and allergy information. For example, the generation AI analyzes the user's dietary restriction information and recommends appropriate restaurants. For example, it may suggest restaurants that are suitable for vegetarians or vegans. The recommendation unit also takes into account the user's allergy information and recommends restaurants where the generation AI can safely dine. For example, it may suggest restaurants that offer nut-free menus for a user with a nut allergy. The recommendation unit also takes into account the user's health condition and recommends restaurants where the generation AI can serve healthy meals. For example, it may suggest restaurants that offer low-calorie or gluten-free menus. This makes it possible to provide appropriate restaurants based on the user's dietary restrictions and allergy information.

[0060] The recommendation unit can analyze a user's purchasing history and recommend shopping spots that suit their preferences. For example, the generation AI analyzes a user's purchasing history and suggests shopping spots that suit their preferences. For example, it may recommend related stores based on products or brands purchased in the past. The recommendation unit also uses the generation AI to suggest new shopping spots based on the user's purchasing history. For example, it may provide information on new stores and sales that the user may be interested in. The recommendation unit also uses the generation AI to learn the user's purchasing patterns and suggest shopping spots that suit specific seasons or events. For example, it may recommend gift shops that are recommended during the Christmas season. This makes it possible to provide the optimal shopping spots based on the user's purchasing history.

[0061] The advertisement providing unit can analyze a user's purchasing history and behavioral patterns and provide individually customized coupons. For example, the generation AI in the advertisement providing unit analyzes a user's purchasing history and provides individually customized coupons. For example, it provides discount coupons related to products or brands purchased in the past. Furthermore, the advertisement providing unit uses the generation AI to provide coupons tailored to specific time periods or days of the week based on the user's behavioral patterns. For example, it provides coupons for restaurants that can be used during weekday lunchtimes. Furthermore, the advertisement providing unit uses the generation AI to learn the user's purchasing history and provide coupons tailored to specific events or seasons. For example, it provides coupons for gift shops that can be used during the Christmas season. This makes it possible to provide optimal coupons based on the user's purchasing history and behavioral patterns.

[0062] The advertisement provision unit can grasp the congestion status of a commercial facility in real time and issue coupons to avoid congestion. For example, the advertisement provision unit uses a generation AI to monitor the congestion status of a commercial facility in real time and provide coupons to avoid congestion. For example, a discount coupon that can be used during less crowded times is provided. The advertisement provision unit also analyzes the congestion status of a commercial facility that a user plans to visit and provides coupons for alternative facilities to avoid congestion. For example, instead of a crowded shopping mall, it provides a coupon for a nearby store. The advertisement provision unit also uses a generation AI to predict congestion at a commercial facility and provides coupons to avoid times when congestion is expected. For example, a coupon that can be used on weekdays is provided to avoid weekend congestion. This makes it possible to provide optimal coupons to avoid congestion at commercial facilities.

[0063] The suggestion receiving unit can evaluate feedback from other users to evaluate the reliability of information provided by the user. In the suggestion receiving unit, for example, the generation AI analyzes feedback from other users and evaluates the reliability of the information provided by the user. For example, information with a lot of positive feedback is given a high rating. The suggestion receiving unit also scores the feedback on the information provided by the user and evaluates the reliability based on the score. For example, information with a high score is preferentially displayed. In the suggestion receiving unit, the generation AI analyzes the content of the feedback and extracts specific evaluation points. For example, reliability is evaluated based on feedback on useful or accurate information. This makes it possible to evaluate the reliability of the information provided by the user.

[0064] The proposal acceptance unit can automatically categorize information provided by users, making it easier to search. For example, the proposal acceptance unit has the generation AI analyze the information provided by users and automatically categorize it. For example, it may categorize it into categories such as tourist attractions, restaurants, shopping, etc. The proposal acceptance unit also has the generation AI extract related keywords based on the information provided by users, making it easier to search. For example, it may display related information when a specific keyword is searched. The proposal acceptance unit also has the generation AI learn from the information provided by users and improve the accuracy of categorization. For example, it may automatically select the most appropriate category based on past data. This makes it easier to search for the information provided by users.

[0065] The point management unit can evaluate the quality and usefulness of the information provided in order to evaluate the user's contribution in detail. For example, the point management unit analyzes the quality of the information provided by the generation AI and evaluates the contribution in detail. For example, it calculates a score based on the accuracy and detail of the information. The point management unit also evaluates the contribution of the generation AI based on the usefulness of the information provided by the user. For example, it assigns a high score to information that other users found useful. The point management unit also learns the quality and usefulness of the information provided by the generation AI and continuously improves the evaluation criteria. For example, it sets optimal evaluation criteria based on past data. This allows the user's contribution to be evaluated in detail.

[0066] The point management unit can automatically analyze the content of the feedback and provide specific areas for improvement and praise to the user. For example, the point management unit uses a generation AI to analyze feedback from other users and extract specific areas for improvement and praise. For example, the point management unit notifies the user of points that need improvement and excellent points based on the content of the feedback. The point management unit also automatically analyzes the content of the feedback and provides specific advice to the user. For example, it makes specific suggestions to increase the level of detail of the information. The point management unit also uses a generation AI to learn the content of the feedback and provide feedback to the user that emphasizes areas of praise. For example, it highlights points that have received high praise from other users. This allows the user to receive specific feedback on areas for improvement and praise.

[0067] The point management unit can use the emotion estimation function to analyze the emotions of the user who provided the feedback and award additional points for emotionally positive feedback. The point management unit, for example, uses the emotion estimation function to analyze the emotions of the user who provided the feedback and award bonus points for feedback with positive emotions. For example, bonus points are awarded for feedback that expresses gratitude. The point management unit also uses the generation AI to identify positive feedback based on the emotion score of the feedback and award bonus points for that feedback. For example, bonus points are awarded for feedback that expresses a high level of joy or satisfaction. The point management unit also builds a system that evaluates the emotional value of feedback based on the emotion estimation data and awards bonus points for positive feedback. For example, bonus points are awarded for feedback with a high emotion score. This makes it possible to award bonus points for emotionally positive feedback.

[0068] The point management unit can compare a user's contribution with other users and display it in a ranking format. For example, the generation AI in the point management unit analyzes a user's contribution and displays it in a ranking format compared to other users. For example, it creates a ranking based on the quality of the information provided and the amount of feedback. The point management unit also builds a system that scores users' contribution and displays it in a ranking format. For example, it displays users with high contributions at the top. The point management unit also allows the generation AI to learn users' contributions and improve the accuracy of the rankings. For example, it sets optimal ranking criteria based on past data. This makes it possible to display users' contributions in a ranking format.

[0069] The point management unit can award additional points by theme when a user contributes on a specific theme or category. For example, the point management unit allows the generation AI to analyze the user's contribution and award bonus points when the user contributes on a specific theme or category. For example, bonus points are awarded for providing information about tourist attractions or restaurants. The point management unit also builds a system in which the generation AI awards bonus points by theme when a user contributes on a specific theme or category. For example, bonus points are awarded for providing information about seasonal events. The point management unit also allows the generation AI to learn the user's contributions and continuously improve the criteria for awarding bonus points by theme. For example, the point management unit sets optimal criteria for awarding bonus points based on past data. This makes it possible to award bonus points when the user contributes on a specific theme or category.

[0070] The point management unit can use the emotion estimation function to provide a special incentive for emotionally positive feedback based on the emotion of the user who provided the feedback. The point management unit, for example, uses the emotion estimation function to analyze the emotion of the user who provided the feedback and provide a special reward for feedback with positive emotions. For example, a special reward is provided for feedback that contains gratitude. The point management unit also uses the generation AI to identify positive feedback based on the emotion score of the feedback and provide a special reward for that feedback. For example, a special reward is provided for feedback that contains a high level of joy or satisfaction. The point management unit also builds a system that evaluates the emotional value of feedback based on the emotion estimation data and provides a special reward for positive feedback. For example, a special reward is provided for feedback with a high emotion score. This makes it possible to provide a special reward for emotionally positive feedback.

[0071] The point management unit can analyze point usage history and recommend the optimal usage method to the user. In the point management unit, for example, the generation AI analyzes the user's point usage history and suggests the optimal usage method. For example, it suggests the most effective way for the user to use points based on past usage patterns. In addition, the point management unit has the generation AI suggest new usage methods based on the user's point usage history. For example, it suggests ways for the user to use points for services or products that they have not yet used. In addition, the point management unit has the generation AI learn the user's point usage patterns and continuously suggest the optimal usage methods. For example, it suggests the optimal timing and method of use based on past data. This makes it possible to suggest the optimal way to use points to the user.

[0072] The point management unit can display the value of the Regional Token DAO in real time, allowing users to use it at the optimal time. For example, the point management unit allows the generation AI to display the value of the Regional Token DAO in real time, allowing users to use it at the optimal time. For example, it may suggest using it when the token value is high. The point management unit also builds a system in which the generation AI suggests the optimal time when users use the Regional Token DAO. For example, it may suggest using it when the token value is rising. The point management unit also allows the generation AI to learn the value of the Regional Token DAO and continuously suggest the optimal time to use it to users. For example, it may predict the optimal time to use it based on past data. This allows users to use the Regional Token DAO at the optimal time.

[0073] The point management unit uses the emotion estimation function to analyze the emotions a user feels when using points and can recommend ways to use them that will give them emotional satisfaction. For example, the point management unit uses the emotion estimation function to analyze the emotions a user feels when using points and recommend ways to use them that will give them emotional satisfaction. For example, it proposes ways to use them that will bring joy to the user. Furthermore, the point management unit uses a generation AI to suggest new ways to use points based on the emotions a user feels when using points. For example, it proposes ways to use points for services or products that will give the user satisfaction. Furthermore, the point management unit builds a system that suggests ways to use points that will give the user emotional satisfaction based on the emotion estimation data. For example, it prioritizes ways to use points that have a high emotion score. This makes it possible to suggest ways to use points that will give the user emotional satisfaction.

[0074] The point management unit can provide a marketplace where points can be exchanged with other users. For example, the point management unit builds a system that provides a marketplace where the generation AI can exchange points with other users. For example, it provides a platform where users can list points and other users can purchase them. In addition, when a user exchanges points with other users, the generation AI proposes the optimal exchange rate. For example, it sets the exchange rate based on supply and demand. In addition, the point management unit allows the generation AI to analyze point exchange history and propose the optimal exchange partner for the user. For example, it proposes a reliable exchange partner based on past transaction history. This allows users to exchange points with other users.

[0075] The point management unit can enable points to be used as tickets to participate in local events and activities. The point management unit, for example, builds a system in which the generation AI can use points as tickets to participate in local events and activities. For example, it makes it possible to purchase tickets for local festivals and workshops with points. Furthermore, when a user uses points to participate in a local event, the point management unit allows the generation AI to suggest the most suitable event. For example, it recommends events based on the user's interests and preferences. Furthermore, the point management unit allows the generation AI to analyze local event information and suggest the most suitable event participation method for the user. For example, it suggests a method of using points to participate in multiple events. This allows the user to use points as tickets to participate in local events and activities.

[0076] The point management unit can use the emotion estimation function to recommend ways of using points that will give users high emotional satisfaction based on the emotions they feel when using points. For example, the point management unit uses the emotion estimation function to analyze the emotions a user feels when using points and suggest ways of using points that will give users high emotional satisfaction. For example, it suggests ways of using points that will bring joy to the user. Furthermore, the point management unit uses a generation AI to suggest new ways of using points based on the emotions a user feels when using points. For example, it suggests ways of using points for services or products that will give the user a sense of satisfaction. Furthermore, the point management unit builds a system that suggests ways of using points that will give users high emotional satisfaction based on the emotion estimation data. For example, it prioritizes ways of using points that have a high emotion score. This makes it possible to suggest ways of using points that will give users high emotional satisfaction.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The course suggestion unit can also monitor the user's health condition and suggest health-conscious courses. For example, the generation AI analyzes the user's number of steps and heart rate to suggest courses that allow for moderate exercise. The course suggestion unit also suggests courses that take calorie consumption into consideration based on the user's health goals. For example, for a user who is on a diet, it will suggest courses that allow for walking or jogging. The course suggestion unit also suggests relaxing courses based on the user's health data. For example, it will suggest a nature walk course to reduce stress. This makes it possible to provide the optimal course according to the user's health condition.

[0079] The recommendation unit can also recommend spots along specific themes based on the user's hobbies and interests. For example, the generation AI analyzes the user's hobbies and suggests museums and galleries to a user who loves art. The recommendation unit also recommends specific events and exhibitions based on the user's interests. For example, it suggests concerts and live events to a user who loves music. The recommendation unit also learns the user's interests and suggests spots that will spark new hobbies and interests. For example, it suggests cooking classes and gourmet festivals to a user who loves cooking. This makes it possible to provide the best spots according to the user's hobbies and interests.

[0080] The ad serving unit can analyze a user's purchasing history and behavioral patterns to provide individually customized coupons. For example, the generation AI analyzes a user's purchasing history to provide individually customized coupons. For example, it provides discount coupons related to products or brands purchased in the past. The ad serving unit also provides coupons tailored to specific time periods or days of the week based on the user's behavioral patterns. For example, it provides coupons for restaurants that can be used during weekday lunchtimes. The ad serving unit also provides coupons tailored to specific events or seasons by having the generation AI learn the user's purchasing history. For example, it provides coupons for gift shops that can be used during the Christmas season. This makes it possible to provide optimal coupons based on the user's purchasing history and behavioral patterns.

[0081] The suggestion receiving unit can evaluate feedback from other users to evaluate the reliability of information provided by the user. For example, the generation AI analyzes feedback from other users and evaluates the reliability of the information provided by the user. For example, information with a lot of positive feedback is given a high rating. The suggestion receiving unit also scores the feedback on the information provided by the user and evaluates the reliability based on the score. For example, information with a high score is displayed preferentially. The suggestion receiving unit also analyzes the content of the feedback and extracts specific evaluation points. For example, reliability is evaluated based on feedback on useful or accurate information. This makes it possible to evaluate the reliability of the information provided by the user.

[0082] The point management unit can evaluate the quality and usefulness of the information provided in order to evaluate the user's contribution in detail. For example, the generation AI analyzes the quality of the information provided by the user and evaluates the contribution in detail. For example, it calculates a score based on the accuracy and detail of the information. The point management unit also evaluates the generation AI's contribution based on the usefulness of the information provided by the user. For example, it assigns a high score to information that other users found useful. The point management unit also allows the generation AI to learn the quality and usefulness of the information provided and continuously improve the evaluation criteria. For example, it sets optimal evaluation criteria based on past data. This allows the user's contribution to be evaluated in detail.

[0083] The course suggestion unit can use the emotion estimation function to suggest a relaxing course or an active course based on the user's current emotional state. For example, the emotion estimation function can be used to analyze the user's current emotional state and suggest a relaxing course. For example, for a user who is feeling stressed, a course including quiet cafes and parks can be suggested. The course suggestion unit can also suggest an active course based on the user's emotional state. For example, for a user who is feeling energetic, a course including sports facilities and activities can be suggested. The course suggestion unit can also use the emotion estimation function to monitor the user's emotional state in real time and dynamically adjust the course if the user's emotions change while out and about. For example, if the user feels tired, a rest spot can be added. This makes it possible to provide an optimal course according to the user's emotional state.

[0084] The recommendation unit can use the emotion estimation function to recommend recommended spots and restaurants based on the user's current emotional state. For example, the emotion estimation function can be used to analyze the user's current emotional state and recommend relaxing spots. For example, a quiet cafe or park can be suggested for a user who is feeling stressed. The recommendation unit can also recommend active spots based on the user's emotional state. For example, sports facilities and activities can be suggested for a user who is feeling energetic. The recommendation unit can also use the emotion estimation function to monitor the user's emotional state in real time and dynamically adjust recommended spots if the user's emotions change while out and about. For example, if the user feels tired, a rest spot can be added. This makes it possible to provide the optimal spots according to the user's emotional state.

[0085] The advertisement providing unit can use the emotion estimation function to customize advertisements based on the user's current emotional state. For example, the emotion estimation function can be used to analyze the user's current emotional state and provide advertisements that help them relax. For example, advertisements for products and services that help them relax can be suggested to a user who is feeling stressed. The advertisement providing unit can also provide active advertisements based on the user's emotional state. For example, advertisements for sports goods and activities can be suggested to a user who is feeling energetic. The advertisement providing unit can also use the emotion estimation function to monitor the user's emotional state in real time and dynamically adjust advertisements when emotions change. For example, if the user feels tired, advertisements for products and services that help them relax can be added. This makes it possible to provide optimal advertisements according to the user's emotional state.

[0086] The point management unit can use the emotion estimation function to analyze the emotions of the user who provided the feedback and award additional points for emotionally positive feedback. For example, the emotion estimation function can be used to analyze the emotions of the user who provided the feedback and award bonus points for feedback with positive emotions. For example, bonus points can be awarded for feedback that expresses gratitude. The point management unit also uses the generation AI to identify positive feedback based on the emotion score of the feedback and award bonus points for that feedback. For example, bonus points can be awarded for feedback that expresses a high level of joy or satisfaction. The point management unit also builds a system that evaluates the emotional value of feedback based on the emotion estimation data and awards bonus points for positive feedback. For example, bonus points can be awarded for feedback with a high emotion score. This makes it possible to award bonus points for emotionally positive feedback.

[0087] The point management unit uses the emotion estimation function to analyze the emotions a user feels when using points and can recommend ways to use them that will give them emotional satisfaction. For example, the emotion estimation function can be used to analyze the emotions a user feels when using points and suggest ways to use them that will give them emotional satisfaction. For example, it can suggest ways to use them that will bring the user joy. The point management unit also uses a generation AI to suggest new ways to use points based on the emotions a user feels when using points. For example, it can suggest ways to use points for services or products that will give the user satisfaction. The point management unit also builds a system that suggests ways to use points that will give the user emotional satisfaction based on the emotion estimation data. For example, it can prioritize ways to use points that have a high emotion score. This makes it possible to suggest ways to use points that will give the user emotional satisfaction.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The course suggestion unit suggests the optimal course based on the purpose of the outing or trip. For example, if the user requests a "one-day course that the whole family can enjoy," the generation AI will suggest a course that combines parks, zoos, family restaurants, etc. Step 2: The recommendation section analyzes the user's preferences and past history to recommend recommended spots, gourmet food, and shopping information. For example, if a user inputs "I like Japanese food," the generation AI will use that information to suggest nearby Japanese restaurants. Step 3: The advertisement providing unit provides advertisements and coupons for commercial facilities. For example, when the user visits a specific commercial facility, the user can receive discount coupons and special offer information. Step 4: The suggestion receiving unit allows users to make suggestions and recommendations. For example, by introducing recommended local spots or hidden gems, users can provide other users with useful information. Step 5: The point management unit manages points based on the information provided by the user. For example, points are added when a user provides course suggestions or useful information, and bonus points are awarded based on feedback from other users.

[0090] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0096] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0097] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0098] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0099] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0100] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0101] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0102] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0104] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 7, a 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0131] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0134] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0140] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0141] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0142] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0143] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0144] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0145] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0146] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0147] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0148] 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.

[0149] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0150] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0151] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0152] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0153] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0154] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0155] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0156] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0157] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The course suggestion department proposes the best course depending on the purpose of your outing or trip, The recommendation section analyzes the user's preferences and past history to recommend recommended spots, gourmet food, and shopping information. An advertising department that provides advertisements and coupons for commercial facilities; a proposal receiving unit for the user to make proposals and introductions; a point management unit that manages points based on the information provided by the user. A system characterized by:

2. The course suggestion unit Learn about the user's past behavior and preferences and suggest individually customized courses 2. The system of claim 1.

3. The recommendation unit Recommend appropriate restaurants based on the user's dietary restrictions and allergy information 2. The system of claim 1.

4. The advertisement providing unit Analyzing the user's purchasing history and behavioral patterns to provide individually customized coupons 2. The system of claim 1.

5. The proposal receiving unit Evaluating feedback from other users to assess the reliability of the information provided by said user 2. The system of claim 1.

6. The point management unit Analyzing the emotions of the user who provided the feedback, and giving additional points to the emotionally positive feedback.

2. The system of claim 1.

Images