system

Abstract

The system according to this embodiment aims to efficiently plan and track the progress of themed travel plans and to facilitate interaction with like-minded individuals. [Solution] The system according to the embodiment comprises a reception unit, a proposal unit, a tracking unit, and a referral unit. The reception unit receives theme settings. The proposal unit proposes a visit plan based on the theme received by the reception unit. The tracking unit tracks the user's progress based on the visit plan proposed by the proposal unit. The referral unit introduces other users pursuing the same theme based on the progress tracked by the tracking unit.

Description

Technical Field

[0006] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the formulation and progress tracking of travel plans based on themes and the exchange with like-minded people are not carried out efficiently, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently carry out the formulation and progress tracking of travel plans based on themes and the exchange with like-minded people.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a proposal unit, a tracking unit, and a referral unit. The reception unit receives theme settings. The proposal unit proposes a visit plan based on the theme received by the reception unit. The tracking unit tracks the user's progress based on the visit plan proposed by the proposal unit. The referral unit introduces other users pursuing the same theme based on the progress tracked by the tracking unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently plan and track the progress of themed travel, as well as facilitate interaction with like-minded individuals. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 3, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that efficiently supports a user's objective of visiting various places according to a specific theme. In this system, the user sets a theme they want to visit and inputs it into the AI ​​agent. Next, the AI ​​agent comprehensively determines the user's current date and time, work schedule, realistic travel distance and means of transportation, budget, etc., and proposes a visit plan. For example, it can handle a wide range of themes such as Japanese castles, dam card distribution locations, non-chain ramen restaurants, and all JR train stations. The AI ​​agent tracks the user's progress and introduces other users pursuing the same theme. For example, the user sets a theme they want to visit. For example, the theme is "I want to visit Japanese castles." This theme is input into the AI ​​agent. Next, the AI ​​agent comprehensively determines the user's current date and time, work schedule, realistic travel distance and means of transportation, budget, etc., and proposes a visit plan. For example, it can suggest castles the user can visit on weekends, or it can create an efficient travel plan by linking it with public transportation timetables and accommodation information. Furthermore, the AI ​​agent tracks the user's progress. It records the places the user has visited, lists unvisited places, and suggests the next destination. This makes it easier for users to track their progress toward their goals and maintain motivation. The AI ​​agent also introduces other users pursuing the same theme, enabling them to exchange information and make joint visits, fostering community building and information sharing. This system allows users to efficiently take action to fully complete their theme and maximize their travel enjoyment. In this way, the AI ​​agent system efficiently supports users in their objectives of visiting various locations according to a specific theme.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, a proposal unit, a tracking unit, and a referral unit. The reception unit allows the user to set a theme they wish to visit. For example, the user can set a theme such as "I want to visit Japanese castles." The proposal unit proposes a visit plan based on the theme received by the reception unit. The proposal unit proposes a visit plan by comprehensively considering factors such as the user's current date and time, work schedule, realistic travel distance and means of transportation, and budget. For example, the proposal unit may suggest castles the user can visit on weekends, or it may create an efficient travel plan by linking public transport timetables and accommodation information. The tracking unit tracks the user's progress based on the visit plan proposed by the proposal unit. For example, the tracking unit records places the user has visited, lists unvisited places, and suggests the next destination. This makes it easier for the user to understand their progress toward achieving their goals and maintain motivation. The referral unit introduces other users pursuing the same theme based on the progress tracked by the tracking unit. The referral unit supports users in exchanging information and conducting joint visits. This allows users to exchange information and conduct joint visits, promoting community formation and information sharing. As a result, the AI ​​agent system according to this embodiment can efficiently support users in their objectives of visiting various locations according to a specific theme.

[0030] The reception desk allows users to set themes for their visits. For example, a user might set a theme such as "I want to visit Japanese castles." The reception desk receives input from users through a user interface. The user interface is designed to allow theme setting in multiple ways, including voice input, text input, and selection from multiple options. For example, speech recognition technology can be used to convert what the user says into text and set it as a theme. It also has a function that automatically suggests theme candidates based on past visit history and the user's interests. This makes it easy for users to set themes and allows for smooth system usage. Furthermore, the reception desk can refer to the user's profile information and past visit history to suggest themes that are best suited to each individual user. For example, it can suggest historical sites to users interested in history, and national parks and scenic spots to users who like nature. This allows users to set themes that match their interests and increases their satisfaction with their visit plans.

[0031] The Proposal Department proposes visit plans based on themes received by the Reception Department. The Proposal Department considers factors such as the user's current date and time, work schedule, realistic travel distance and mode of transport, and budget to propose a visit plan. Specifically, the Proposal Department uses AI to analyze the user's schedule and budget to calculate the optimal destinations and routes. For example, it can suggest castles the user can visit on weekends, or integrate public transport timetables and accommodation information to create an efficient travel plan. The AI ​​prioritizes suggesting destinations the user prefers based on their past visit history and ratings. It also takes weather and event information into consideration to create a plan that maximizes the user's experience at their destination. Furthermore, the Proposal Department supports users in booking activities and sightseeing spots in advance. For example, it can make reservations for popular tourist attractions and restaurants, ensuring a smooth visit. This allows the Proposal Department to provide optimal visit plans tailored to the user's needs, enhancing the visit experience.

[0032] The tracking unit tracks the user's progress based on the visit plan proposed by the suggestion unit. For example, the tracking unit records the places the user has visited, lists unvisited places, and suggests the next destination. Specifically, it uses GPS technology to determine the user's current location in real time and checks the status of arrival at visited locations. When the user arrives at a visited location, the visit history is automatically updated, and the next destination is suggested. It also has a function to allow the user to evaluate their experience at visited locations, and based on the evaluation results, it can propose a more accurate next visit plan. Furthermore, the tracking unit checks whether the user is enjoying the activities and sights at visited locations and provides support as needed. For example, if a problem or inconvenience occurs at a visited location, it responds quickly to ensure the user can continue their visit with peace of mind. In this way, the tracking unit can accurately grasp the progress of the user's visit plan and support the visit experience.

[0033] The Referral Department introduces other users pursuing the same theme based on progress tracked by the Tracking Department. The Referral Department supports users in exchanging information and conducting joint visits, for example. Specifically, it matches compatible users based on their visit history and interests, providing opportunities for communication. For instance, it can introduce users who want to visit the same castle, allowing them to jointly plan visits and exchange information. Furthermore, the Referral Department prioritizes privacy protection and security measures to ensure safe interaction among users. For example, it protects users' personal information and implements a system to share information only when necessary. In addition, the Referral Department plans and manages events and community activities to promote interaction among users. For example, it holds online events and offline meetups related to themes, providing opportunities for users to interact directly. This allows the Referral Department to support information exchange and joint visits among users, promoting community building and information sharing.

[0034] The suggestion unit can propose a visit plan by comprehensively considering the user's current date and time, work schedule, realistic travel distance and means of transportation, and budget. For example, the suggestion unit can suggest places that can be visited based on the user's current date and time. For example, the suggestion unit can create a visit plan considering the user's work schedule. The suggestion unit can also propose a visit plan considering the user's realistic travel distance and means of transportation. For example, the suggestion unit can create an efficient travel plan by linking public transport timetables and accommodation information. Furthermore, the suggestion unit can propose a visit plan considering the user's budget. For example, the suggestion unit can suggest destinations that fit the user's budget. This allows for the proposal of an optimal visit plan tailored to the user's situation. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input user data into a generative AI and have the generative AI execute the visit plan proposal.

[0035] The tracking unit can record the places the user has visited, list unvisited places, and suggest the next place to visit. For example, the tracking unit can record the places the user has visited in a database. For example, the tracking unit can automatically collect information on the places the user has visited and store it in a database. The tracking unit can also list unvisited places and suggest the next place to visit. For example, the tracking unit can list places the user has not yet visited and suggest them as the next place to visit. This allows for efficient management of the user's visit progress and the suggestion of the next place to visit. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input the user's visit data into AI and have the AI ​​perform the task of listing unvisited places.

[0036] The integration unit can integrate information on public transportation timetables and accommodations. For example, the integration unit can retrieve public transportation timetables from a database and reflect them in the travel plan. For example, the integration unit can retrieve public transportation timetables in real time and reflect them in the travel plan. The integration unit can also retrieve information on accommodations from a database and reflect it in the travel plan. For example, the integration unit can retrieve information on the availability of accommodations and reflect it in the travel plan. This allows for the creation of efficient travel plans. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input public transportation timetable data into AI and have AI perform the task of reflecting it in the travel plan.

[0037] The cost management department can track the costs of each visit and manage the total cost. For example, the cost management department can record the cost of each visit in a database. For example, the cost management department can automatically collect the costs of places visited by the user and store them in the database. The cost management department can also manage the total cost. For example, the cost management department can calculate and manage the total cost based on the user's visit plan. This allows for efficient budget management of the visit plan. Some or all of the above processes in the cost management department may be performed using AI, for example, or not using AI. For example, the cost management department can input the user's cost data into AI and have the AI ​​manage the total cost.

[0038] The report generation unit can generate a visit report after each visit and collect feedback. For example, the report generation unit can generate a visit report based on information about the places the user visited. For example, the report generation unit can collect photos and comments of the places the user visited and generate a visit report. The report generation unit can also collect feedback. For example, the report generation unit can collect evaluations and impressions of the places the user visited and reflect them in the next visit plan. This allows for the collection of post-visit feedback and its reflection in the next visit plan. Some or all of the above processes in the report generation unit may be performed using AI, for example, or not using AI. For example, the report generation unit can input user visit data into AI and have the AI ​​generate the visit report.

[0039] The reception desk can analyze the user's past theme setting history and suggest the most suitable theme. For example, the reception desk can suggest new, relevant themes based on places and themes the user has visited in the past. For example, the reception desk can analyze the trends of themes the user has set in the past and suggest themes that might interest them. The reception desk can also suggest highly-rated themes based on the user's ratings of places they have visited in the past. This enables optimal theme setting based on the user's past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's theme setting history data into AI and have the AI ​​suggest the most suitable theme.

[0040] The reception desk can filter themes based on the user's current interests and preferences when setting themes. For example, the reception desk can suggest relevant themes based on keywords the user has recently searched for or their browsing history. For example, the reception desk can analyze accounts and posts the user follows on social media and suggest themes that might interest them. The reception desk can also suggest relevant themes based on events and activities the user has recently participated in. This makes it possible to set themes based on the user's current interests and preferences. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input user interest data into AI and have the AI ​​perform theme setting filtering.

[0041] The reception desk can prioritize suggesting themes that are highly relevant to the user's geographical location when setting a theme. For example, the reception desk can prioritize suggesting themes related to places close to the user's current location. For example, the reception desk can prioritize suggesting themes related to places the user plans to visit. It can also prioritize suggesting themes related to places the user has visited in the past. This enables theme setting based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into AI and have the AI ​​perform theme setting suggestions.

[0042] The reception desk can analyze the user's social media activity and suggest relevant themes when setting a theme. For example, the reception desk can suggest relevant themes based on the accounts and posts the user follows on social media. For example, the reception desk can suggest relevant themes based on the groups and events the user participates in on social media. Furthermore, the reception desk can suggest relevant themes based on the content the user shares on social media. This makes it possible to set themes based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into AI and have the AI ​​perform theme setting suggestions.

[0043] The suggestion unit can propose the most suitable visit plan by referring to the user's past visit history when suggesting a visit plan. For example, the suggestion unit can suggest a new visit plan based on places and themes the user has visited in the past. For example, the suggestion unit can analyze the trends of visit plans the user has previously set and suggest visit plans that are likely to interest them. The suggestion unit can also suggest highly-rated visit plans based on the user's ratings of places they have visited in the past. This allows for the proposal of the most suitable visit plan based on the user's past visit history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's visit history data into AI and have the AI ​​propose the most suitable visit plan.

[0044] The suggestion unit can filter visit plans based on the user's current lifestyle and areas of interest. For example, the suggestion unit can suggest relevant visit plans based on keywords the user has recently searched for and their browsing history. For example, the suggestion unit can analyze accounts and posts the user follows on social media and suggest visit plans that might interest them. The suggestion unit can also suggest relevant visit plans based on events and activities the user has recently participated in. This allows the suggestion unit to propose visit plans that are tailored to the user's current lifestyle and areas of interest. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input user lifestyle and area of ​​interest data into AI and have the AI ​​perform the filtering of visit plans.

[0045] The suggestion unit can prioritize suggesting highly relevant plans when proposing a visit plan, taking into account the user's geographical location. For example, the suggestion unit can prioritize suggesting visit plans related to places close to the user's current location. For example, the suggestion unit can prioritize suggesting visit plans related to places the user plans to visit. It can also prioritize suggesting visit plans related to places the user has visited in the past. This allows for the suggestion of visit plans based on the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location data into AI and have the AI ​​perform the visit plan suggestion.

[0046] The suggestion unit can analyze a user's social media activity and propose a relevant plan when suggesting a visit plan. For example, the suggestion unit can suggest a relevant visit plan based on the accounts and posts the user follows on social media. For example, the suggestion unit can suggest a relevant visit plan based on the groups and events the user participates in on social media. Furthermore, the suggestion unit can suggest a relevant visit plan based on the content the user shares on social media. This allows for the suggestion of a visit plan based on the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's social media activity data into AI and have the AI ​​execute the visit plan proposal.

[0047] The tracking unit can suggest the optimal progress management method by referring to the user's past visit history when tracking progress. For example, the tracking unit can suggest new relevant progress management methods based on places and themes the user has visited in the past. For example, the tracking unit can analyze the trends of progress management methods the user has previously set and suggest progress management methods that might interest them. The tracking unit can also suggest highly-rated progress management methods based on the user's evaluation of places they have visited in the past. This allows the tracking unit to suggest the optimal progress management method based on the user's past visit history. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's visit history data into AI and have the AI ​​suggest the optimal progress management method.

[0048] The tracking unit can filter progress based on the user's current lifestyle and areas of interest. For example, the tracking unit can suggest relevant progress management methods based on keywords the user has recently searched for and their browsing history. For example, the tracking unit can analyze accounts and posts the user follows on social media and suggest progress management methods that might interest them. The tracking unit can also suggest relevant progress management methods based on events and activities the user has recently participated in. This enables progress management based on the user's current lifestyle and areas of interest. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not. For example, the tracking unit can input user lifestyle and area of ​​interest data into AI and have the AI ​​perform the progress management filtering.

[0049] The tracking unit can prioritize tracking highly relevant progress while considering the user's geographical location information. For example, the tracking unit can prioritize tracking progress related to locations close to the user's current location. For example, the tracking unit can prioritize tracking progress related to places the user plans to visit. The tracking unit can also prioritize tracking progress related to places the user has visited in the past. This enables progress management based on the user's geographical location information. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's geographical location information data into AI and have the AI ​​make progress management suggestions.

[0050] The tracking unit can analyze the user's social media activity and track relevant progress during progress tracking. For example, the tracking unit can track relevant progress based on the accounts and posts the user follows on social media. For example, the tracking unit can track relevant progress based on the groups and events the user participates in on social media. The tracking unit can also track relevant progress based on the content the user shares on social media. This enables progress management based on the user's social media activity. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input the user's social media activity data into AI and have the AI ​​make progress management suggestions.

[0051] The referral system can suggest the most suitable referral method when referring other users, by referring to the user's past interaction history. For example, the referral system can suggest relevant new users based on the trends of users the user has interacted with in the past. For example, the referral system can analyze the trends of interaction methods the user has set in the past and suggest users that might be of interest to them. The referral system can also suggest highly-rated users based on the user's evaluations of users they have interacted with in the past. This makes it possible to introduce the most suitable other users based on the user's past interaction history. Some or all of the above processing in the referral system may be performed using AI, for example, or not using AI. For example, the referral system can input the user's interaction history data into AI and have the AI ​​suggest the most suitable referral method.

[0052] The referral function can filter users based on their current lifestyle and areas of interest when recommending other users. For example, the referral function can recommend relevant users based on keywords the user has recently searched for or their browsing history. For example, the referral function can analyze accounts and posts the user follows on social media and recommend users who might be of interest to them. The referral function can also recommend relevant users based on events and activities the user has recently participated in. This makes it possible to recommend other users based on the user's current lifestyle and areas of interest. Some or all of the above processing in the referral function may be performed using AI, for example, or not. For example, the referral function can input data on the user's lifestyle and areas of interest into an AI and have the AI ​​perform the recommendation of other users.

[0053] The referral unit can prioritize recommending highly relevant users by considering the user's geographical location when recommending other users. For example, the referral unit can prioritize recommending users who are close to the user's current location. For example, the referral unit can prioritize recommending users who are related to places the user plans to visit. It can also prioritize recommending users who are near places the user has visited in the past. This makes it possible to recommend other users based on the user's geographical location. Some or all of the above processing in the referral unit may be performed using AI, for example, or not using AI. For example, the referral unit can input the user's geographical location data into AI and have the AI ​​perform the recommendation of other users.

[0054] The referral unit can analyze a user's social media activity and recommend relevant users when referring other users. For example, the referral unit can recommend relevant users based on the accounts and posts a user follows on social media. For example, the referral unit can recommend relevant users based on the groups and events a user participates in on social media. The referral unit can also recommend relevant users based on the content a user shares on social media. This makes it possible to recommend other users based on a user's social media activity. Some or all of the above processing in the referral unit may be performed using AI, for example, or not using AI. For example, the referral unit can input a user's social media activity data into an AI and have the AI ​​perform the recommendation of other users.

[0055] The integration unit can propose the optimal integration method by referring to the user's past integration history when providing integration information. For example, the integration unit can propose new relevant integration methods based on the trends of information the user has previously integrated. For example, the integration unit can analyze the trends of integration methods the user has previously set and propose integration methods that might be of interest. The integration unit can also propose highly-rated integration methods based on the user's evaluation of information they have previously integrated. This allows the integration unit to propose the optimal integration method based on the user's past integration history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's integration history data into AI and have the AI ​​propose the optimal integration method.

[0056] The integration unit can prioritize providing highly relevant information by considering the user's geographical location when providing integration information. For example, the integration unit can prioritize providing integration information related to locations close to the user's current location. For example, the integration unit can prioritize providing integration information related to places the user plans to visit. The integration unit can also prioritize providing integration information related to places the user has visited in the past. This allows for the provision of integration information based on the user's geographical location. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's geographical location data into AI and have AI perform the provision of integration information.

[0057] The cost management department can propose the optimal cost management method by referring to the user's past spending history during cost management. For example, the cost management department can propose new relevant cost management methods based on trends in items the user has spent on in the past. For example, the cost management department can analyze trends in cost management methods previously set by the user and propose cost management methods that might be of interest. Furthermore, the cost management department can propose highly-rated cost management methods based on evaluations of items the user has spent on in the past. This allows for the proposal of the optimal cost management method based on the user's past spending history. Some or all of the above processes in the cost management department may be performed using AI, for example, or not. For example, the cost management department can input the user's spending history data into AI and have the AI ​​propose the optimal cost management method.

[0058] The cost management unit can prioritize the management of highly relevant costs by considering the user's geographical location information during cost management. For example, the cost management unit can prioritize costs related to locations close to the user's current location. For example, the cost management unit can prioritize costs related to places the user plans to visit. It can also prioritize costs related to places the user has visited in the past. This enables cost management based on the user's geographical location information. Some or all of the above processing in the cost management unit may be performed using AI, for example, or not using AI. For example, the cost management unit can input the user's geographical location information data into AI and have the AI ​​execute cost management suggestions.

[0059] The report generation unit can generate the most suitable report by referring to the user's past visit history when generating a report. For example, the report generation unit can generate a new, relevant report based on places and themes the user has visited in the past. For example, the report generation unit can analyze the trends of reports the user has previously set and generate reports that are likely to be of interest. The report generation unit can also generate highly rated reports based on the user's ratings of places they have visited in the past. This allows for the generation of the most suitable report based on the user's past visit history. Some or all of the above processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input the user's visit history data into AI and have the AI ​​generate the most suitable report.

[0060] The report generation unit can prioritize generating highly relevant reports by considering the user's geographical location information during report generation. For example, the report generation unit can prioritize generating reports related to locations close to the user's current location. For example, the report generation unit can prioritize generating reports related to places the user plans to visit. It can also prioritize generating reports related to locations near places the user has visited in the past. This enables the generation of reports based on the user's geographical location information. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input the user's geographical location data into AI and have the AI ​​perform the report generation.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The suggestion function can analyze a user's past visit history and propose the optimal visit plan. For example, it can suggest new destinations based on places and themes the user has visited in the past. Specifically, it can suggest castles the user has not yet visited based on a list of castles the user has visited in the past. It can also analyze trends in the visit plans the user has set in the past and suggest destinations that might interest them. For example, it can suggest highly-rated ramen restaurants based on the user's ratings of ramen restaurants they have visited in the past. In this way, it can propose the optimal visit plan based on the user's past visit history.

[0063] The referral function can analyze a user's past interaction history and recommend the most suitable other users. For example, it can recommend relevant new users based on the trends of users the user has interacted with in the past. Specifically, it can recommend highly-rated users based on the ratings the user has given to users they have interacted with in the past. It can also analyze the trends in the interaction methods the user has set in the past and recommend users who are likely to be interested in them. For example, it can recommend users who have participated in related events based on the trends of events the user has attended in the past. This makes it possible to recommend the most suitable other users based on the user's past interaction history.

[0064] The cost management department can analyze a user's past spending history and propose the most suitable cost management method. For example, it can suggest new cost management methods based on trends in items the user has spent money on in the past. Specifically, it can suggest new places that the user can visit within their budget based on the costs of places they have visited in the past. It can also analyze trends in cost management methods the user has previously set and suggest cost management methods that they might be interested in. For example, it can suggest highly-rated accommodations based on the user's ratings of accommodations they have used in the past. In this way, it can propose the most suitable cost management method based on the user's past spending history.

[0065] The suggestion function can propose travel plans based on the user's current lifestyle and areas of interest. For example, it can suggest relevant destinations based on keywords the user has recently searched for and their browsing history. Specifically, it can suggest relevant destinations based on tourist spots and events the user has recently searched for. It can also analyze accounts and posts the user follows on social media and suggest destinations that might interest them. For example, it can suggest relevant destinations based on posts from travel bloggers the user follows. This allows for the proposal of optimal travel plans based on the user's current lifestyle and areas of interest.

[0066] The tracking unit can manage progress while taking the user's geographical location into consideration. For example, it can prioritize tracking progress related to locations close to the user's current location. Specifically, it can prioritize tracking progress for nearby tourist destinations or events. It can also prioritize tracking progress related to places the user plans to visit. For example, it can prioritize tracking progress for the next tourist destination the user plans to visit. Furthermore, it can prioritize tracking progress related to locations near places the user has visited in the past. For example, it can prioritize tracking progress for new tourist destinations near previously visited tourist destinations. This enables optimal progress management based on the user's geographical location.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk sets the theme the user wants to visit. For example, the user can set a theme such as "I want to visit Japanese castles." Step 2: The proposal department proposes a visit plan based on the theme received by the reception department. The proposal department proposes a visit plan by comprehensively considering factors such as the user's current date and time, work schedule, realistic travel distance and means of transportation, and budget. For example, the proposal department may suggest castles that the user can visit on the weekend, or it may be able to create an efficient travel plan by linking public transport timetables and accommodation information. Step 3: The tracking unit tracks the user's progress based on the visit plan proposed by the suggestion unit. For example, the tracking unit records the places the user has visited, lists unvisited places, and suggests the next places to visit. This makes it easier for the user to understand their progress toward achieving their goals and maintain their motivation. Step 4: The referral team refers other users following the same topic based on the progress tracked by the tracking team. The referral team supports users in exchanging information and conducting joint visits, for example. This allows users to exchange information and conduct joint visits, promoting community building and information sharing.

[0069] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that efficiently supports a user's objective of visiting various places according to a specific theme. In this system, the user sets a theme they want to visit and inputs it into the AI ​​agent. Next, the AI ​​agent comprehensively determines the user's current date and time, work schedule, realistic travel distance and means of transportation, budget, etc., and proposes a visit plan. For example, it can handle a wide range of themes such as Japanese castles, dam card distribution locations, non-chain ramen restaurants, and all JR train stations. The AI ​​agent tracks the user's progress and introduces other users pursuing the same theme. For example, the user sets a theme they want to visit. For example, the theme is "I want to visit Japanese castles." This theme is input into the AI ​​agent. Next, the AI ​​agent comprehensively determines the user's current date and time, work schedule, realistic travel distance and means of transportation, budget, etc., and proposes a visit plan. For example, it can suggest castles the user can visit on weekends, or it can create an efficient travel plan by linking it with public transportation timetables and accommodation information. Furthermore, the AI ​​agent tracks the user's progress. It records the places the user has visited, lists unvisited places, and suggests the next destination. This makes it easier for users to track their progress toward their goals and maintain motivation. The AI ​​agent also introduces other users pursuing the same theme, enabling them to exchange information and make joint visits, fostering community building and information sharing. This system allows users to efficiently take action to fully complete their theme and maximize their travel enjoyment. In this way, the AI ​​agent system efficiently supports users in their objectives of visiting various locations according to a specific theme.

[0070] The AI ​​agent system according to this embodiment comprises a reception unit, a proposal unit, a tracking unit, and a referral unit. The reception unit allows the user to set a theme they wish to visit. For example, the user can set a theme such as "I want to visit Japanese castles." The proposal unit proposes a visit plan based on the theme received by the reception unit. The proposal unit proposes a visit plan by comprehensively considering factors such as the user's current date and time, work schedule, realistic travel distance and means of transportation, and budget. For example, the proposal unit may suggest castles the user can visit on weekends, or it may create an efficient travel plan by linking public transport timetables and accommodation information. The tracking unit tracks the user's progress based on the visit plan proposed by the proposal unit. For example, the tracking unit records places the user has visited, lists unvisited places, and suggests the next destination. This makes it easier for the user to understand their progress toward achieving their goals and maintain motivation. The referral unit introduces other users pursuing the same theme based on the progress tracked by the tracking unit. The referral unit supports users in exchanging information and conducting joint visits. This allows users to exchange information and conduct joint visits, promoting community formation and information sharing. As a result, the AI ​​agent system according to this embodiment can efficiently support users in their objectives of visiting various locations according to a specific theme.

[0071] The reception desk allows users to set themes for their visits. For example, a user might set a theme such as "I want to visit Japanese castles." The reception desk receives input from users through a user interface. The user interface is designed to allow theme setting in multiple ways, including voice input, text input, and selection from multiple options. For example, speech recognition technology can be used to convert what the user says into text and set it as a theme. It also has a function that automatically suggests theme candidates based on past visit history and the user's interests. This makes it easy for users to set themes and allows for smooth system usage. Furthermore, the reception desk can refer to the user's profile information and past visit history to suggest themes that are best suited to each individual user. For example, it can suggest historical sites to users interested in history, and national parks and scenic spots to users who like nature. This allows users to set themes that match their interests and increases their satisfaction with their visit plans.

[0072] The Proposal Department proposes visit plans based on themes received by the Reception Department. The Proposal Department considers factors such as the user's current date and time, work schedule, realistic travel distance and mode of transport, and budget to propose a visit plan. Specifically, the Proposal Department uses AI to analyze the user's schedule and budget to calculate the optimal destinations and routes. For example, it can suggest castles the user can visit on weekends, or integrate public transport timetables and accommodation information to create an efficient travel plan. The AI ​​prioritizes suggesting destinations the user prefers based on their past visit history and ratings. It also takes weather and event information into consideration to create a plan that maximizes the user's experience at their destination. Furthermore, the Proposal Department supports users in booking activities and sightseeing spots in advance. For example, it can make reservations for popular tourist attractions and restaurants, ensuring a smooth visit. This allows the Proposal Department to provide optimal visit plans tailored to the user's needs, enhancing the visit experience.

[0073] The tracking unit tracks the user's progress based on the visit plan proposed by the suggestion unit. For example, the tracking unit records the places the user has visited, lists unvisited places, and suggests the next destination. Specifically, it uses GPS technology to determine the user's current location in real time and checks the status of arrival at visited locations. When the user arrives at a visited location, the visit history is automatically updated, and the next destination is suggested. It also has a function to allow the user to evaluate their experience at visited locations, and based on the evaluation results, it can propose a more accurate next visit plan. Furthermore, the tracking unit checks whether the user is enjoying the activities and sights at visited locations and provides support as needed. For example, if a problem or inconvenience occurs at a visited location, it responds quickly to ensure the user can continue their visit with peace of mind. In this way, the tracking unit can accurately grasp the progress of the user's visit plan and support the visit experience.

[0074] The Referral Department introduces other users pursuing the same theme based on progress tracked by the Tracking Department. The Referral Department supports users in exchanging information and conducting joint visits, for example. Specifically, it matches compatible users based on their visit history and interests, providing opportunities for communication. For instance, it can introduce users who want to visit the same castle, allowing them to jointly plan visits and exchange information. Furthermore, the Referral Department prioritizes privacy protection and security measures to ensure safe interaction among users. For example, it protects users' personal information and implements a system to share information only when necessary. In addition, the Referral Department plans and manages events and community activities to promote interaction among users. For example, it holds online events and offline meetups related to themes, providing opportunities for users to interact directly. This allows the Referral Department to support information exchange and joint visits among users, promoting community building and information sharing.

[0075] The suggestion unit can propose a visit plan by comprehensively considering the user's current date and time, work schedule, realistic travel distance and means of transportation, and budget. For example, the suggestion unit can suggest places that can be visited based on the user's current date and time. For example, the suggestion unit can create a visit plan considering the user's work schedule. The suggestion unit can also propose a visit plan considering the user's realistic travel distance and means of transportation. For example, the suggestion unit can create an efficient travel plan by linking public transport timetables and accommodation information. Furthermore, the suggestion unit can propose a visit plan considering the user's budget. For example, the suggestion unit can suggest destinations that fit the user's budget. This allows for the proposal of an optimal visit plan tailored to the user's situation. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input user data into a generative AI and have the generative AI execute the visit plan proposal.

[0076] The tracking unit can record the places the user has visited, list unvisited places, and suggest the next place to visit. For example, the tracking unit can record the places the user has visited in a database. For example, the tracking unit can automatically collect information on the places the user has visited and store it in a database. The tracking unit can also list unvisited places and suggest the next place to visit. For example, the tracking unit can list places the user has not yet visited and suggest them as the next place to visit. This allows for efficient management of the user's visit progress and the suggestion of the next place to visit. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input the user's visit data into AI and have the AI ​​perform the task of listing unvisited places.

[0077] The referral function can introduce other users pursuing the same theme and support information exchange and joint visits. For example, the referral function can search a database for other users pursuing the same theme and introduce them. For example, the referral function can search for and introduce other users pursuing the same theme based on a theme set by the user. The referral function can also support information exchange and joint visits. For example, the referral function can provide a platform for users to exchange information and conduct joint visits. This promotes interaction among users and enables information sharing. Some or all of the above processing in the referral function is implemented using sentiment estimation functions, for example, using a sentiment engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the referral function can input a user's sentiment data into the generative AI and have the generative AI perform introductions to other users.

[0078] The integration unit can integrate information on public transportation timetables and accommodations. For example, the integration unit can retrieve public transportation timetables from a database and reflect them in the travel plan. For example, the integration unit can retrieve public transportation timetables in real time and reflect them in the travel plan. The integration unit can also retrieve information on accommodations from a database and reflect it in the travel plan. For example, the integration unit can retrieve information on the availability of accommodations and reflect it in the travel plan. This allows for the creation of efficient travel plans. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input public transportation timetable data into AI and have AI perform the task of reflecting it in the travel plan.

[0079] The cost management department can track the costs of each visit and manage the total cost. For example, the cost management department can record the cost of each visit in a database. For example, the cost management department can automatically collect the costs of places visited by the user and store them in the database. The cost management department can also manage the total cost. For example, the cost management department can calculate and manage the total cost based on the user's visit plan. This allows for efficient budget management of the visit plan. Some or all of the above processes in the cost management department may be performed using AI, for example, or not using AI. For example, the cost management department can input the user's cost data into AI and have the AI ​​manage the total cost.

[0080] The report generation unit can generate a visit report after each visit and collect feedback. For example, the report generation unit can generate a visit report based on information about the places the user visited. For example, the report generation unit can collect photos and comments of the places the user visited and generate a visit report. The report generation unit can also collect feedback. For example, the report generation unit can collect evaluations and impressions of the places the user visited and reflect them in the next visit plan. This allows for the collection of post-visit feedback and its reflection in the next visit plan. Some or all of the above processes in the report generation unit may be performed using AI, for example, or not using AI. For example, the report generation unit can input user visit data into AI and have the AI ​​generate the visit report.

[0081] The reception desk can estimate the user's emotions and suggest theme settings based on those emotions. For example, if the user is feeling stressed, the reception desk can suggest relaxing themes. For example, it could suggest themes such as visiting hot springs or nature walks. If the user is excited, the reception desk can also suggest active themes. For example, it could suggest themes such as adventure sports or theme park visits. Furthermore, if the user is tired, the reception desk can suggest refreshing themes. For example, it could suggest themes such as visiting relaxation facilities or cafes. This makes it possible to suggest theme settings that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the reception desk can input user emotion data into the generative AI and have the generative AI suggest theme settings.

[0082] The reception desk can analyze the user's past theme setting history and suggest the most suitable theme. For example, the reception desk can suggest new, relevant themes based on places and themes the user has visited in the past. For example, the reception desk can analyze the trends of themes the user has set in the past and suggest themes that might interest them. The reception desk can also suggest highly-rated themes based on the user's ratings of places they have visited in the past. This enables optimal theme setting based on the user's past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's theme setting history data into AI and have the AI ​​suggest the most suitable theme.

[0083] The reception desk can filter themes based on the user's current interests and preferences when setting themes. For example, the reception desk can suggest relevant themes based on keywords the user has recently searched for or their browsing history. For example, the reception desk can analyze accounts and posts the user follows on social media and suggest themes that might interest them. The reception desk can also suggest relevant themes based on events and activities the user has recently participated in. This makes it possible to set themes based on the user's current interests and preferences. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input user interest data into AI and have the AI ​​perform theme setting filtering.

[0084] The reception desk can estimate the user's emotions and determine the priority of theme settings based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize suggesting relaxing themes. For example, it may prioritize suggesting themes such as visiting hot springs or nature walks. Also, if the user is excited, the reception desk can prioritize suggesting active themes. For example, it may prioritize suggesting themes such as adventure sports or theme park visits. Furthermore, if the user is tired, the reception desk can prioritize suggesting refreshing themes. For example, it may prioritize suggesting themes such as visiting relaxation facilities or cafes. This allows for the determination of theme settings priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the reception desk can input user emotion data into the generative AI and have the generative AI determine the priority of theme settings.

[0085] The reception desk can prioritize suggesting themes that are highly relevant to the user's geographical location when setting a theme. For example, the reception desk can prioritize suggesting themes related to places close to the user's current location. For example, the reception desk can prioritize suggesting themes related to places the user plans to visit. It can also prioritize suggesting themes related to places the user has visited in the past. This enables theme setting based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into AI and have the AI ​​perform theme setting suggestions.

[0086] The reception desk can analyze the user's social media activity and suggest relevant themes when setting a theme. For example, the reception desk can suggest relevant themes based on the accounts and posts the user follows on social media. For example, the reception desk can suggest relevant themes based on the groups and events the user participates in on social media. Furthermore, the reception desk can suggest relevant themes based on the content the user shares on social media. This makes it possible to set themes based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into AI and have the AI ​​perform theme setting suggestions.

[0087] The suggestion unit can estimate the user's emotions and adjust the presentation of the visit plan based on those emotions. For example, if the user is relaxed, the suggestion unit can suggest a visit plan that proceeds at a leisurely pace. For example, if the user is in a hurry, the suggestion unit can suggest a visit plan that emphasizes the shortest route. Furthermore, if the user is excited, the suggestion unit can suggest a visit plan with visually stimulating effects. This allows the presentation of the visit plan to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the visit plan.

[0088] The suggestion unit can propose the most suitable visit plan by referring to the user's past visit history when suggesting a visit plan. For example, the suggestion unit can suggest a new visit plan based on places and themes the user has visited in the past. For example, the suggestion unit can analyze the trends of visit plans the user has previously set and suggest visit plans that are likely to interest them. The suggestion unit can also suggest highly-rated visit plans based on the user's ratings of places they have visited in the past. This allows for the proposal of the most suitable visit plan based on the user's past visit history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's visit history data into AI and have the AI ​​propose the most suitable visit plan.

[0089] The suggestion unit can filter visit plans based on the user's current lifestyle and areas of interest. For example, the suggestion unit can suggest relevant visit plans based on keywords the user has recently searched for and their browsing history. For example, the suggestion unit can analyze accounts and posts the user follows on social media and suggest visit plans that might interest them. The suggestion unit can also suggest relevant visit plans based on events and activities the user has recently participated in. This allows the suggestion unit to propose visit plans that are tailored to the user's current lifestyle and areas of interest. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input user lifestyle and area of ​​interest data into AI and have the AI ​​perform the filtering of visit plans.

[0090] The suggestion function can estimate the user's emotions and prioritize visit plans based on those emotions. For example, if the user is feeling stressed, the suggestion function will prioritize suggesting relaxing visit plans. For instance, it might prioritize suggesting hot spring tours or nature walks. Similarly, if the user is excited, the suggestion function can prioritize suggesting active visit plans. For example, it might prioritize suggesting adventure sports or theme park visits. Furthermore, if the user is tired, the suggestion function can prioritize suggesting refreshing visit plans. For example, it might prioritize suggesting relaxation facilities or cafe hopping. This allows for the prioritization of visit plans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The proposal department can, for example, input user emotion data into a generating AI and have the generating AI determine the priorities of the visit plan.

[0091] The suggestion unit can prioritize suggesting highly relevant plans when proposing a visit plan, taking into account the user's geographical location. For example, the suggestion unit can prioritize suggesting visit plans related to places close to the user's current location. For example, the suggestion unit can prioritize suggesting visit plans related to places the user plans to visit. It can also prioritize suggesting visit plans related to places the user has visited in the past. This allows for the suggestion of visit plans based on the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location data into AI and have the AI ​​perform the visit plan suggestion.

[0092] The suggestion unit can analyze a user's social media activity and propose a relevant plan when suggesting a visit plan. For example, the suggestion unit can suggest a relevant visit plan based on the accounts and posts the user follows on social media. For example, the suggestion unit can suggest a relevant visit plan based on the groups and events the user participates in on social media. Furthermore, the suggestion unit can suggest a relevant visit plan based on the content the user shares on social media. This allows for the suggestion of a visit plan based on the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's social media activity data into AI and have the AI ​​execute the visit plan proposal.

[0093] The tracking unit can estimate the user's emotions and adjust the progress display method based on the estimated emotions. For example, if the user is stressed, the tracking unit can provide a simple and highly visible display method. For example, if the user is relaxed, the tracking unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the tracking unit can provide a concise display method. This allows the progress display method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the tracking unit can input user emotion data into the generative AI and have the generative AI adjust the progress display method.

[0094] The tracking unit can suggest the optimal progress management method by referring to the user's past visit history when tracking progress. For example, the tracking unit can suggest new relevant progress management methods based on places and themes the user has visited in the past. For example, the tracking unit can analyze the trends of progress management methods the user has previously set and suggest progress management methods that might interest them. The tracking unit can also suggest highly-rated progress management methods based on the user's evaluation of places they have visited in the past. This allows the tracking unit to suggest the optimal progress management method based on the user's past visit history. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's visit history data into AI and have the AI ​​suggest the optimal progress management method.

[0095] The tracking unit can filter progress based on the user's current lifestyle and areas of interest. For example, the tracking unit can suggest relevant progress management methods based on keywords the user has recently searched for and their browsing history. For example, the tracking unit can analyze accounts and posts the user follows on social media and suggest progress management methods that might interest them. The tracking unit can also suggest relevant progress management methods based on events and activities the user has recently participated in. This enables progress management based on the user's current lifestyle and areas of interest. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not. For example, the tracking unit can input user lifestyle and area of ​​interest data into AI and have the AI ​​perform the progress management filtering.

[0096] The tracking unit can estimate the user's emotions and determine the priority of activities based on those emotions. For example, if the user is stressed, the tracking unit can prioritize activities that promote relaxation. For instance, it might suggest activities like visiting hot springs or nature walks. Similarly, if the user is excited, the tracking unit can prioritize active activities. For example, it might suggest activities like adventure sports or theme park visits. Furthermore, if the user is tired, the tracking unit can prioritize activities that promote refreshment. For example, it might suggest activities like visiting relaxation facilities or cafes. This allows for the determination of activity priorities that align with the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The tracking unit can, for example, input user emotion data into the generative AI and have the generative AI determine the priority of activities.

[0097] The tracking unit can prioritize tracking highly relevant progress while considering the user's geographical location information. For example, the tracking unit can prioritize tracking progress related to locations close to the user's current location. For example, the tracking unit can prioritize tracking progress related to places the user plans to visit. The tracking unit can also prioritize tracking progress related to places the user has visited in the past. This enables progress management based on the user's geographical location information. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's geographical location information data into AI and have the AI ​​make progress management suggestions.

[0098] The tracking unit can analyze the user's social media activity and track relevant progress during progress tracking. For example, the tracking unit can track relevant progress based on the accounts and posts the user follows on social media. For example, the tracking unit can track relevant progress based on the groups and events the user participates in on social media. The tracking unit can also track relevant progress based on the content the user shares on social media. This enables progress management based on the user's social media activity. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input the user's social media activity data into AI and have the AI ​​make progress management suggestions.

[0099] The introduction unit can estimate the user's emotions and adjust how it introduces other users based on those emotions. For example, if the user is nervous, the introduction unit can provide a friendly introduction. For example, if the user is relaxed, the introduction unit can provide an introduction that includes detailed information. Also, if the user is in a hurry, the introduction unit can provide a concise introduction. This allows the introduction of other users to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the introduction unit can input user emotion data into the generative AI and have the generative AI adjust how it introduces other users.

[0100] The referral system can suggest the most suitable referral method when referring other users, by referring to the user's past interaction history. For example, the referral system can suggest relevant new users based on the trends of users the user has interacted with in the past. For example, the referral system can analyze the trends of interaction methods the user has set in the past and suggest users that might be of interest to them. The referral system can also suggest highly-rated users based on the user's evaluations of users they have interacted with in the past. This makes it possible to introduce the most suitable other users based on the user's past interaction history. Some or all of the above processing in the referral system may be performed using AI, for example, or not using AI. For example, the referral system can input the user's interaction history data into AI and have the AI ​​suggest the most suitable referral method.

[0101] The referral function can filter users based on their current lifestyle and areas of interest when recommending other users. For example, the referral function can recommend relevant users based on keywords the user has recently searched for or their browsing history. For example, the referral function can analyze accounts and posts the user follows on social media and recommend users who might be of interest to them. The referral function can also recommend relevant users based on events and activities the user has recently participated in. This makes it possible to recommend other users based on the user's current lifestyle and areas of interest. Some or all of the above processing in the referral function may be performed using AI, for example, or not. For example, the referral function can input data on the user's lifestyle and areas of interest into an AI and have the AI ​​perform the recommendation of other users.

[0102] The referral system can estimate a user's emotions and prioritize other users based on those emotions. For example, if a user is stressed, the referral system will prioritize recommending users who can help them relax. For example, if a user is excited, the referral system can prioritize recommending active users. Similarly, if a user is tired, the referral system can prioritize recommending users who can help them refresh. This allows for the prioritization of other users to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the referral system can input user emotion data into the generative AI and have the generative AI determine the priority of other users.

[0103] The referral unit can prioritize recommending highly relevant users by considering the user's geographical location when recommending other users. For example, the referral unit can prioritize recommending users who are close to the user's current location. For example, the referral unit can prioritize recommending users who are related to places the user plans to visit. It can also prioritize recommending users who are near places the user has visited in the past. This makes it possible to recommend other users based on the user's geographical location. Some or all of the above processing in the referral unit may be performed using AI, for example, or not using AI. For example, the referral unit can input the user's geographical location data into AI and have the AI ​​perform the recommendation of other users.

[0104] The referral unit can analyze a user's social media activity and recommend relevant users when referring other users. For example, the referral unit can recommend relevant users based on the accounts and posts a user follows on social media. For example, the referral unit can recommend relevant users based on the groups and events a user participates in on social media. The referral unit can also recommend relevant users based on the content a user shares on social media. This makes it possible to recommend other users based on a user's social media activity. Some or all of the above processing in the referral unit may be performed using AI, for example, or not using AI. For example, the referral unit can input a user's social media activity data into an AI and have the AI ​​perform the recommendation of other users.

[0105] The integration unit can estimate the user's emotions and adjust the display method of integration information based on the estimated user emotions. For example, if the user is tense, the integration unit can provide a simple and highly visible display method. For example, if the user is relaxed, the integration unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the integration unit can provide a display method that gets straight to the point. This allows the display method of integration information to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the integration unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the integration information.

[0106] The integration unit can propose the optimal integration method by referring to the user's past integration history when providing integration information. For example, the integration unit can propose new relevant integration methods based on the trends of information the user has previously integrated. For example, the integration unit can analyze the trends of integration methods the user has previously set and propose integration methods that might be of interest. The integration unit can also propose highly-rated integration methods based on the user's evaluation of information they have previously integrated. This allows the integration unit to propose the optimal integration method based on the user's past integration history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's integration history data into AI and have the AI ​​propose the optimal integration method.

[0107] The integration unit can estimate the user's emotions and determine the priority of integration information based on the estimated user emotions. For example, if the user is stressed, the integration unit can prioritize providing relaxing integration information. For example, if the user is excited, the integration unit can prioritize providing active integration information. Also, if the user is tired, the integration unit can prioritize providing refreshing integration information. This allows for the determination of integration information priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the integration unit can input user emotion data into the generative AI and have the generative AI determine the priority of integration information.

[0108] The integration unit can prioritize providing highly relevant information by considering the user's geographical location when providing integration information. For example, the integration unit can prioritize providing integration information related to locations close to the user's current location. For example, the integration unit can prioritize providing integration information related to places the user plans to visit. The integration unit can also prioritize providing integration information related to places the user has visited in the past. This allows for the provision of integration information based on the user's geographical location. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's geographical location data into AI and have AI perform the provision of integration information.

[0109] The cost management unit can estimate the user's emotions and adjust the cost management method based on the estimated emotions. For example, if the user is stressed, the cost management unit can provide a simple and easy-to-understand cost management method. For example, if the user is relaxed, the cost management unit can provide a cost management method that includes detailed information. Furthermore, if the user is in a hurry, the cost management unit can provide a cost management method that gets straight to the point. This allows the cost management method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the cost management unit can input user emotion data into the generative AI and have the generative AI adjust the cost management method.

[0110] The cost management department can propose the optimal cost management method by referring to the user's past spending history during cost management. For example, the cost management department can propose new relevant cost management methods based on trends in items the user has spent on in the past. For example, the cost management department can analyze trends in cost management methods previously set by the user and propose cost management methods that might be of interest. Furthermore, the cost management department can propose highly-rated cost management methods based on evaluations of items the user has spent on in the past. This allows for the proposal of the optimal cost management method based on the user's past spending history. Some or all of the above processes in the cost management department may be performed using AI, for example, or not. For example, the cost management department can input the user's spending history data into AI and have the AI ​​propose the optimal cost management method.

[0111] The cost management unit can estimate the user's emotions and determine cost management priorities based on those estimated emotions. For example, if the user is stressed, the cost management unit can prioritize providing relaxing cost management methods. For example, if the user is excited, the cost management unit can prioritize providing active cost management methods. Also, if the user is tired, the cost management unit can prioritize providing refreshing cost management methods. This allows for the determination of cost management priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the cost management unit can input user emotion data into the generative AI and have the generative AI determine the cost management priorities.

[0112] The cost management unit can prioritize the management of highly relevant costs by considering the user's geographical location information during cost management. For example, the cost management unit can prioritize costs related to locations close to the user's current location. For example, the cost management unit can prioritize costs related to places the user plans to visit. It can also prioritize costs related to places the user has visited in the past. This enables cost management based on the user's geographical location information. Some or all of the above processing in the cost management unit may be performed using AI, for example, or not using AI. For example, the cost management unit can input the user's geographical location information data into AI and have the AI ​​execute cost management suggestions.

[0113] The report generation unit can estimate the user's emotions and adjust the report's presentation based on those emotions. For example, if the user is stressed, the report generation unit can provide a simple and easy-to-read report. For example, if the user is relaxed, the report generation unit can provide a report with detailed information. Furthermore, if the user is in a hurry, the report generation unit can provide a concise report. This allows the report's presentation to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the report generation unit can input user emotion data into the generative AI and have the generative AI adjust the report's presentation.

[0114] The report generation unit can generate the most suitable report by referring to the user's past visit history when generating a report. For example, the report generation unit can generate a new, relevant report based on places and themes the user has visited in the past. For example, the report generation unit can analyze the trends of reports the user has previously set and generate reports that are likely to be of interest. The report generation unit can also generate highly rated reports based on the user's ratings of places they have visited in the past. This allows for the generation of the most suitable report based on the user's past visit history. Some or all of the above processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input the user's visit history data into AI and have the AI ​​generate the most suitable report.

[0115] The report generation unit can estimate the user's emotions and determine the priority of reports based on the estimated emotions. For example, if the user is stressed, the report generation unit can prioritize providing relaxing reports. For example, if the user is excited, the report generation unit can prioritize providing active reports. Also, if the user is tired, the report generation unit can prioritize providing refreshing reports. This allows for the prioritization of reports according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the report generation unit can input user emotion data into the generative AI and have the generative AI determine the priority of reports.

[0116] The report generation unit can prioritize generating highly relevant reports by considering the user's geographical location information during report generation. For example, the report generation unit can prioritize generating reports related to locations close to the user's current location. For example, the report generation unit can prioritize generating reports related to places the user plans to visit. It can also prioritize generating reports related to locations near places the user has visited in the past. This enables the generation of reports based on the user's geographical location information. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input the user's geographical location data into AI and have the AI ​​perform the report generation.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] The suggestion function can estimate the user's emotions and adjust the suggested visit plan based on those emotions. For example, if the user is feeling stressed, it can suggest relaxing destinations. Specifically, it can suggest relaxing places such as hot springs or nature parks. If the user is excited, it can suggest active destinations. For example, it can suggest active places such as adventure sports or theme parks. Furthermore, if the user is tired, it can suggest refreshing destinations. For example, it can suggest refreshing places such as relaxation facilities or cafe hopping. This allows the system to propose the optimal visit plan tailored to the user's emotions.

[0119] The suggestion function can analyze a user's past visit history and propose the optimal visit plan. For example, it can suggest new destinations based on places and themes the user has visited in the past. Specifically, it can suggest castles the user has not yet visited based on a list of castles the user has visited in the past. It can also analyze trends in the visit plans the user has set in the past and suggest destinations that might interest them. For example, it can suggest highly-rated ramen restaurants based on the user's ratings of ramen restaurants they have visited in the past. In this way, it can propose the optimal visit plan based on the user's past visit history.

[0120] The tracking unit can estimate the user's emotions and adjust the progress display method based on the estimated emotions. For example, if the user is stressed, it can provide a simple and highly visible display method. Specifically, it can display the progress concisely so that the user can understand it immediately. If the user is relaxed, it can also provide a display method that includes detailed information. For example, it can display detailed information and photos of the visited location. Furthermore, if the user is in a hurry, it can provide a concise display method. For example, it can highlight information about the next visited location. In this way, the progress display method can be adjusted according to the user's emotions.

[0121] The referral function can analyze a user's past interaction history and recommend the most suitable other users. For example, it can recommend relevant new users based on the trends of users the user has interacted with in the past. Specifically, it can recommend highly-rated users based on the ratings the user has given to users they have interacted with in the past. It can also analyze the trends in the interaction methods the user has set in the past and recommend users who are likely to be interested in them. For example, it can recommend users who have participated in related events based on the trends of events the user has attended in the past. This makes it possible to recommend the most suitable other users based on the user's past interaction history.

[0122] The integration unit can estimate the user's emotions and adjust how integration information is displayed based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible display method. Specifically, it can display public transport timetables and accommodation information concisely so that the user can understand it immediately. If the user is relaxed, it can also provide a display method that includes detailed information. For example, it can display detailed information and photos of accommodations. Furthermore, if the user is in a hurry, it can provide a display method that focuses on the essentials. For example, it can highlight information about the next destination. In this way, the way integration information is displayed can be adjusted according to the user's emotions.

[0123] The cost management department can analyze a user's past spending history and propose the most suitable cost management method. For example, it can suggest new cost management methods based on trends in items the user has spent money on in the past. Specifically, it can suggest new places that the user can visit within their budget based on the costs of places they have visited in the past. It can also analyze trends in cost management methods the user has previously set and suggest cost management methods that they might be interested in. For example, it can suggest highly-rated accommodations based on the user's ratings of accommodations they have used in the past. In this way, it can propose the most suitable cost management method based on the user's past spending history.

[0124] The report generation unit can estimate the user's emotions and adjust the report's presentation based on those emotions. For example, if the user is nervous, it can provide a simple and highly visual report. Specifically, it can concisely summarize information about the visited location so that the user can understand it immediately. If the user is relaxed, it can provide a report with detailed information. For example, it can provide a report that includes detailed information and photos of the visited location. Furthermore, if the user is in a hurry, it can provide a report that gets straight to the point. For example, it can highlight information about the next visited location. In this way, the report's presentation can be adjusted according to the user's emotions.

[0125] The suggestion function can propose travel plans based on the user's current lifestyle and areas of interest. For example, it can suggest relevant destinations based on keywords the user has recently searched for and their browsing history. Specifically, it can suggest relevant destinations based on tourist spots and events the user has recently searched for. It can also analyze accounts and posts the user follows on social media and suggest destinations that might interest them. For example, it can suggest relevant destinations based on posts from travel bloggers the user follows. This allows for the proposal of optimal travel plans based on the user's current lifestyle and areas of interest.

[0126] The tracking unit can manage progress while taking the user's geographical location into consideration. For example, it can prioritize tracking progress related to locations close to the user's current location. Specifically, it can prioritize tracking progress for nearby tourist destinations or events. It can also prioritize tracking progress related to places the user plans to visit. For example, it can prioritize tracking progress for the next tourist destination the user plans to visit. Furthermore, it can prioritize tracking progress related to locations near places the user has visited in the past. For example, it can prioritize tracking progress for new tourist destinations near previously visited tourist destinations. This enables optimal progress management based on the user's geographical location.

[0127] The introduction section can estimate the user's emotions and adjust how other users are introduced based on those emotions. For example, if the user is nervous, a friendly introduction can be provided. Specifically, the profiles of other users can be summarized concisely so that the user can understand them immediately. If the user is relaxed, a more detailed introduction can be provided. For example, an introduction that includes the detailed profile and photos of other users can be provided. Furthermore, if the user is in a hurry, a concise introduction can be provided. For example, important information about other users can be highlighted. In this way, the introduction of other users can be adjusted according to the user's emotions.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The reception desk sets the theme the user wants to visit. For example, the user can set a theme such as "I want to visit Japanese castles." Step 2: The proposal department proposes a visit plan based on the theme received by the reception department. The proposal department proposes a visit plan by comprehensively considering factors such as the user's current date and time, work schedule, realistic travel distance and means of transportation, and budget. For example, the proposal department may suggest castles that the user can visit on the weekend, or it may be able to create an efficient travel plan by linking public transport timetables and accommodation information. Step 3: The tracking unit tracks the user's progress based on the visit plan proposed by the suggestion unit. For example, the tracking unit records the places the user has visited, lists unvisited places, and suggests the next places to visit. This makes it easier for the user to understand their progress toward achieving their goals and maintain their motivation. Step 4: The referral team refers other users following the same topic based on the progress tracked by the tracking team. The referral team supports users in exchanging information and conducting joint visits, for example. This allows users to exchange information and conduct joint visits, promoting community building and information sharing.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0132] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the reception unit, proposal unit, tracking unit, and referral unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the user sets the theme they wish to visit. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where it proposes a visit plan based on a comprehensive assessment of the user's current date and time, work schedule, travel distance and means of transportation, and budget. The tracking unit is implemented, for example, by the control unit 46A of the smart device 14, where it tracks the user's progress and records the places they have visited. The referral unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where it introduces other users pursuing the same theme. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the reception unit, proposal unit, tracking unit, and referral unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, which sets the theme the user wants to visit. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which comprehensively determines the user's current date and time, work schedule, travel distance and means of transportation, budget, etc., and proposes a visit plan. The tracking unit is implemented by the control unit 46A of the smart glasses 214, which tracks the user's progress and records the places visited. The referral unit is implemented by the specific processing unit 290 of the data processing unit 12, which introduces other users pursuing the same theme. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the reception unit, proposal unit, tracking unit, and referral unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the user sets the theme they wish to visit. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where it proposes a visit plan based on a comprehensive assessment of the user's current date and time, work schedule, travel distance and means of transportation, and budget. The tracking unit is implemented by, for example, the control unit 46A of the headset terminal 314, where it tracks the user's progress and records the places they have visited. The referral unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where it introduces other users pursuing the same theme. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0168] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0169] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0170] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0172] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0173] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0174] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0175] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0176] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0177] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0178] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0179] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0181] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0182] Each of the multiple elements described above, including the reception unit, proposal unit, tracking unit, and introduction unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, which sets the theme the user wishes to visit. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes a visit plan by comprehensively determining the user's current date and time, work schedule, travel distance and means of transportation, and budget. The tracking unit is implemented by, for example, the control unit 46A of the robot 414, which tracks the user's progress and records the places visited. The introduction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which introduces other users pursuing the same theme. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0183] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0184] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0185] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0186] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0187] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0188] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0189] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0190] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0191] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0192] 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.

[0193] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0194] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0195] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0196] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0197] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0198] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0199] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0200] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0201] (Note 1) The reception desk accepts theme requests, A proposal department proposes a visit plan based on the themes received by the aforementioned reception department, A tracking unit tracks the user's progress based on the visit plan proposed by the aforementioned proposal unit, The system includes a referral unit that introduces other users following the same theme based on the progress tracked by the aforementioned tracking unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, The system proposes a visit plan based on a comprehensive assessment of the user's current date and time, work schedule, realistic travel distance and mode of transport, and budget. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned tracking unit is It records the places the user has visited, lists unvisited places, and suggests the next place to visit. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned introduction section is, We introduce other users pursuing the same topic, supporting information exchange and collaborative visits. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a coordinating department that links public transportation timetables and accommodation information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The company has a cost management department that tracks the costs of each visit and manages the total expenses. The system described in Appendix 1, characterized by the features described herein. (Note 7) It includes a report generation unit that generates a visit report after each visit and collects feedback. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and suggests theme settings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It analyzes the user's past theme settings and suggests the most suitable theme. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When setting a theme, filtering is performed based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates user sentiment and determines theme priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When selecting a theme, the system prioritizes suggesting themes that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When setting a theme, we analyze the user's social media activity and suggest relevant themes. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, The system estimates the user's emotions and adjusts how the visit plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When proposing a visit plan, we refer to the user's past visit history to suggest the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When proposing a visit plan, filtering is performed based on the user's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, The system estimates the user's emotions and prioritizes visit plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When proposing a visit plan, the system prioritizes suggesting highly relevant plans by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When proposing a visit plan, we analyze the user's social media activity and propose a relevant plan. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned tracking unit is It estimates the user's emotions and adjusts how progress is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned tracking unit is When tracking progress, we refer to the user's past visit history to suggest the optimal progress management method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned tracking unit is When tracking progress, filtering is performed based on the user's current life situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned tracking unit is It estimates the user's emotions and prioritizes progress based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned tracking unit is When tracking progress, the system prioritizes tracking the most relevant progress by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned tracking unit is When tracking progress, analyze users' social media activity and track relevant progress. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned introduction section is, It estimates the user's emotions and adjusts how other users are introduced based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned introduction section is, When introducing other users, we refer to the user's past interaction history to suggest the most suitable introduction method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned introduction section is, When introducing other users, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned introduction section is, It estimates the user's emotions and determines the priority of other users based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned introduction section is, When introducing other users, the system prioritizes recommending highly relevant users by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned introduction section is, When recommending other users, we analyze the user's social media activity and recommend relevant users. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned linkage unit is, It estimates the user's emotions and adjusts how linked information is displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned linkage unit is, When providing integration information, we will refer to the user's past integration history to suggest the most suitable integration method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned linkage unit is, It estimates the user's emotions and prioritizes linked information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned linkage unit is, When providing linked information, the system prioritizes providing highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned cost control department, We estimate user sentiment and adjust cost management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned cost control department, When managing costs, we refer to the user's past spending history to suggest the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned cost control department, Estimate user sentiment and determine cost management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned cost control department, When managing costs, prioritize and manage highly relevant costs by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 40) The report generation unit, It estimates user sentiment and adjusts the way reports are presented based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 41) The report generation unit, When generating reports, the system references the user's past visit history to generate the most suitable reports. The system described in Appendix 1, characterized by the features described herein. (Note 42) The report generation unit, It estimates user sentiment and prioritizes reports based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 43) The report generation unit, When generating reports, the system prioritizes generating highly relevant reports by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0202] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk accepts theme requests, A proposal department proposes a visit plan based on the themes received by the aforementioned reception department, A tracking unit tracks the user's progress based on the visit plan proposed by the aforementioned proposal unit, The system includes a referral unit that introduces other users following the same theme based on the progress tracked by the aforementioned tracking unit. A system characterized by the following features.

2. The aforementioned proposal section is, The system proposes a visit plan based on a comprehensive assessment of the user's current date and time, work schedule, realistic travel distance and mode of transport, and budget. The system according to feature 1.

3. The aforementioned tracking unit is It records the places the user has visited, lists unvisited places, and suggests the next place to visit. The system according to feature 1.

4. The aforementioned introduction section is, We introduce other users pursuing the same topic, supporting information exchange and collaborative visits. The system according to feature 1.

5. It includes a coordinating department that links public transportation timetables and accommodation information. The system according to feature 1.

6. The company has a cost management department that tracks the costs of each visit and manages the total expenses. The system according to feature 1.

7. It includes a report generation unit that generates a visit report after each visit and collects feedback. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and suggests theme settings based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is It analyzes the user's past theme settings and suggests the most suitable theme. The system according to feature 1.

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