System

Abstract

An object of the system according to the embodiment is to generate an optimal plan based on a desire of a user, list necessary tools and spots, and participate in a related community.SOLUTION: A system includes a desire analysis part, a plan generation part, a list generation part, and a community participation part. The desire analysis unit analyzes a desire of a user. The plan generation part generates an optimum plan on the basis of the desire analyzed by the desire analysis part. A list generation part lists necessary tools or spots based on the desire analyzed by the desire analysis part. The community participation unit participates in a community related to the request, and performs matching with a person having the same hobby.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to generate optimal plans based on users' preferences, list necessary equipment and spots, and join related communities.

[0005] The system according to the embodiment aims to generate an optimal plan based on the user's wishes, list the necessary tools and spots, and allow the user to join related communities. [Means for solving the problem]

[0006] The system according to the embodiment includes a desire analysis unit, a plan creation unit, a list creation unit, and a community participation unit. The desire analysis unit analyzes the user's desires. The plan creation unit creates an optimal plan based on the desires analyzed by the desire analysis unit. The list creation unit lists necessary tools or spots based on the desires analyzed by the desire analysis unit. The community participation unit joins a community related to the desires and matches the user with people who share the same hobbies. [Effects of the Invention]

[0007] The system according to the embodiment generates an optimal plan based on the user's wishes, lists the necessary tools and spots, and allows the user to join related communities. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​application according to an embodiment of the present invention is a system that analyzes a user's preferences, automatically generates optimal plans and lists, and even supports related procedures and community participation. This allows the AI ​​application to efficiently and accurately fulfill the user's preferences. For example, by generating a travel plan, the user can easily create the optimal travel plan. Furthermore, by listing hobby tools and spots, the user can easily obtain the necessary information and complete the procedures smoothly. Furthermore, by participating in communities and matching with people who share the same hobbies, the user can gain new opportunities for interaction.

[0029] An AI app according to an embodiment includes a desire analysis unit, a plan generation unit, a list generation unit, and a community participation unit. The desire analysis unit analyzes a user's desires. For example, if a user inputs "I want to travel to Kyoto," the desire analysis unit analyzes the desire and collects information about the trip. Furthermore, if a user inputs "I want to take up fishing," the desire analysis unit analyzes the desire and collects information about fishing. The plan generation unit generates an optimal plan based on the desires analyzed by the desire analysis unit. For example, the plan generation unit automatically creates a travel plan including recommended travel times, spots, accommodations, etc. based on tourist information about Kyoto. Furthermore, the plan generation unit lists fishing equipment needed and recommended spots in the user's area, and provides the list to the user. The list generation unit lists necessary equipment and spots based on the desires analyzed by the desire analysis unit. For example, the list generation unit lists fishing equipment needed and recommended spots in the user's area, and provides the list to the user. Furthermore, the list generation unit lists travel equipment and tourist spots, and provides the list to the user. The community participation unit joins a community related to the user's preferences and matches the user with people who share the same hobbies. For example, the community participation unit can join a fishing community and interact with other fishing enthusiasts. Alternatively, the community participation unit can join a travel community and interact with other travel enthusiasts. As a result, the AI ​​app according to the embodiment can generate optimal plans and lists based on the user's preferences and support the user with related procedures and community participation.

[0030] The preference analysis unit can analyze the user's past behavioral history or preferences to generate a more personalized plan. The preference analysis unit, for example, collects data on places the user has visited or events the user has participated in in the past and customizes the travel plan based on that data. For example, it can reflect preferences for tourist spots visited in the past. The preference analysis unit can also analyze the user's past search history and purchase history to suggest plans that match their preferences. For example, it can utilize information from travel guidebooks purchased in the past and accommodations booked in the past. The preference analysis unit can also incorporate spots and events that the user may be interested in into the plan based on the user's activity history in communities and forums they have participated in in the past. For example, it can reflect information about fishing events they have participated in in the past. This makes it possible to provide a more personalized plan based on the user's past behavioral history and preferences.

[0031] The desire analysis unit allows the generation AI to suggest additional information related to the user's input, thereby improving the level of detail of the plan. For example, if a user inputs, "I want to travel to Kyoto," the desire analysis unit will suggest additional information about seasonal events and local specialties in Kyoto. For example, it will suggest cherry blossom viewing spots during cherry blossom season. If a user inputs, "I want to start fishing," the desire analysis unit will suggest basic fishing knowledge and guides for beginners. For example, it will include basic fishing techniques and precautions. If a user inputs, "I want to go mountain climbing," the desire analysis unit will suggest additional information about the equipment and safety measures needed for mountain climbing. For example, it will provide information about mountain climbing routes and weather information. This allows the generation AI to suggest additional information related to the user's input, thereby improving the level of detail of the plan.

[0032] The plan generation unit can generate multiple options for a plan desired by the user and allow the user to select from them. For example, if the user inputs, "I want to travel to Kyoto," the plan generation unit's generation AI will propose multiple travel plans and allow the user to select from them. For example, it can provide plans that emphasize sightseeing and plans that emphasize gourmet food. If the user inputs, "I want to take up fishing," the plan generation unit can propose a list of multiple fishing spots and equipment and allow the user to select from them. For example, it can provide spots for beginners and spots for advanced hikers. If the user inputs, "I want to go mountain climbing," the plan generation unit can propose a list of multiple mountain climbing routes and equipment and allow the user to select from them. For example, it can provide routes by difficulty level and equipment for each season. This allows the user to select from multiple options.

[0033] When generating a plan, the plan generation unit can provide a more reliable plan by referring to reviews or ratings from other users. For example, if a user inputs, "I want to travel to Kyoto," the plan generation unit's generation AI refers to reviews and ratings from other users to suggest reliable tourist spots and accommodations. For example, it prioritizes selecting highly rated tourist spots and hotels. Also, if a user inputs, "I want to take up fishing," the plan generation unit's generation AI refers to reviews and ratings from other users to suggest reliable fishing spots and equipment. For example, it selects popular fishing tackle manufacturers and fishing locations. Also, if a user inputs, "I want to go mountain climbing," the plan generation unit's generation AI refers to reviews and ratings from other users to suggest reliable mountain climbing routes and equipment. For example, it selects routes and equipment that are highly rated by experienced users. This allows the plan generation unit to provide a reliable plan by referring to reviews and ratings from other users.

[0034] The list generation unit can customize and list the optimal equipment or spots according to the user's budget and preferences. For example, if a user inputs "I want to start fishing," the list generation unit generates a list of optimal fishing equipment according to the user's budget and preferences. For example, it suggests fishing rods and reels that can be purchased within the user's budget. If a user inputs "I want to go mountain climbing," the list generation unit generates a list of optimal mountain climbing equipment according to the user's budget and preferences. For example, it suggests mountain climbing boots and backpacks that can be purchased within the user's budget. If a user inputs "I want to go camping," the list generation unit generates a list of optimal camping equipment according to the user's budget and preferences. For example, it suggests tents and sleeping bags that can be purchased within the user's budget. This allows the list of optimal equipment and spots to be customized and generated according to the user's budget and preferences.

[0035] The list generation unit allows the generation AI to provide detailed explanations and instructions for use for listed tools or spots. For example, if a user inputs "I want to start fishing," the list generation unit provides detailed explanations and instructions for use for listed fishing tools. For example, it explains how to use a fishing rod and how to adjust a reel. Also, if a user inputs "I want to go mountain climbing," the list generation unit provides detailed explanations and instructions for use for listed mountain climbing equipment. For example, it explains how to choose mountain climbing boots and how to pack a backpack. Also, if a user inputs "I want to go camping," the list generation unit provides detailed explanations and instructions for use for listed camping tools. For example, it explains how to set up a tent and how to use a sleeping bag. In this way, detailed explanations and instructions for use can be provided for listed tools and spots.

[0036] The list generation unit can display reviews or ratings from other users for the listed equipment or spots to help with selection. For example, when a user checks a list of fishing equipment, the list generation unit displays reviews and ratings from other users to help with selection. For example, highly rated fishing rods and reels are displayed preferentially. Furthermore, when a user checks a list of mountain climbing equipment, the list generation unit displays reviews and ratings from other users to help with selection. For example, highly rated mountain climbing boots and backpacks are displayed preferentially. Furthermore, when a user checks a list of camping equipment, the list generation unit displays reviews and ratings from other users to help with selection. For example, highly rated tents and sleeping bags are displayed preferentially. This allows the user to refer to reviews and ratings from other users to help with selection.

[0037] The list generation unit can automatically provide event or campaign information related to the listed equipment or spots. For example, when a user checks a list of fishing equipment, the list generation unit automatically provides related fishing event or campaign information. For example, fishing tournament or sale information is displayed. Furthermore, when a user checks a list of mountain climbing equipment, the list generation unit automatically provides related mountain climbing event or campaign information. For example, mountain climbing tour or discount information is displayed. Furthermore, when a user checks a list of camping equipment, the list generation unit automatically provides related camping event or campaign information. For example, camping festival or promotion information is displayed. In this way, related event and campaign information can be automatically provided.

[0038] The community participation module can suggest optimal communities or matching partners based on the user's hobbies and interests. For example, if a user inputs "I want to start fishing," the generation AI analyzes the user's hobbies and interests and suggests optimal fishing communities. For example, it can introduce fishing clubs for beginners and local fishing events. If a user inputs "I want to go mountain climbing," the generation AI analyzes the user's hobbies and interests and suggests optimal mountain climbing communities. For example, it can introduce local mountain climbing circles and online forums. If a user inputs "I want to go camping," the generation AI analyzes the user's hobbies and interests and suggests optimal camping communities. For example, it can introduce family camping groups and gatherings of solo camping enthusiasts. This allows the generation AI to suggest optimal communities and matching partners based on the user's hobbies and interests.

[0039] The community participation unit analyzes the user's activity history within the community and can suggest more suitable matching partners. For example, the community participation unit analyzes the user's activity history within a fishing community and suggests other users with common interests. For example, it matches users who are interested in the same fishing spots. The community participation unit also analyzes the user's activity history within a mountain climbing community and suggests other users who are interested in a common mountain climbing route. For example, it matches users who have experience climbing the same mountain. The community participation unit also analyzes the user's activity history within a camping community and suggests other users who are interested in a common camping style. For example, it matches users who have used the same campsite. In this way, it is possible to analyze the activity history within the community and suggest more suitable matching partners.

[0040] The community participation unit can automatically suggest events or activities within the community, encouraging user participation. For example, when a user joins a fishing community, the generation AI automatically suggests fishing events and activities. For example, it can introduce local fishing tournaments and fishing classes. Furthermore, when a user joins a mountain climbing community, the generation AI automatically suggests mountain climbing events and activities. For example, it can introduce group mountain climbing and mountain climbing seminars. Furthermore, when a user joins a camping community, the generation AI automatically suggests camping events and activities. For example, it can introduce camping festivals and workshops. In this way, events and activities within the community can be automatically suggested, encouraging user participation.

[0041] The community participation unit can provide a chat function or forum to promote interaction between users within the community. For example, the community participation unit provides a chat function or forum so that users can interact with other users within a fishing community. For example, a forum can be provided for discussing fishing techniques and recommended spots. The community participation unit also provides a chat function or forum so that users can interact with other users within a mountain climbing community. For example, a forum can be provided for exchanging information about mountain climbing routes and equipment. The community participation unit also provides a chat function or forum so that users can interact with other users within a camping community. For example, a forum can be provided for discussing camping tips and recommended campsites. In this way, it is possible to provide a chat function or forum to promote interaction between users within the community.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The desire analysis unit allows the generation AI to suggest additional information related to the user's input, improving the level of detail of the plan. For example, if a user inputs, "I want to travel to Kyoto," the generation AI will suggest additional information about seasonal events and local specialties in Kyoto. For example, it will suggest cherry blossom viewing spots during cherry blossom season. If a user inputs, "I want to start fishing," the desire analysis unit will suggest basic fishing knowledge and guides for beginners. For example, it will include basic fishing techniques and precautions. If a user inputs, "I want to go mountain climbing," the desire analysis unit will suggest additional information about the equipment and safety measures needed for mountain climbing. For example, it will provide information on mountain climbing routes and weather information. This allows the generation AI to suggest additional information related to the user's input, improving the level of detail of the plan.

[0044] The plan generation unit can generate multiple options for a plan desired by the user and allow the user to select from them. For example, if a user inputs, "I want to travel to Kyoto," the generation AI will propose multiple travel plans from which the user can choose. For example, it will provide plans that emphasize sightseeing and plans that emphasize gourmet food. Also, if a user inputs, "I want to take up fishing," the plan generation unit will propose a list of multiple fishing spots and equipment from which the user can choose. For example, it will provide spots for beginners and spots for advanced hikers. Also, if a user inputs, "I want to go mountain climbing," the plan generation unit will propose a list of multiple mountain climbing routes and equipment from which the user can choose. For example, it will provide routes by difficulty level and equipment for each season. This allows the user to choose from multiple options.

[0045] When generating a plan, the plan generation unit can provide a more reliable plan by referring to reviews or ratings from other users. For example, if a user inputs, "I want to travel to Kyoto," the generation AI refers to the reviews and ratings from other users and suggests reliable tourist spots and accommodations. For example, it prioritizes the selection of highly rated tourist spots and hotels. Also, if a user inputs, "I want to take up fishing," the plan generation unit can refer to the reviews and ratings from other users and suggest reliable fishing spots and equipment. For example, it can select popular fishing tackle manufacturers and fishing locations. Also, if a user inputs, "I want to go mountain climbing," the plan generation unit can refer to the reviews and ratings from other users and suggest reliable mountain climbing routes and equipment. For example, it can select routes and equipment that are highly rated by experienced users. This allows the generation AI to provide a reliable plan by referring to the reviews and ratings from other users.

[0046] The list generation unit can customize and list the optimal equipment or spots according to the user's budget and preferences. For example, if a user inputs "I want to start fishing," the generation AI will list the optimal fishing equipment according to the user's budget and preferences. For example, it will suggest fishing rods and reels that can be purchased within the user's budget. If a user inputs "I want to go mountain climbing," the list generation unit will list the optimal mountain climbing equipment according to the user's budget and preferences. For example, it will suggest mountain climbing boots and backpacks that can be purchased within the user's budget. If a user inputs "I want to go camping," the list generation unit will list the optimal camping equipment according to the user's budget and preferences. For example, it will suggest tents and sleeping bags that can be purchased within the user's budget. This allows the list of optimal equipment and spots to be customized and listed according to the user's budget and preferences.

[0047] The list generation unit allows the generation AI to provide detailed explanations and instructions for use for listed tools or spots. For example, if a user inputs "I want to start fishing," the generation AI provides detailed explanations and instructions for use for listed fishing tools. For example, it explains how to use a fishing rod and how to adjust a reel. If a user inputs "I want to go mountain climbing," the list generation unit provides detailed explanations and instructions for use for listed mountain climbing equipment. For example, it explains how to choose mountain climbing boots and how to pack a backpack. If a user inputs "I want to go camping," the list generation unit provides detailed explanations and instructions for use for listed camping tools. For example, it explains how to set up a tent and how to use a sleeping bag. This allows detailed explanations and instructions for use to be provided for listed tools and spots.

[0048] The community participation unit can provide a chat function or forum to promote interaction between users within the community. For example, a chat function or forum can be provided so that a user can interact with other users within a fishing community. For example, a forum can be provided to discuss fishing techniques and recommended spots. The community participation unit can also provide a chat function or forum so that a user can interact with other users within a mountain climbing community. For example, a forum can be provided to exchange information about mountain climbing routes and equipment. The community participation unit can also provide a chat function or forum so that a user can interact with other users within a camping community. For example, a forum can be provided to discuss camping tips and recommended campsites. In this way, a chat function or forum can be provided to promote interaction between users within the community.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The desire analysis unit analyzes the user's desires. For example, if the user inputs "I want to go on a trip to Kyoto," the desire analysis unit analyzes the desire and collects information about the trip. Similarly, if the user inputs "I want to take up fishing," the desire analysis unit analyzes the desire and collects information about fishing. Step 2: The plan generation unit generates an optimal plan based on the desires analyzed by the desire analysis unit. For example, the plan generation unit automatically creates a travel plan based on tourist information about Kyoto, including recommended times, spots, and accommodations. It also lists fishing equipment and recommended spots in the area and provides them to the user. Step 3: The list generator creates a list of necessary tools and spots based on the desires analyzed by the desire analysis unit. For example, the list generator creates a list of tools needed for fishing and recommended spots in the area where the user lives, and provides it to the user. It also creates a list of tools needed for travel and tourist spots, and provides it to the user. Step 4: The community participation section joins communities related to your interests and matches you with people who share the same hobbies. For example, you can join a fishing community to interact with other fishing enthusiasts, or join a travel community to interact with other travel enthusiasts.

[0051] (Example 2) The AI ​​application according to an embodiment of the present invention is a system that analyzes a user's preferences, automatically generates optimal plans and lists, and even supports related procedures and community participation. This allows the AI ​​application to efficiently and accurately fulfill the user's preferences. For example, by generating a travel plan, the user can easily create the optimal travel plan. Furthermore, by listing hobby tools and spots, the user can easily obtain the necessary information and complete the procedures smoothly. Furthermore, by participating in communities and matching with people who share the same hobbies, the user can gain new opportunities for interaction.

[0052] An AI app according to an embodiment includes a desire analysis unit, a plan generation unit, a list generation unit, and a community participation unit. The desire analysis unit analyzes a user's desires. For example, if a user inputs "I want to travel to Kyoto," the desire analysis unit analyzes the desire and collects information about the trip. Furthermore, if a user inputs "I want to take up fishing," the desire analysis unit analyzes the desire and collects information about fishing. The plan generation unit generates an optimal plan based on the desires analyzed by the desire analysis unit. For example, the plan generation unit automatically creates a travel plan including recommended travel times, spots, accommodations, etc. based on tourist information about Kyoto. Furthermore, the plan generation unit lists fishing equipment needed and recommended spots in the user's area, and provides the list to the user. The list generation unit lists necessary equipment and spots based on the desires analyzed by the desire analysis unit. For example, the list generation unit lists fishing equipment needed and recommended spots in the user's area, and provides the list to the user. Furthermore, the list generation unit lists travel equipment and tourist spots, and provides the list to the user. The community participation unit joins a community related to the user's preferences and matches the user with people who share the same hobbies. For example, the community participation unit can join a fishing community and interact with other fishing enthusiasts. Alternatively, the community participation unit can join a travel community and interact with other travel enthusiasts. As a result, the AI ​​app according to the embodiment can generate optimal plans and lists based on the user's preferences and support the user with related procedures and community participation.

[0053] The preference analysis unit can analyze the user's past behavioral history or preferences to generate a more personalized plan. The preference analysis unit, for example, collects data on places the user has visited or events the user has participated in in the past and customizes the travel plan based on that data. For example, it can reflect preferences for tourist spots visited in the past. The preference analysis unit can also analyze the user's past search history and purchase history to suggest plans that match their preferences. For example, it can utilize information from travel guidebooks purchased in the past and accommodations booked in the past. The preference analysis unit can also incorporate spots and events that the user may be interested in into the plan based on the user's activity history in communities and forums they have participated in in the past. For example, it can reflect information about fishing events they have participated in in the past. This makes it possible to provide a more personalized plan based on the user's past behavioral history and preferences.

[0054] The desire analysis unit allows the generation AI to suggest additional information related to the user's input, thereby improving the level of detail of the plan. For example, if a user inputs, "I want to travel to Kyoto," the desire analysis unit will suggest additional information about seasonal events and local specialties in Kyoto. For example, it will suggest cherry blossom viewing spots during cherry blossom season. If a user inputs, "I want to start fishing," the desire analysis unit will suggest basic fishing knowledge and guides for beginners. For example, it will include basic fishing techniques and precautions. If a user inputs, "I want to go mountain climbing," the desire analysis unit will suggest additional information about the equipment and safety measures needed for mountain climbing. For example, it will provide information about mountain climbing routes and weather information. This allows the generation AI to suggest additional information related to the user's input, thereby improving the level of detail of the plan.

[0055] The desire analysis unit can use the emotion estimation function to analyze the emotions expressed by the user at the time of input and generate a plan that matches those emotions. For example, if the user inputs "I want to relax," the desire analysis unit allows the generation AI to analyze the user's emotions and propose a relaxing travel plan. For example, it generates a plan that includes hot springs and resort hotels. If the user inputs "I want to be adventurous," the desire analysis unit allows the generation AI to analyze the user's emotions and propose a plan that satisfies the adventurous spirit. For example, it generates a plan that includes adventure tours and outdoor activities. If the user inputs "I want to be healed," the desire analysis unit allows the generation AI to analyze the user's emotions and propose a plan that provides healing. For example, it generates a plan that includes places rich in nature and relaxation facilities. This makes it possible to provide a plan that matches the user's emotions.

[0056] The plan generation unit can generate multiple options for a plan desired by the user and allow the user to select from them. For example, if the user inputs, "I want to travel to Kyoto," the plan generation unit's generation AI will propose multiple travel plans and allow the user to select from them. For example, it can provide plans that emphasize sightseeing and plans that emphasize gourmet food. If the user inputs, "I want to take up fishing," the plan generation unit can propose a list of multiple fishing spots and equipment and allow the user to select from them. For example, it can provide spots for beginners and spots for advanced hikers. If the user inputs, "I want to go mountain climbing," the plan generation unit can propose a list of multiple mountain climbing routes and equipment and allow the user to select from them. For example, it can provide routes by difficulty level and equipment for each season. This allows the user to select from multiple options.

[0057] When generating a plan, the plan generation unit can provide a more reliable plan by referring to reviews or ratings from other users. For example, if a user inputs, "I want to travel to Kyoto," the plan generation unit's generation AI refers to reviews and ratings from other users to suggest reliable tourist spots and accommodations. For example, it prioritizes selecting highly rated tourist spots and hotels. Also, if a user inputs, "I want to take up fishing," the plan generation unit's generation AI refers to reviews and ratings from other users to suggest reliable fishing spots and equipment. For example, it selects popular fishing tackle manufacturers and fishing locations. Also, if a user inputs, "I want to go mountain climbing," the plan generation unit's generation AI refers to reviews and ratings from other users to suggest reliable mountain climbing routes and equipment. For example, it selects routes and equipment that are highly rated by experienced users. This allows the plan generation unit to provide a reliable plan by referring to reviews and ratings from other users.

[0058] The plan generation unit uses the emotion estimation function to analyze the user's emotions in real time when selecting a plan and propose an optimal plan. For example, when the user selects from multiple travel plans, the plan generation unit uses the emotion estimation function to analyze the user's emotions in real time and propose an optimal plan. For example, if the user is excited, the plan generation unit proposes an active plan. Furthermore, when the user selects from multiple fishing spots, the plan generation unit uses the emotion estimation function to analyze the user's emotions in real time and propose an optimal spot. For example, if the user is relaxed, the plan generation unit proposes a quiet spot. Furthermore, when the user selects from multiple mountain climbing routes, the plan generation unit uses the emotion estimation function to analyze the user's emotions in real time and propose an optimal route. For example, if the user is feeling adventurous, the plan generation unit proposes a more difficult route. In this way, the optimal plan can be proposed according to the user's emotions.

[0059] The list generation unit can customize and list the optimal equipment or spots according to the user's budget and preferences. For example, if a user inputs "I want to start fishing," the list generation unit generates a list of optimal fishing equipment according to the user's budget and preferences. For example, it suggests fishing rods and reels that can be purchased within the user's budget. If a user inputs "I want to go mountain climbing," the list generation unit generates a list of optimal mountain climbing equipment according to the user's budget and preferences. For example, it suggests mountain climbing boots and backpacks that can be purchased within the user's budget. If a user inputs "I want to go camping," the list generation unit generates a list of optimal camping equipment according to the user's budget and preferences. For example, it suggests tents and sleeping bags that can be purchased within the user's budget. This allows the list of optimal equipment and spots to be customized and generated according to the user's budget and preferences.

[0060] The list generation unit allows the generation AI to provide detailed explanations and instructions for use for listed tools or spots. For example, if a user inputs "I want to start fishing," the list generation unit provides detailed explanations and instructions for use for listed fishing tools. For example, it explains how to use a fishing rod and how to adjust a reel. Also, if a user inputs "I want to go mountain climbing," the list generation unit provides detailed explanations and instructions for use for listed mountain climbing equipment. For example, it explains how to choose mountain climbing boots and how to pack a backpack. Also, if a user inputs "I want to go camping," the list generation unit provides detailed explanations and instructions for use for listed camping tools. For example, it explains how to set up a tent and how to use a sleeping bag. In this way, detailed explanations and instructions for use can be provided for listed tools and spots.

[0061] The list generation unit can display reviews or ratings from other users for the listed equipment or spots to help with selection. For example, when a user checks a list of fishing equipment, the list generation unit displays reviews and ratings from other users to help with selection. For example, highly rated fishing rods and reels are displayed preferentially. Furthermore, when a user checks a list of mountain climbing equipment, the list generation unit displays reviews and ratings from other users to help with selection. For example, highly rated mountain climbing boots and backpacks are displayed preferentially. Furthermore, when a user checks a list of camping equipment, the list generation unit displays reviews and ratings from other users to help with selection. For example, highly rated tents and sleeping bags are displayed preferentially. This allows the user to refer to reviews and ratings from other users to help with selection.

[0062] The list generation unit can automatically provide event or campaign information related to the listed equipment or spots. For example, when a user checks a list of fishing equipment, the list generation unit automatically provides related fishing event or campaign information. For example, fishing tournament or sale information is displayed. Furthermore, when a user checks a list of mountain climbing equipment, the list generation unit automatically provides related mountain climbing event or campaign information. For example, mountain climbing tour or discount information is displayed. Furthermore, when a user checks a list of camping equipment, the list generation unit automatically provides related camping event or campaign information. For example, camping festival or promotion information is displayed. In this way, related event and campaign information can be automatically provided.

[0063] The list generation unit uses the emotion estimation function to analyze the emotion of the user when selecting a list in real time and suggest an optimal list. For example, when the user selects a list of fishing gear, the list generation unit uses the emotion estimation function to analyze the user's emotion in real time and suggest the optimal gear. For example, if the user is excited, the list generation unit suggests high-performance gear. Furthermore, when the user selects a list of mountain climbing equipment, the list generation unit uses the emotion estimation function to analyze the user's emotion in real time and suggest the optimal gear. For example, if the user is relaxed, the list generation unit suggests lightweight equipment. Furthermore, when the user selects a list of camping gear, the list generation unit uses the emotion estimation function to analyze the user's emotion in real time and suggest the optimal gear. For example, if the user has a spirit of adventure, the list generation unit suggests multi-functional gear. In this way, the optimal list can be suggested according to the user's emotion.

[0064] The community participation module can suggest optimal communities or matching partners based on the user's hobbies and interests. For example, if a user inputs "I want to start fishing," the generation AI analyzes the user's hobbies and interests and suggests optimal fishing communities. For example, it can introduce fishing clubs for beginners and local fishing events. If a user inputs "I want to go mountain climbing," the generation AI analyzes the user's hobbies and interests and suggests optimal mountain climbing communities. For example, it can introduce local mountain climbing circles and online forums. If a user inputs "I want to go camping," the generation AI analyzes the user's hobbies and interests and suggests optimal camping communities. For example, it can introduce family camping groups and gatherings of solo camping enthusiasts. This allows the generation AI to suggest optimal communities and matching partners based on the user's hobbies and interests.

[0065] The community participation unit analyzes the user's activity history within the community and can suggest more suitable matching partners. For example, the community participation unit analyzes the user's activity history within a fishing community and suggests other users with common interests. For example, it matches users who are interested in the same fishing spots. The community participation unit also analyzes the user's activity history within a mountain climbing community and suggests other users who are interested in a common mountain climbing route. For example, it matches users who have experience climbing the same mountain. The community participation unit also analyzes the user's activity history within a camping community and suggests other users who are interested in a common camping style. For example, it matches users who have used the same campsite. In this way, it is possible to analyze the activity history within the community and suggest more suitable matching partners.

[0066] The community participation unit can use the emotion estimation function to analyze the emotion of the user when joining a community and suggest matching partners according to the emotion. For example, the community participation unit analyzes the emotion of the user when joining a fishing community and suggests matching partners according to the emotion. For example, if the user is relaxed, it suggests users who have a similarly relaxed mood. The community participation unit also analyzes the emotion of the user when joining a mountain climbing community and suggests matching partners according to the emotion. For example, if the user is excited, it suggests users who have a similar adventurous spirit. The community participation unit also analyzes the emotion of the user when joining a camping community and suggests matching partners according to the emotion. For example, if the user is seeking relaxation, it suggests users who are also seeking relaxation. In this way, it is possible to suggest matching partners according to the user's emotion.

[0067] The community participation unit can automatically suggest events or activities within the community, encouraging user participation. For example, when a user joins a fishing community, the generation AI automatically suggests fishing events and activities. For example, it can introduce local fishing tournaments and fishing classes. Furthermore, when a user joins a mountain climbing community, the generation AI automatically suggests mountain climbing events and activities. For example, it can introduce group mountain climbing and mountain climbing seminars. Furthermore, when a user joins a camping community, the generation AI automatically suggests camping events and activities. For example, it can introduce camping festivals and workshops. In this way, events and activities within the community can be automatically suggested, encouraging user participation.

[0068] The community participation unit can provide a chat function or forum to promote interaction between users within the community. For example, the community participation unit provides a chat function or forum so that users can interact with other users within a fishing community. For example, a forum can be provided for discussing fishing techniques and recommended spots. The community participation unit also provides a chat function or forum so that users can interact with other users within a mountain climbing community. For example, a forum can be provided for exchanging information about mountain climbing routes and equipment. The community participation unit also provides a chat function or forum so that users can interact with other users within a camping community. For example, a forum can be provided for discussing camping tips and recommended campsites. In this way, it is possible to provide a chat function or forum to promote interaction between users within the community.

[0069] The community participation unit can use the emotion estimation function to analyze the emotions of a user when interacting within a community in real time and suggest optimal interaction partners. For example, the community participation unit analyzes the emotions of a user when interacting with other users in a fishing community in real time and suggests optimal interaction partners. For example, if the user is relaxed, it suggests users who are in a similarly relaxed mood. The community participation unit also analyzes the emotions of a user when interacting with other users in a mountain climbing community in real time and suggests optimal interaction partners. For example, if the user is excited, it suggests users who have a similar adventurous spirit. The community participation unit also analyzes the emotions of a user when interacting with other users in a camping community in real time and suggests optimal interaction partners. For example, if the user is seeking relaxation, it suggests users who are also seeking relaxation. In this way, it is possible to suggest optimal interaction partners according to the user's emotions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The desire analysis unit allows the generation AI to suggest additional information related to the user's input, improving the level of detail of the plan. For example, if a user inputs, "I want to travel to Kyoto," the generation AI will suggest additional information about seasonal events and local specialties in Kyoto. For example, it will suggest cherry blossom viewing spots during cherry blossom season. If a user inputs, "I want to start fishing," the desire analysis unit will suggest basic fishing knowledge and guides for beginners. For example, it will include basic fishing techniques and precautions. If a user inputs, "I want to go mountain climbing," the desire analysis unit will suggest additional information about the equipment and safety measures needed for mountain climbing. For example, it will provide information on mountain climbing routes and weather information. This allows the generation AI to suggest additional information related to the user's input, improving the level of detail of the plan.

[0072] The plan generation unit can generate multiple options for a plan desired by the user and allow the user to select from them. For example, if a user inputs, "I want to travel to Kyoto," the generation AI will propose multiple travel plans from which the user can choose. For example, it will provide plans that emphasize sightseeing and plans that emphasize gourmet food. Also, if a user inputs, "I want to take up fishing," the plan generation unit will propose a list of multiple fishing spots and equipment from which the user can choose. For example, it will provide spots for beginners and spots for advanced hikers. Also, if a user inputs, "I want to go mountain climbing," the plan generation unit will propose a list of multiple mountain climbing routes and equipment from which the user can choose. For example, it will provide routes by difficulty level and equipment for each season. This allows the user to choose from multiple options.

[0073] When generating a plan, the plan generation unit can provide a more reliable plan by referring to reviews or ratings from other users. For example, if a user inputs, "I want to travel to Kyoto," the generation AI refers to the reviews and ratings from other users and suggests reliable tourist spots and accommodations. For example, it prioritizes the selection of highly rated tourist spots and hotels. Also, if a user inputs, "I want to take up fishing," the plan generation unit can refer to the reviews and ratings from other users and suggest reliable fishing spots and equipment. For example, it can select popular fishing tackle manufacturers and fishing locations. Also, if a user inputs, "I want to go mountain climbing," the plan generation unit can refer to the reviews and ratings from other users and suggest reliable mountain climbing routes and equipment. For example, it can select routes and equipment that are highly rated by experienced users. This allows the generation AI to provide a reliable plan by referring to the reviews and ratings from other users.

[0074] The list generation unit can customize and list the optimal equipment or spots according to the user's budget and preferences. For example, if a user inputs "I want to start fishing," the generation AI will list the optimal fishing equipment according to the user's budget and preferences. For example, it will suggest fishing rods and reels that can be purchased within the user's budget. If a user inputs "I want to go mountain climbing," the list generation unit will list the optimal mountain climbing equipment according to the user's budget and preferences. For example, it will suggest mountain climbing boots and backpacks that can be purchased within the user's budget. If a user inputs "I want to go camping," the list generation unit will list the optimal camping equipment according to the user's budget and preferences. For example, it will suggest tents and sleeping bags that can be purchased within the user's budget. This allows the list of optimal equipment and spots to be customized and listed according to the user's budget and preferences.

[0075] The list generation unit allows the generation AI to provide detailed explanations and instructions for use for listed tools or spots. For example, if a user inputs "I want to start fishing," the generation AI provides detailed explanations and instructions for use for listed fishing tools. For example, it explains how to use a fishing rod and how to adjust a reel. If a user inputs "I want to go mountain climbing," the list generation unit provides detailed explanations and instructions for use for listed mountain climbing equipment. For example, it explains how to choose mountain climbing boots and how to pack a backpack. If a user inputs "I want to go camping," the list generation unit provides detailed explanations and instructions for use for listed camping tools. For example, it explains how to set up a tent and how to use a sleeping bag. This allows detailed explanations and instructions for use to be provided for listed tools and spots.

[0076] The desire analysis unit uses the emotion estimation function to analyze the emotions expressed by the user at the time of input and generate a plan that matches those emotions. For example, if a user inputs "I want to relax," the generation AI analyzes the user's emotions and proposes a relaxing travel plan. For example, it generates a plan that includes hot springs and resort hotels. If a user inputs "I want to be adventurous," the desire analysis unit analyzes the user's emotions and proposes a plan that satisfies the adventurous spirit. For example, it generates a plan that includes adventure tours and outdoor activities. If a user inputs "I want to be healed," the desire analysis unit analyzes the user's emotions and proposes a plan that provides healing. For example, it generates a plan that includes places rich in nature and relaxation facilities. This makes it possible to provide plans that match the user's emotions.

[0077] The plan generation unit can use the emotion estimation function to analyze the user's emotions in real time when selecting a plan and propose an optimal plan. For example, when the user selects from multiple travel plans, the emotion estimation function is used to analyze the user's emotions in real time and propose an optimal plan. For example, if the user is excited, an active plan is proposed. Furthermore, when the user selects from multiple fishing spots, the plan generation unit uses the emotion estimation function to analyze the user's emotions in real time and propose an optimal spot. For example, if the user is relaxed, a quiet spot is proposed. Furthermore, when the user selects from multiple mountain climbing routes, the plan generation unit uses the emotion estimation function to analyze the user's emotions in real time and propose an optimal route. For example, if the user is feeling adventurous, a more difficult route is proposed. In this way, the optimal plan can be proposed according to the user's emotions.

[0078] The list generation unit uses the emotion estimation function to analyze the emotion of the user when selecting a list in real time and suggest the optimal list. For example, when the user selects a list of fishing gear, the emotion estimation function is used to analyze the user's emotion in real time and suggest the optimal gear. For example, if the user is excited, high-performance gear is suggested. Furthermore, when the user selects a list of mountain climbing equipment, the list generation unit uses the emotion estimation function to analyze the user's emotion in real time and suggest the optimal gear. For example, if the user is relaxed, lightweight gear is suggested. Furthermore, when the user selects a list of camping gear, the list generation unit uses the emotion estimation function to analyze the user's emotion in real time and suggest the optimal gear. For example, if the user is adventurous, multi-functional gear is suggested. In this way, the optimal list can be suggested according to the user's emotion.

[0079] The community participation unit can use the emotion estimation function to analyze the emotion of the user when joining a community and suggest matching partners according to the emotion. For example, the emotion of the user when joining a fishing community can be analyzed and matching partners according to the emotion can be suggested. For example, if the user is relaxed, users with a similarly relaxed mood can be suggested. The community participation unit can also analyze the emotion of the user when joining a mountain climbing community and suggest matching partners according to the emotion. For example, if the user is excited, users with a similarly adventurous spirit can be suggested. The community participation unit can also analyze the emotion of the user when joining a camping community and suggest matching partners according to the emotion. For example, if the user is seeking relaxation, users who are also seeking relaxation can be suggested. In this way, matching partners can be suggested according to the user's emotion.

[0080] The community participation unit can provide a chat function or forum to promote interaction between users within the community. For example, a chat function or forum can be provided so that a user can interact with other users within a fishing community. For example, a forum can be provided to discuss fishing techniques and recommended spots. The community participation unit can also provide a chat function or forum so that a user can interact with other users within a mountain climbing community. For example, a forum can be provided to exchange information about mountain climbing routes and equipment. The community participation unit can also provide a chat function or forum so that a user can interact with other users within a camping community. For example, a forum can be provided to discuss camping tips and recommended campsites. In this way, a chat function or forum can be provided to promote interaction between users within the community.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The desire analysis unit analyzes the user's desires. For example, if the user inputs "I want to go on a trip to Kyoto," the desire analysis unit analyzes the desire and collects information about the trip. Similarly, if the user inputs "I want to take up fishing," the desire analysis unit analyzes the desire and collects information about fishing. Step 2: The plan generation unit generates an optimal plan based on the desires analyzed by the desire analysis unit. For example, the plan generation unit automatically creates a travel plan based on tourist information about Kyoto, including recommended times, spots, and accommodations. It also lists fishing equipment and recommended spots in the area and provides them to the user. Step 3: The list generator creates a list of necessary tools and spots based on the desires analyzed by the desire analysis unit. For example, the list generator creates a list of tools needed for fishing and recommended spots in the area where the user lives, and provides it to the user. It also creates a list of tools needed for travel and tourist spots, and provides it to the user. Step 4: The community participation section joins communities related to your interests and matches you with people who share the same hobbies. For example, you can join a fishing community to interact with other fishing enthusiasts, or join a travel community to interact with other travel enthusiasts.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] 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.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a preference analysis unit that analyzes a user's preference; a plan generation unit that generates an optimal plan based on the desires analyzed by the desire analysis unit; a list generating unit that lists necessary tools or spots based on the desires analyzed by the desire analyzing unit; a community participation unit that participates in a community related to the user's preference and matches the user with people who have the same hobby. A system characterized by:

2. The desire analysis unit Analyzing the user's past behavioral history or preferences to generate a more personalized plan 2. The system of claim 1.

3. The desire analysis unit The generation AI suggests additional information related to the user's input, improving the level of detail of the plan.

2. The system of claim 1.

4. The desire analysis unit Analyzing the emotion of the user at the time of input and generating the plan according to the emotion 2. The system of claim 1.

5. The plan generation unit Generate multiple options for the plan desired by the user and allow the user to select from them.

2. The system of claim 1.

6. The plan generation unit When generating a plan, the system refers to reviews or ratings from other users to provide a more reliable plan.

2. The system of claim 1.

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