system

Abstract

The system according to the embodiment aims to provide travel plans using travel data posted by users and to provide returns to the users who posted the travel plans. [Solution] A system according to an embodiment includes an analysis unit, a provision unit, and a return unit. The analysis unit analyzes posted data. The provision unit provides travel plans based on the data analyzed by the analysis unit. The return unit provides returns to users based on the travel plans provided by the provision unit.

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 technology had the problem of not having a sufficient system in place to effectively utilize travel data posted by users to provide travel plans and provide appropriate returns to posters.

[0005] The system according to the embodiment aims to provide travel plans using travel data posted by users and to provide returns to the users who posted the travel plans. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a provision unit, and a return unit. The analysis unit analyzes the posted data. The provision unit provides a travel plan based on the data analyzed by the analysis unit. The return unit returns a return to the user based on the travel plan provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide travel plans by utilizing travel data posted by users and can provide returns to the users who posted the travel plans. [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) A travel plan providing system according to an embodiment of the present invention allows users to create travel itineraries and reviews of visited restaurants using an app, and then provides recommended travel plans to third parties based on the data. This system allows users to post their travel itineraries and reviews of visited restaurants to the app, analyzes the posted data, and provides recommended travel plans to third parties. This system allows users to share their travel experiences and provide useful information to other users. In return, posters receive a 10% rebate on accommodation and meal costs via an electronic payment system. For example, users post information, photos, and ratings of tourist spots and restaurants they have visited to the app. This information is stored in the app's database. The posted data is then analyzed. This analysis involves using AI to categorize the posts and identify highly rated spots and restaurants. For example, tourist spots and restaurants that have been highly rated by multiple users are extracted. Based on the analysis results, recommended travel plans are provided to third parties. For example, for a user planning to visit a specific area, a travel plan including highly rated tourist spots and restaurants in that area is proposed. This proposal is provided to the user through the app. In addition, posters receive a 10% rebate on accommodation and meal costs via the electronic payment system as a reward. For example, a portion of the cost of a hotel where a user stays or a restaurant where a user dine is rebated through the electronic payment system. This allows users to obtain economic benefits by sharing their travel experiences. This system allows users to share their travel experiences and provide useful information to other users. Posters also receive a 10% rebate on accommodation and meal costs via the electronic payment system as a reward, which increases users' motivation to post content. Furthermore, providing recommended travel plans to third parties makes travel planning easier and improves travel satisfaction. This allows the travel plan providing system to share users' travel experiences and provide useful information to other users. Posters also receive a 10% rebate on accommodation and meal costs via the electronic payment system as a reward, which increases users' motivation to post content. Furthermore, providing recommended travel plans to third parties makes travel planning easier and improves travel satisfaction.

[0029] The travel plan providing system according to the embodiment includes an analysis unit, a provision unit, and a refund unit. The analysis unit analyzes posted data. The analysis unit analyzes posted content using, for example, text mining technology. The analysis unit can also analyze posted photos using image analysis technology. The analysis unit can also classify posted data using a machine learning algorithm to identify highly rated spots and restaurants. For example, the analysis unit extracts tourist spots and restaurants that have been highly rated by multiple users. The provision unit provides a travel plan based on the data analyzed by the analysis unit. For example, the provision unit proposes a travel plan to a user planning to visit a specific area that includes highly rated tourist spots and restaurants in that area. The provision unit can also provide a travel plan customized based on the user's interests. For example, the provision unit proposes a travel plan individually customized based on places the user has visited and spots they have rated in the past. The refund unit refunds a return to the user based on the travel plan provided by the provision unit. For example, the refund unit refunds 10% of accommodation and meal costs via an electronic payment system. The return unit can also provide a return in the form of points return or cash back. For example, the return unit may return a portion of the cost of a hotel where the user stays or a restaurant where the user dine as points. This allows the travel plan providing system according to the embodiment to share users' travel experiences and provide useful information to other users. In addition, posters are given a return of 10% of the cost of accommodation or meals through an electronic payment system, which increases users' motivation to post. Furthermore, providing recommended travel plans to third parties makes it easier for them to plan their trip and improves travel satisfaction.

[0030] The travel plan providing system includes a collection unit that collects posted data. The collection unit collects the posted data. The collection unit collects, for example, text data, image data, video data, etc. posted by users to the app. The collection unit can also collect information on tourist spots and restaurants visited by users. For example, the collection unit collects photos and ratings of tourist spots posted by users. The collection unit can also collect reviews and comments posted by users. For example, the collection unit collects reviews and ratings of restaurants visited by users. This allows the collection unit to efficiently collect data posted by users and provide it to the analysis unit. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data posted by users into AI and have the AI ​​collect the data. This allows the collection unit to efficiently collect data posted by users and provide it to the analysis unit.

[0031] The analysis unit can classify the posted content and identify highly rated spots and restaurants. The analysis unit classifies the posted content using, for example, text mining technology. For example, the analysis unit analyzes reviews and comments posted by users and classifies them by category. The analysis unit can also analyze posted photos using image analysis technology and identify highly rated spots and restaurants. For example, the analysis unit extracts tourist spots and restaurants that have been highly rated by multiple users. The analysis unit can also classify posted data using a machine learning algorithm and identify highly rated spots and restaurants. For example, the analysis unit identifies highly rated tourist spots and restaurants based on data posted by users. This allows the analysis unit to identify highly rated spots and restaurants. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data posted by users into AI and have the AI ​​analyze the data. This allows the analysis unit to identify highly rated spots and restaurants.

[0032] The providing unit can propose a travel plan to a user planning to visit a specific area, the travel plan including highly rated tourist attractions and restaurants in the area. For example, the providing unit proposes a travel plan to a user planning to visit a specific area, the travel plan including highly rated tourist attractions and restaurants in the area. For example, the providing unit collects information on tourist attractions and restaurants in the area the user plans to visit and proposes a travel plan including highly rated spots. The providing unit can also provide a travel plan customized based on the user's interests. For example, the providing unit proposes an individually customized travel plan based on places the user has previously visited and spots they have rated. Furthermore, the providing unit can propose an optimal travel plan based on the user's travel purpose and budget. For example, if the user is planning a family trip, the providing unit proposes a travel plan including family-friendly tourist attractions and restaurants. This allows the providing unit to propose a travel plan including highly rated tourist attractions and restaurants in the specific area. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input information about the area the user plans to visit into AI and have the AI ​​execute the travel plan proposal. This allows the provider to propose travel plans that include highly rated tourist spots and restaurants in a particular area.

[0033] The refund unit can refund a certain percentage of the cost of accommodation or meals through the electronic payment system. For example, the refund unit refunds 10% of the cost of accommodation or meals through the electronic payment system. For example, the refund unit refunds a portion of the cost of a hotel where the user stayed or a restaurant where the user ate as points. The refund unit can also provide returns in the form of cashback or coupons. For example, the refund unit refunds a portion of the cost of a hotel where the user stayed as cashback. Furthermore, the refund unit can refund a portion of the cost of a tourist spot or a restaurant where the user visited as a coupon. For example, the refund unit refunds a portion of the cost of a restaurant where the user visited as a coupon that can be used the next time. In this way, the refund unit can refund 10% of the cost of accommodation or meals through the electronic payment system. Some or all of the above-mentioned processing in the refund unit may be performed using, for example, AI, or may be performed without AI. For example, the refund unit can input the cost of a hotel where the user stayed or a restaurant where the user ate into AI and have the AI ​​calculate the refund. In this way, the refund unit can refund 10% of the cost of accommodation or meals through the electronic payment system.

[0034] The collection unit can analyze the user's past posting history and select the optimal collection method. For example, the collection unit can analyze time periods when the user frequently posted in the past and concentrate collection on those time periods. The collection unit can also analyze trends in the content the user has posted in the past and prioritize collecting related data. Furthermore, the collection unit can collect data related to specific events or locations from the user's past posting history. For example, the collection unit can analyze reviews of tourist spots and restaurants posted by the user in the past and prioritize collecting related data. This allows the collection unit to analyze the user's past posting history and select the optimal collection method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past posting history into AI and have the AI ​​select the optimal collection method. This allows the collection unit to analyze the user's past posting history and select the optimal collection method.

[0035] When collecting posted data, the collection unit can filter the posted data based on the user's current travel situation and areas of interest. For example, if the user is currently traveling, the collection unit prioritizes collecting real-time posted data. The collection unit can also filter and collect related posted data based on the user's areas of interest (e.g., gourmet food, tourist spots). The collection unit can also collect data at an appropriate time depending on the user's current travel situation (e.g., while traveling, while staying). For example, the collection unit prioritizes collecting information on tourist spots and restaurants in the area where the user is currently staying. This allows the collection unit to filter the posted data based on the user's current travel situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's current travel situation and areas of interest into AI and have the AI ​​filter the data. This allows the collection unit to filter the posted data based on the user's current travel situation and areas of interest.

[0036] When collecting posted data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific tourist destination, the collection unit prioritizes collecting posted data related to the tourist destination. Furthermore, when the user is in a specific restaurant, the collection unit can prioritize collecting reviews related to the restaurant. Furthermore, when the user is staying in a specific area, the collection unit can prioritize collecting data on tourist spots and events related to the area. For example, the collection unit prioritizes collecting information on tourist spots and restaurants in the area where the user is currently staying. This allows the collection unit to prioritize collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI and have the AI ​​collect data. This allows the collection unit to prioritize collecting highly relevant data by taking into account the user's geographical location information.

[0037] When collecting posting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects travel photos and comments shared by the user on social media. The collection unit can also collect information on tourist spots and restaurants that the user follows on social media. Furthermore, the collection unit can collect information on travel groups and events that the user participates in on social media. For example, the collection unit collects travel photos and comments shared by the user on social media. This allows the collection unit to analyze the user's social media activities and collect related data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activities into AI and have the AI ​​collect data. This allows the collection unit to analyze the user's social media activities and collect related data.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the posted data. For example, the analysis unit performs a detailed analysis on posted data with a high rating. The analysis unit can also perform a standard analysis on posted data with a medium rating. The analysis unit can also perform a brief analysis on posted data with a low rating. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the posted data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the posted data. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the posted data to AI and have the AI ​​adjust the level of detail of the analysis. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the posted data.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of posted data. For example, the analysis unit can apply an analysis algorithm related to food quality and service to restaurant reviews. The analysis unit can also apply an analysis algorithm related to scenery and access to tourist attraction reviews. The analysis unit can also apply an analysis algorithm related to room cleanliness and facilities to accommodation reviews. For example, the analysis unit can apply an analysis algorithm related to food quality and service to restaurant reviews. This allows the analysis unit to apply different analysis algorithms depending on the category of posted data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of posted data into AI and have the AI ​​apply the analysis algorithm. This allows the analysis unit to apply different analysis algorithms depending on the category of posted data.

[0040] During analysis, the analysis unit can determine the priority of analysis based on the time when the posted data was submitted. For example, the analysis unit prioritizes analysis of the most recently posted data. The analysis unit can also prioritize analysis of posted data related to a specific event or season. Furthermore, the analysis unit can prioritize analysis of data related to a specific period from the user's past posting history. For example, the analysis unit prioritizes analysis of the most recently posted data. This allows the analysis unit to determine the priority of analysis based on the time when the posted data was submitted. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the posted data was submitted to AI and have the AI ​​determine the priority of analysis. This allows the analysis unit to determine the priority of analysis based on the time when the posted data was submitted.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the posted data. For example, the analysis unit prioritizes analyzing highly rated posted data. The analysis unit can also prioritize analyzing posted data related to the user's field of interest. Furthermore, the analysis unit can prioritize analyzing posted data related to a specific region or theme. For example, the analysis unit prioritizes analyzing highly rated posted data. This allows the analysis unit to adjust the order of analysis based on the relevance of the posted data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the posted data into AI and have the AI ​​adjust the order of analysis. This allows the analysis unit to adjust the order of analysis based on the relevance of the posted data.

[0042] The providing unit can adjust the level of detail provided based on the importance of the travel plan when providing the travel plan. For example, the providing unit provides detailed information for a travel plan that includes highly rated tourist attractions and restaurants. The providing unit can also provide standard information for a travel plan that includes medium-rated tourist attractions and restaurants. The providing unit can also provide concise information for a travel plan that includes low-rated tourist attractions and restaurants. For example, the providing unit adjusts the level of detail provided based on the importance of the travel plan. This allows the providing unit to adjust the level of detail provided based on the importance of the travel plan. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the importance of the travel plan to AI and cause the AI ​​to adjust the level of detail provided. This allows the providing unit to adjust the level of detail provided based on the importance of the travel plan.

[0043] The providing unit can apply different providing algorithms depending on the category of the travel plan when providing the plan. For example, the providing unit can apply a providing algorithm that emphasizes restaurant ratings and menu information to a gourmet travel plan. The providing unit can also apply a providing algorithm that emphasizes tourist destination ratings and access information to a sightseeing travel plan. The providing unit can also apply a providing algorithm that emphasizes store ratings and product information to a shopping travel plan. For example, the providing unit can apply a providing algorithm that emphasizes restaurant ratings and menu information to a gourmet travel plan. This allows the providing unit to apply different providing algorithms depending on the category of the travel plan. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the category of the travel plan into AI and have the AI ​​apply the providing algorithm. This allows the providing unit to apply different providing algorithms depending on the category of the travel plan.

[0044] The providing unit can determine the priority of provision based on the time of submission of the travel plan when it is provided. For example, the providing unit can prioritize providing the latest travel plan. The providing unit can also prioritize providing travel plans related to a specific event or season. Furthermore, the providing unit can prioritize providing travel plans related to a specific period based on the user's past travel history. For example, the providing unit can prioritize providing the latest travel plan. This allows the providing unit to determine the priority of provision based on the time of submission of the travel plan. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the time of submission of the travel plan into AI and have the AI ​​determine the priority of provision. This allows the providing unit to determine the priority of provision based on the time of submission of the travel plan.

[0045] The providing unit can adjust the order of providing travel plans based on the relevance of the travel plans when providing them. For example, the providing unit can prioritize providing travel plans that include highly rated tourist attractions and restaurants. The providing unit can also prioritize providing travel plans related to the user's area of ​​interest. Furthermore, the providing unit can prioritize providing travel plans related to a specific region or theme. For example, the providing unit can prioritize providing travel plans that include highly rated tourist attractions and restaurants. This allows the providing unit to adjust the order of providing the travel plans based on the relevance of the travel plans. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the relevance of the travel plans into AI and have the AI ​​adjust the order of providing the travel plans. This allows the providing unit to adjust the order of providing the travel plans based on the relevance of the travel plans.

[0046] When providing a refund, the refund unit can analyze the user's past consumption behavior and select the optimal refund method. For example, the refund unit can provide a refund to accommodations or restaurants that the user has used in the past. The refund unit can also provide a refund to a specific category (e.g., gourmet food, sightseeing) based on the user's past consumption behavior. The refund unit can also analyze the user's past consumption behavior and select the most effective refund method. For example, the refund unit can provide a refund to accommodations or restaurants that the user has used in the past. This allows the refund unit to analyze the user's past consumption behavior and select the optimal refund method. Some or all of the above-described processing in the refund unit may be performed using, for example, AI, or may be performed without using AI. For example, the refund unit can input the user's past consumption behavior into AI and have the AI ​​select the optimal refund method. This allows the refund unit to analyze the user's past consumption behavior and select the optimal refund method.

[0047] The redemption unit can customize the redemption method based on the user's current living situation when redeeming the redemption. For example, if the user is traveling, the redemption unit can provide an immediate redemption to reduce travel expenses. Furthermore, if the user is at home, the redemption unit can provide a coupon that can be used for the user's next trip. Furthermore, the redemption unit can provide an appropriate redemption method depending on the user's current living situation (e.g., at work, on vacation). For example, if the user is traveling, the redemption unit can provide an immediate redemption to reduce travel expenses. This allows the redemption unit to customize the redemption method based on the user's current living situation. Some or all of the above-described processing in the redemption unit may be performed using, or without, AI. For example, the redemption unit can input the user's current living situation into AI and have the AI ​​customize the redemption method. This allows the redemption unit to customize the redemption method based on the user's current living situation.

[0048] The cashback unit can select the optimal cashback method by taking into account the user's geographical location information. For example, if the user is in a specific tourist destination, the cashback unit can provide a coupon that can be used at the tourist destination. Furthermore, if the user is in a specific restaurant, the cashback unit can provide a discount that can be used at the restaurant. Furthermore, if the user is staying in a specific region, the cashback unit can provide a benefit that can be used in the region. For example, if the user is in a specific tourist destination, the cashback unit can provide a coupon that can be used at the tourist destination. This allows the cashback unit to select the optimal cashback method by taking into account the user's geographical location information. Some or all of the above-described processing by the cashback unit may be performed using, or without, AI. For example, the cashback unit can input the user's geographical location information into AI and have the AI ​​select the optimal cashback method. This allows the cashback unit to select the optimal cashback method by taking into account the user's geographical location information.

[0049] When providing a refund, the refund unit can analyze the user's social media activity and suggest a method of redemption. For example, the refund unit can provide a refund based on travel information shared by the user on social media. The refund unit can also provide a refund for tourist attractions or restaurants that the user follows on social media. The refund unit can also provide a refund for travel groups or events that the user participates in on social media. For example, the refund unit can provide a refund based on travel information shared by the user on social media. This allows the refund unit to analyze the user's social media activity and suggest a method of redemption. Some or all of the above-described processing in the refund unit may be performed using, for example, AI, or may be performed without AI. For example, the refund unit can input the user's social media activity into AI and have the AI ​​suggest a method of redemption. This allows the refund unit to analyze the user's social media activity and suggest a method of redemption.

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

[0051] The analysis unit can analyze the user's travel history and suggest new travel plans based on past travel patterns. For example, the analysis unit analyzes data on tourist spots and restaurants visited by the user in the past and compares it with data from other users with similar travel patterns. The analysis unit can also suggest new spots with similar characteristics based on spots that the user has previously rated highly. Furthermore, the analysis unit can suggest travel plans related to specific seasons or events based on the user's past travel history. This allows the analysis unit to provide more personalized travel plans by utilizing the user's past travel history.

[0052] The collection unit can analyze the user's social media activity and collect data that is useful for proposing travel plans. For example, the collection unit collects travel photos and comments shared by the user on social media. The collection unit can also collect information on tourist spots and restaurants that the user follows. Furthermore, the collection unit can collect information on travel groups and events that the user participates in. This allows the collection unit to utilize the user's social media activity to collect more relevant data.

[0053] The providing unit can propose an optimal travel plan in real time, taking into account the user's current geographical location information. For example, if the user is in a specific tourist destination, the providing unit can propose a travel plan that includes highly rated spots and restaurants around the tourist destination. Also, if the user is traveling, the providing unit can propose spots that the user can stop by on the way to the next destination. Furthermore, if the user is staying in a specific area, the providing unit can propose a travel plan that includes events and festivals being held in that area. In this way, the providing unit can utilize the user's current geographical location information to provide a more appropriate travel plan.

[0054] The collection unit can analyze the user's past travel history and select the optimal data collection method. For example, it can analyze the time periods in which the user frequently posted in the past and concentrate collection on those time periods. It can also analyze trends in the content posted by the user in the past and prioritize collection of related data. It can also collect data related to specific events or locations from the user's past travel history. This allows the collection unit to utilize the user's past travel history to collect data more efficiently.

[0055] The providing unit can provide a travel plan customized according to the user's travel purpose and budget. For example, if the user is planning a family trip, the providing unit can suggest a travel plan that includes family-friendly tourist spots and restaurants. If the user is concerned about budget, the providing unit can suggest a travel plan that includes cost-effective spots and restaurants. Furthermore, if the user is interested in a specific theme (e.g., history, nature), the providing unit can suggest a travel plan that includes spots related to that theme. This allows the providing unit to provide the optimal travel plan according to the user's travel purpose and budget.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The analysis unit analyzes the posted data. The analysis unit can analyze the posted content using text mining technology and the posted photos using image analysis technology. It also uses machine learning algorithms to classify the posted data and identify highly rated spots and restaurants. For example, it can extract tourist spots and restaurants that have been highly rated by multiple users. Step 2: The provider provides a travel plan based on the data analyzed by the analyzer. For a user planning to visit a specific area, the provider proposes a travel plan that includes highly rated tourist spots and restaurants in that area. The provider also provides a travel plan customized based on the user's interests. For example, the provider proposes an individually customized travel plan based on places the user has visited in the past and spots they have rated. Step 3: The return unit returns a return to the user based on the travel plan provided by the provider. The return unit returns 10% of the accommodation and meal costs through an electronic payment system. The return unit can also provide a return in the form of points or cash back. For example, a portion of the cost of the hotel where the user stayed or the restaurant where the user ate could be returned as points.

[0058] (Example 2) A travel plan providing system according to an embodiment of the present invention allows users to create travel itineraries and reviews of visited restaurants using an app, and then provides recommended travel plans to third parties based on the data. This system allows users to post their travel itineraries and reviews of visited restaurants to the app, analyzes the posted data, and provides recommended travel plans to third parties. This system allows users to share their travel experiences and provide useful information to other users. In return, posters receive a 10% rebate on accommodation and meal costs via an electronic payment system. For example, users post information, photos, and ratings of tourist spots and restaurants they have visited to the app. This information is stored in the app's database. The posted data is then analyzed. This analysis involves using AI to categorize the posts and identify highly rated spots and restaurants. For example, tourist spots and restaurants that have been highly rated by multiple users are extracted. Based on the analysis results, recommended travel plans are provided to third parties. For example, for a user planning to visit a specific area, a travel plan including highly rated tourist spots and restaurants in that area is proposed. This proposal is provided to the user through the app. In addition, posters receive a 10% rebate on accommodation and meal costs via the electronic payment system as a reward. For example, a portion of the cost of a hotel where a user stays or a restaurant where a user dine is rebated through the electronic payment system. This allows users to obtain economic benefits by sharing their travel experiences. This system allows users to share their travel experiences and provide useful information to other users. Posters also receive a 10% rebate on accommodation and meal costs via the electronic payment system as a reward, which increases users' motivation to post content. Furthermore, providing recommended travel plans to third parties makes travel planning easier and improves travel satisfaction. This allows the travel plan providing system to share users' travel experiences and provide useful information to other users. Posters also receive a 10% rebate on accommodation and meal costs via the electronic payment system as a reward, which increases users' motivation to post content. Furthermore, providing recommended travel plans to third parties makes travel planning easier and improves travel satisfaction.

[0059] The travel plan providing system according to the embodiment includes an analysis unit, a provision unit, and a refund unit. The analysis unit analyzes posted data. The analysis unit analyzes posted content using, for example, text mining technology. The analysis unit can also analyze posted photos using image analysis technology. The analysis unit can also classify posted data using a machine learning algorithm to identify highly rated spots and restaurants. For example, the analysis unit extracts tourist spots and restaurants that have been highly rated by multiple users. The provision unit provides a travel plan based on the data analyzed by the analysis unit. For example, the provision unit proposes a travel plan to a user planning to visit a specific area that includes highly rated tourist spots and restaurants in that area. The provision unit can also provide a travel plan customized based on the user's interests. For example, the provision unit proposes a travel plan individually customized based on places the user has visited and spots they have rated in the past. The refund unit refunds a return to the user based on the travel plan provided by the provision unit. For example, the refund unit refunds 10% of accommodation and meal costs via an electronic payment system. The return unit can also provide a return in the form of points return or cash back. For example, the return unit may return a portion of the cost of a hotel where the user stays or a restaurant where the user dine as points. This allows the travel plan providing system according to the embodiment to share users' travel experiences and provide useful information to other users. In addition, posters are given a return of 10% of the cost of accommodation or meals through an electronic payment system, which increases users' motivation to post. Furthermore, providing recommended travel plans to third parties makes it easier for them to plan their trip and improves travel satisfaction.

[0060] The travel plan providing system includes a collection unit that collects posted data. The collection unit collects the posted data. The collection unit collects, for example, text data, image data, video data, etc. posted by users to the app. The collection unit can also collect information on tourist spots and restaurants visited by users. For example, the collection unit collects photos and ratings of tourist spots posted by users. The collection unit can also collect reviews and comments posted by users. For example, the collection unit collects reviews and ratings of restaurants visited by users. This allows the collection unit to efficiently collect data posted by users and provide it to the analysis unit. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data posted by users into AI and have the AI ​​collect the data. This allows the collection unit to efficiently collect data posted by users and provide it to the analysis unit.

[0061] The analysis unit can classify the posted content and identify highly rated spots and restaurants. The analysis unit classifies the posted content using, for example, text mining technology. For example, the analysis unit analyzes reviews and comments posted by users and classifies them by category. The analysis unit can also analyze posted photos using image analysis technology and identify highly rated spots and restaurants. For example, the analysis unit extracts tourist spots and restaurants that have been highly rated by multiple users. The analysis unit can also classify posted data using a machine learning algorithm and identify highly rated spots and restaurants. For example, the analysis unit identifies highly rated tourist spots and restaurants based on data posted by users. This allows the analysis unit to identify highly rated spots and restaurants. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data posted by users into AI and have the AI ​​analyze the data. This allows the analysis unit to identify highly rated spots and restaurants.

[0062] The providing unit can propose a travel plan to a user planning to visit a specific area, the travel plan including highly rated tourist attractions and restaurants in the area. For example, the providing unit proposes a travel plan to a user planning to visit a specific area, the travel plan including highly rated tourist attractions and restaurants in the area. For example, the providing unit collects information on tourist attractions and restaurants in the area the user plans to visit and proposes a travel plan including highly rated spots. The providing unit can also provide a travel plan customized based on the user's interests. For example, the providing unit proposes an individually customized travel plan based on places the user has previously visited and spots they have rated. Furthermore, the providing unit can propose an optimal travel plan based on the user's travel purpose and budget. For example, if the user is planning a family trip, the providing unit proposes a travel plan including family-friendly tourist attractions and restaurants. This allows the providing unit to propose a travel plan including highly rated tourist attractions and restaurants in the specific area. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input information about the area the user plans to visit into AI and have the AI ​​execute the travel plan proposal. This allows the provider to propose travel plans that include highly rated tourist spots and restaurants in a particular area.

[0063] The refund unit can refund a certain percentage of the cost of accommodation or meals through the electronic payment system. For example, the refund unit refunds 10% of the cost of accommodation or meals through the electronic payment system. For example, the refund unit refunds a portion of the cost of a hotel where the user stayed or a restaurant where the user ate as points. The refund unit can also provide returns in the form of cashback or coupons. For example, the refund unit refunds a portion of the cost of a hotel where the user stayed as cashback. Furthermore, the refund unit can refund a portion of the cost of a tourist spot or a restaurant where the user visited as a coupon. For example, the refund unit refunds a portion of the cost of a restaurant where the user visited as a coupon that can be used the next time. In this way, the refund unit can refund 10% of the cost of accommodation or meals through the electronic payment system. Some or all of the above-mentioned processing in the refund unit may be performed using, for example, AI, or may be performed without AI. For example, the refund unit can input the cost of a hotel where the user stayed or a restaurant where the user ate into AI and have the AI ​​calculate the refund. In this way, the refund unit can refund 10% of the cost of accommodation or meals through the electronic payment system.

[0064] The collection unit can estimate a user's emotions and adjust the timing of collecting posted data based on the estimated user emotions. For example, if the user is excited, the collection unit can collect posted data immediately to promote real-time information provision. Furthermore, if the user is relaxed, the collection unit can collect posted data at regular intervals to avoid excessive burden on the user. Furthermore, if the user is feeling stressed, the collection unit can delay the collection timing to reduce the user's burden. For example, the collection unit can estimate a user's emotions and adjust the collection timing based on the estimated emotions. This allows the collection unit to adjust the timing of collecting posted data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​adjust the collection timing. This allows the collection unit to adjust the timing of collecting post data based on the user's emotions.

[0065] The collection unit can analyze the user's past posting history and select the optimal collection method. For example, the collection unit can analyze time periods when the user frequently posted in the past and concentrate collection on those time periods. The collection unit can also analyze trends in the content the user has posted in the past and prioritize collecting related data. Furthermore, the collection unit can collect data related to specific events or locations from the user's past posting history. For example, the collection unit can analyze reviews of tourist spots and restaurants posted by the user in the past and prioritize collecting related data. This allows the collection unit to analyze the user's past posting history and select the optimal collection method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past posting history into AI and have the AI ​​select the optimal collection method. This allows the collection unit to analyze the user's past posting history and select the optimal collection method.

[0066] When collecting posted data, the collection unit can filter the posted data based on the user's current travel situation and areas of interest. For example, if the user is currently traveling, the collection unit prioritizes collecting real-time posted data. The collection unit can also filter and collect related posted data based on the user's areas of interest (e.g., gourmet food, tourist spots). The collection unit can also collect data at an appropriate time depending on the user's current travel situation (e.g., while traveling, while staying). For example, the collection unit prioritizes collecting information on tourist spots and restaurants in the area where the user is currently staying. This allows the collection unit to filter the posted data based on the user's current travel situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's current travel situation and areas of interest into AI and have the AI ​​filter the data. This allows the collection unit to filter the posted data based on the user's current travel situation and areas of interest.

[0067] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, when the user is excited, the collection unit prioritizes collecting posted data with positive reviews. Furthermore, when the user is relaxed, the collection unit can prioritize collecting posted data including detailed reviews and photos. Furthermore, when the user is stressed, the collection unit can prioritize collecting concise reviews and short comments. For example, the collection unit estimates the user's emotions and prioritizes the data to be collected based on the estimated emotions. This allows the collection unit to prioritize the data to be collected based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​determine the priority of the data. This allows the collection unit to determine the priority of data to be collected based on the user's emotions.

[0068] When collecting posted data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific tourist destination, the collection unit prioritizes collecting posted data related to the tourist destination. Furthermore, when the user is in a specific restaurant, the collection unit can prioritize collecting reviews related to the restaurant. Furthermore, when the user is staying in a specific area, the collection unit can prioritize collecting data on tourist spots and events related to the area. For example, the collection unit prioritizes collecting information on tourist spots and restaurants in the area where the user is currently staying. This allows the collection unit to prioritize collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI and have the AI ​​collect data. This allows the collection unit to prioritize collecting highly relevant data by taking into account the user's geographical location information.

[0069] When collecting posting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects travel photos and comments shared by the user on social media. The collection unit can also collect information on tourist spots and restaurants that the user follows on social media. Furthermore, the collection unit can collect information on travel groups and events that the user participates in on social media. For example, the collection unit collects travel photos and comments shared by the user on social media. This allows the collection unit to analyze the user's social media activities and collect related data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activities into AI and have the AI ​​collect data. This allows the collection unit to analyze the user's social media activities and collect related data.

[0070] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is excited, the analysis unit can provide an analysis result that emphasizes positive expressions. Furthermore, if the user is relaxed, the analysis unit can provide a detailed and calm analysis result. Furthermore, if the user is stressed, the analysis unit can provide a concise and to-the-point analysis result. For example, the analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. This allows the analysis unit to adjust the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the way the analysis is presented. This allows the analysis unit to adjust the way the analysis is presented based on the user's emotions.

[0071] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the posted data. For example, the analysis unit performs a detailed analysis on posted data with a high rating. The analysis unit can also perform a standard analysis on posted data with a medium rating. The analysis unit can also perform a brief analysis on posted data with a low rating. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the posted data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the posted data. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the posted data to AI and have the AI ​​adjust the level of detail of the analysis. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the posted data.

[0072] During analysis, the analysis unit can apply different analysis algorithms depending on the category of posted data. For example, the analysis unit can apply an analysis algorithm related to food quality and service to restaurant reviews. The analysis unit can also apply an analysis algorithm related to scenery and access to tourist attraction reviews. The analysis unit can also apply an analysis algorithm related to room cleanliness and facilities to accommodation reviews. For example, the analysis unit can apply an analysis algorithm related to food quality and service to restaurant reviews. This allows the analysis unit to apply different analysis algorithms depending on the category of posted data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of posted data into AI and have the AI ​​apply the analysis algorithm. This allows the analysis unit to apply different analysis algorithms depending on the category of posted data.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. This allows the analysis unit to adjust the length of the analysis based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the length of the analysis. This allows the analysis unit to adjust the length of the analysis based on the user's emotions.

[0074] During analysis, the analysis unit can determine the priority of analysis based on the time when the posted data was submitted. For example, the analysis unit prioritizes analysis of the most recently posted data. The analysis unit can also prioritize analysis of posted data related to a specific event or season. Furthermore, the analysis unit can prioritize analysis of data related to a specific period from the user's past posting history. For example, the analysis unit prioritizes analysis of the most recently posted data. This allows the analysis unit to determine the priority of analysis based on the time when the posted data was submitted. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the posted data was submitted to AI and have the AI ​​determine the priority of analysis. This allows the analysis unit to determine the priority of analysis based on the time when the posted data was submitted.

[0075] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the posted data. For example, the analysis unit prioritizes analyzing highly rated posted data. The analysis unit can also prioritize analyzing posted data related to the user's field of interest. Furthermore, the analysis unit can prioritize analyzing posted data related to a specific region or theme. For example, the analysis unit prioritizes analyzing highly rated posted data. This allows the analysis unit to adjust the order of analysis based on the relevance of the posted data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the posted data into AI and have the AI ​​adjust the order of analysis. This allows the analysis unit to adjust the order of analysis based on the relevance of the posted data.

[0076] The providing unit can estimate the user's emotions and adjust the way the travel plan is presented based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide a travel plan that emphasizes positive expressions. Furthermore, if the user is relaxed, the providing unit can provide a detailed and calm travel plan. Furthermore, if the user is stressed, the providing unit can provide a concise and to-the-point travel plan. For example, the providing unit can estimate the user's emotions and adjust the way the travel plan is presented based on the estimated emotions. This allows the providing unit to adjust the way the travel plan is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into an AI and have the AI ​​adjust the way the travel plan is presented. This allows the providing unit to adjust the way in which the travel plan is presented based on the user's feelings.

[0077] The providing unit can adjust the level of detail provided based on the importance of the travel plan when providing the travel plan. For example, the providing unit provides detailed information for a travel plan that includes highly rated tourist attractions and restaurants. The providing unit can also provide standard information for a travel plan that includes medium-rated tourist attractions and restaurants. The providing unit can also provide concise information for a travel plan that includes low-rated tourist attractions and restaurants. For example, the providing unit adjusts the level of detail provided based on the importance of the travel plan. This allows the providing unit to adjust the level of detail provided based on the importance of the travel plan. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the importance of the travel plan to AI and cause the AI ​​to adjust the level of detail provided. This allows the providing unit to adjust the level of detail provided based on the importance of the travel plan.

[0078] The providing unit can apply different providing algorithms depending on the category of the travel plan when providing the plan. For example, the providing unit can apply a providing algorithm that emphasizes restaurant ratings and menu information to a gourmet travel plan. The providing unit can also apply a providing algorithm that emphasizes tourist destination ratings and access information to a sightseeing travel plan. The providing unit can also apply a providing algorithm that emphasizes store ratings and product information to a shopping travel plan. For example, the providing unit can apply a providing algorithm that emphasizes restaurant ratings and menu information to a gourmet travel plan. This allows the providing unit to apply different providing algorithms depending on the category of the travel plan. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the category of the travel plan into AI and have the AI ​​apply the providing algorithm. This allows the providing unit to apply different providing algorithms depending on the category of the travel plan.

[0079] The providing unit can estimate the user's emotions and adjust the length of the travel plan to be provided based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide a short, concise travel plan. Furthermore, if the user is relaxed, the providing unit can provide a longer travel plan with detailed explanations. Furthermore, if the user is excited, the providing unit can provide a travel plan with visually stimulating effects. For example, the providing unit can estimate the user's emotions and adjust the length of the travel plan to be provided based on the estimated emotions. This allows the providing unit to adjust the length of the travel plan to be provided based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI. For example, the providing unit can input the user's emotion data into an AI and have the AI ​​adjust the length of the travel plan. This allows the providing unit to adjust the length of the travel plan to be provided based on the user's feelings.

[0080] The providing unit can determine the priority of provision based on the time of submission of the travel plan when it is provided. For example, the providing unit can prioritize providing the latest travel plan. The providing unit can also prioritize providing travel plans related to a specific event or season. Furthermore, the providing unit can prioritize providing travel plans related to a specific period based on the user's past travel history. For example, the providing unit can prioritize providing the latest travel plan. This allows the providing unit to determine the priority of provision based on the time of submission of the travel plan. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the time of submission of the travel plan into AI and have the AI ​​determine the priority of provision. This allows the providing unit to determine the priority of provision based on the time of submission of the travel plan.

[0081] The providing unit can adjust the order of providing travel plans based on the relevance of the travel plans when providing them. For example, the providing unit can prioritize providing travel plans that include highly rated tourist attractions and restaurants. The providing unit can also prioritize providing travel plans related to the user's area of ​​interest. Furthermore, the providing unit can prioritize providing travel plans related to a specific region or theme. For example, the providing unit can prioritize providing travel plans that include highly rated tourist attractions and restaurants. This allows the providing unit to adjust the order of providing the travel plans based on the relevance of the travel plans. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the relevance of the travel plans into AI and have the AI ​​adjust the order of providing the travel plans. This allows the providing unit to adjust the order of providing the travel plans based on the relevance of the travel plans.

[0082] The refund unit can estimate the user's emotion and adjust the refund method based on the estimated user's emotion. For example, when the user is excited, the refund unit can immediately refund the user, thereby increasing user satisfaction. Furthermore, when the user is relaxed, the refund unit can gradually refund the user, thereby maintaining long-term satisfaction. Furthermore, when the user is stressed, the refund unit can quickly refund the user, thereby reducing the user's burden. For example, the refund unit can estimate the user's emotion and adjust the refund method based on the estimated emotion. This allows the refund unit to adjust the refund method based on the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the refund unit may be performed using an AI, for example, or without an AI. For example, the refund unit can input the user's emotion data into an AI and have the AI ​​adjust the refund method. This allows the refund unit to adjust the refund method based on the user's emotion.

[0083] When providing a refund, the refund unit can analyze the user's past consumption behavior and select the optimal refund method. For example, the refund unit can provide a refund to accommodations or restaurants that the user has used in the past. The refund unit can also provide a refund to a specific category (e.g., gourmet food, sightseeing) based on the user's past consumption behavior. The refund unit can also analyze the user's past consumption behavior and select the most effective refund method. For example, the refund unit can provide a refund to accommodations or restaurants that the user has used in the past. This allows the refund unit to analyze the user's past consumption behavior and select the optimal refund method. Some or all of the above-described processing in the refund unit may be performed using, for example, AI, or may be performed without using AI. For example, the refund unit can input the user's past consumption behavior into AI and have the AI ​​select the optimal refund method. This allows the refund unit to analyze the user's past consumption behavior and select the optimal refund method.

[0084] The redemption unit can customize the redemption method based on the user's current living situation when redeeming the redemption. For example, if the user is traveling, the redemption unit can provide an immediate redemption to reduce travel expenses. Furthermore, if the user is at home, the redemption unit can provide a coupon that can be used for the user's next trip. Furthermore, the redemption unit can provide an appropriate redemption method depending on the user's current living situation (e.g., at work, on vacation). For example, if the user is traveling, the redemption unit can provide an immediate redemption to reduce travel expenses. This allows the redemption unit to customize the redemption method based on the user's current living situation. Some or all of the above-described processing in the redemption unit may be performed using, or without, AI. For example, the redemption unit can input the user's current living situation into AI and have the AI ​​customize the redemption method. This allows the redemption unit to customize the redemption method based on the user's current living situation.

[0085] The return unit can estimate the user's emotion and determine the priority of return based on the estimated user's emotion. For example, when the user is excited, the return unit can prioritize immediate return. Furthermore, when the user is relaxed, the return unit can prioritize gradual return. Furthermore, when the user is stressed, the return unit can prioritize quick return. For example, the return unit can estimate the user's emotion and determine the priority of return based on the estimated emotion. As a result, the return unit can determine the priority of return based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the return unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the return unit can input the user's emotion data into an AI and have the AI ​​determine the priority of return. As a result, the return unit can determine the priority of return based on the user's emotion.

[0086] The cashback unit can select the optimal cashback method by taking into account the user's geographical location information. For example, if the user is in a specific tourist destination, the cashback unit can provide a coupon that can be used at the tourist destination. Furthermore, if the user is in a specific restaurant, the cashback unit can provide a discount that can be used at the restaurant. Furthermore, if the user is staying in a specific region, the cashback unit can provide a benefit that can be used in the region. For example, if the user is in a specific tourist destination, the cashback unit can provide a coupon that can be used at the tourist destination. This allows the cashback unit to select the optimal cashback method by taking into account the user's geographical location information. Some or all of the above-described processing by the cashback unit may be performed using, or without, AI. For example, the cashback unit can input the user's geographical location information into AI and have the AI ​​select the optimal cashback method. This allows the cashback unit to select the optimal cashback method by taking into account the user's geographical location information.

[0087] When providing a refund, the refund unit can analyze the user's social media activity and suggest a method of redemption. For example, the refund unit can provide a refund based on travel information shared by the user on social media. The refund unit can also provide a refund for tourist attractions or restaurants that the user follows on social media. The refund unit can also provide a refund for travel groups or events that the user participates in on social media. For example, the refund unit can provide a refund based on travel information shared by the user on social media. This allows the refund unit to analyze the user's social media activity and suggest a method of redemption. Some or all of the above-described processing in the refund unit may be performed using, for example, AI, or may be performed without AI. For example, the refund unit can input the user's social media activity into AI and have the AI ​​suggest a method of redemption. This allows the refund unit to analyze the user's social media activity and suggest a method of redemption. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, provision unit, return unit, and collection unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the posted data. The provision unit is realized by the control unit 46A of the smart device 14 and provides a travel plan based on the analysis results. The return unit is realized by the specific processing unit 290 of the data processing device 12 and returns a return to the user. The collection unit is realized by the control unit 46A of the smart device 14 and collects the data posted by the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, provision unit, return unit, and collection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the posted data. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides a travel plan based on the analysis results. The return unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and returns a return to the user. The collection unit is realized, for example, by the control unit 46A of the smart glasses 214 and collects the data posted by the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, provision unit, return unit, and collection unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the posted data. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides a travel plan based on the analysis results. The return unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and returns a return to the user. The collection unit is realized, for example, by the control unit 46A of the headset type terminal 314 and collects the data posted by users. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, provision unit, refund unit, and collection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the posted data. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides a travel plan based on the analysis results. The refund unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and refunds returns to the user. The collection unit is realized, for example, by the control unit 46A of the robot 414 and collects the data posted by the user.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The analysis unit can analyze the user's travel history and suggest new travel plans based on past travel patterns. For example, the analysis unit analyzes data on tourist spots and restaurants visited by the user in the past and compares it with data from other users with similar travel patterns. The analysis unit can also suggest new spots with similar characteristics based on spots that the user has previously rated highly. Furthermore, the analysis unit can suggest travel plans related to specific seasons or events based on the user's past travel history. This allows the analysis unit to provide more personalized travel plans by utilizing the user's past travel history.

[0090] The collection unit can analyze the user's social media activity and collect data that is useful for proposing travel plans. For example, the collection unit collects travel photos and comments shared by the user on social media. The collection unit can also collect information on tourist spots and restaurants that the user follows. Furthermore, the collection unit can collect information on travel groups and events that the user participates in. This allows the collection unit to utilize the user's social media activity to collect more relevant data.

[0091] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is excited, the analysis results can be displayed using visually stimulating graphics. If the user is relaxed, the analysis results can be displayed using calm colors and detailed explanations. Furthermore, if the user is stressed, the analysis results can be displayed in a concise and to-the-point manner. This allows the analysis unit to provide the analysis results in the optimal display method according to the user's emotions.

[0092] The providing unit can propose an optimal travel plan in real time, taking into account the user's current geographical location information. For example, if the user is in a specific tourist destination, the providing unit can propose a travel plan that includes highly rated spots and restaurants around the tourist destination. Also, if the user is traveling, the providing unit can propose spots that the user can stop by on the way to the next destination. Furthermore, if the user is staying in a specific area, the providing unit can propose a travel plan that includes events and festivals being held in that area. In this way, the providing unit can utilize the user's current geographical location information to provide a more appropriate travel plan.

[0093] The refund unit can estimate the user's emotion and adjust the timing of the refund based on the estimated emotion. For example, if the user is excited, the refund can be performed immediately to increase the user's satisfaction. Alternatively, if the user is relaxed, the refund can be performed gradually to maintain long-term satisfaction. Furthermore, if the user is stressed, the refund can be performed quickly to reduce the user's burden. In this way, the refund unit can provide the optimal timing of the refund based on the user's emotion.

[0094] The collection unit can analyze the user's past travel history and select the optimal data collection method. For example, it can analyze the time periods in which the user frequently posted in the past and concentrate collection on those time periods. It can also analyze trends in the content posted by the user in the past and prioritize collection of related data. It can also collect data related to specific events or locations from the user's past travel history. This allows the collection unit to utilize the user's past travel history to collect data more efficiently.

[0095] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is excited, it can prioritize the analysis of posted data with positive ratings. Also, if the user is relaxed, it can prioritize the analysis of posted data including detailed reviews and photos. Furthermore, if the user is stressed, it can prioritize the analysis of concise reviews and short comments. This allows the analysis unit to determine the priority of analysis based on the user's emotions.

[0096] The providing unit can provide a travel plan customized according to the user's travel purpose and budget. For example, if the user is planning a family trip, the providing unit can suggest a travel plan that includes family-friendly tourist spots and restaurants. If the user is concerned about budget, the providing unit can suggest a travel plan that includes cost-effective spots and restaurants. Furthermore, if the user is interested in a specific theme (e.g., history, nature), the providing unit can suggest a travel plan that includes spots related to that theme. This allows the providing unit to provide the optimal travel plan according to the user's travel purpose and budget.

[0097] The refund unit can estimate the user's emotions and customize the refund method based on the estimated emotions. For example, if the user is excited, the refund can be performed immediately to increase the user's satisfaction. Alternatively, if the user is relaxed, the refund can be performed gradually to maintain long-term satisfaction. Furthermore, if the user is stressed, the refund can be performed quickly to reduce the user's burden. In this way, the refund unit can provide the optimal refund method based on the user's emotions.

[0098] The providing unit can estimate the user's emotions and adjust the way in which the travel plan is presented based on the estimated emotions. For example, if the user is excited, the providing unit can provide a travel plan that emphasizes positive expressions. If the user is relaxed, the providing unit can provide a detailed travel plan that uses calm expressions. Furthermore, if the user is stressed, the providing unit can provide a concise travel plan that gets straight to the point. This allows the providing unit to adjust the way in which the travel plan is presented based on the user's emotions.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The analysis unit analyzes the posted data. The analysis unit can analyze the posted content using text mining technology and the posted photos using image analysis technology. It also uses machine learning algorithms to classify the posted data and identify highly rated spots and restaurants. For example, it can extract tourist spots and restaurants that have been highly rated by multiple users. Step 2: The provider provides a travel plan based on the data analyzed by the analyzer. For a user planning to visit a specific area, the provider proposes a travel plan that includes highly rated tourist spots and restaurants in that area. The provider also provides a travel plan customized based on the user's interests. For example, the provider proposes an individually customized travel plan based on places the user has visited in the past and spots they have rated. Step 3: The return unit returns a return to the user based on the travel plan provided by the provider. The return unit returns 10% of the accommodation and meal costs through an electronic payment system. The return unit can also provide a return in the form of points or cash back. For example, a portion of the cost of the hotel where the user stayed or the restaurant where the user ate could be returned as points.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0129] 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.

[0130] 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.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The analysis unit analyzes the submitted data, A provisioning unit provides a travel plan based on the data analyzed by the aforementioned analysis unit, A reward unit that provides a return to the user based on the travel plan provided by the aforementioned provision unit, Equipped with A system characterized by:

2. It includes a data collection unit that collects submitted data.

2. The system of claim 1.

3. The analysis unit The content of the posts is categorized to identify highly-rated spots and restaurants.

2. The system of claim 1.

4. The providing unit For users planning to visit a specific region, we propose travel plans that include highly-rated tourist attractions and restaurants in that area.

2. The system of claim 1.

5. The reduction unit is A certain percentage of accommodation and meal expenses will be refunded through an electronic payment system.

2. The system of claim 1.

6. The collecting unit We estimate the user's sentiment and adjust the timing of collecting posting data based on the estimated user sentiment.

3. The system of claim 2.

7. The collecting unit Analyze the user's past posting history and select the appropriate data collection method.

3. The system of claim 2.

8. The collecting unit When collecting submitted data, filtering is performed based on the user's current travel status and areas of interest.

3. The system of claim 2.

9. The collecting unit It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions.

3. The system of claim 2.

Images